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  • Hanging of host network connections when starting KVM guest on bridge

    - by Chris Phillips
    Hi, I've a KVM system upon which I'm running a network bridge directly between all VM's and a bond0 (eth0, eth1) on the host OS. As such, all machines are presented on the same subnet, available outside of the box. The bond is doing mode 1 active / passive, with an arp_ip_target set to the default gateway, which has caused some issues in itself, but I can't see the bond configs mattering here myself. I'm seeing odd things most times when I stop and start a guest on the platform, in that on the host I lose network connectivity (icmp, ssh) for about 30 seconds. I don't lose connectivity on the other already running VM's though... they can always ping the default GW, but the host can't. I say "about 30 seconds" but from some tests it actually seems to be 28 seconds usually (or at least, I lose 28 pings...) and I'm wondering if this somehow relates to the bridge config. I'm not running STP on the bridge at all, and the forwarding delay is set to 1 second, path cost on the bond0 lowered to 10 and port priority of bond0 also lowered to 1. As such I don't think that the bridge should ever be able to think that bond0 is not connected just fine (as continued guest connectivity implies) yet the IP of the host, which is on the bridge device (... could that matter?? ) becomes unreachable. I'm fairly sure it's about the bridged networking, but at the same time as this happens when a VM is started there are clearly loads of other things also happening so maybe I'm way off the mark. Lack of connectivity: # ping 10.20.11.254 PING 10.20.11.254 (10.20.11.254) 56(84) bytes of data. 64 bytes from 10.20.11.254: icmp_seq=1 ttl=255 time=0.921 ms 64 bytes from 10.20.11.254: icmp_seq=2 ttl=255 time=0.541 ms type=1700 audit(1293462808.589:325): dev=vnet6 prom=256 old_prom=0 auid=42949672 95 ses=4294967295 type=1700 audit(1293462808.604:326): dev=vnet7 prom=256 old_prom=0 auid=42949672 95 ses=4294967295 type=1700 audit(1293462808.618:327): dev=vnet8 prom=256 old_prom=0 auid=42949672 95 ses=4294967295 kvm: 14116: cpu0 unimplemented perfctr wrmsr: 0x186 data 0x130079 kvm: 14116: cpu0 unimplemented perfctr wrmsr: 0xc1 data 0xffdd694a kvm: 14116: cpu0 unimplemented perfctr wrmsr: 0x186 data 0x530079 64 bytes from 10.20.11.254: icmp_seq=30 ttl=255 time=0.514 ms 64 bytes from 10.20.11.254: icmp_seq=31 ttl=255 time=0.551 ms 64 bytes from 10.20.11.254: icmp_seq=32 ttl=255 time=0.437 ms 64 bytes from 10.20.11.254: icmp_seq=33 ttl=255 time=0.392 ms brctl output of relevant bridge: # brctl showstp brdev brdev bridge id 8000.b2e1378d1396 designated root 8000.b2e1378d1396 root port 0 path cost 0 max age 19.99 bridge max age 19.99 hello time 1.99 bridge hello time 1.99 forward delay 0.99 bridge forward delay 0.99 ageing time 299.95 hello timer 0.50 tcn timer 0.00 topology change timer 0.00 gc timer 0.04 flags vnet5 (3) port id 8003 state forwarding designated root 8000.b2e1378d1396 path cost 100 designated bridge 8000.b2e1378d1396 message age timer 0.00 designated port 8003 forward delay timer 0.00 designated cost 0 hold timer 0.00 flags vnet0 (2) port id 8002 state forwarding designated root 8000.b2e1378d1396 path cost 100 designated bridge 8000.b2e1378d1396 message age timer 0.00 designated port 8002 forward delay timer 0.00 designated cost 0 hold timer 0.00 flags bond0 (1) port id 0001 state forwarding designated root 8000.b2e1378d1396 path cost 10 designated bridge 8000.b2e1378d1396 message age timer 0.00 designated port 0001 forward delay timer 0.00 designated cost 0 hold timer 0.00 flags I do see the new port listed as learning, but in line with the forward delay, only for 1 or 2 seconds when polling the brctl output on a loop. All pointers, tips or stabs in the dark appreciated.

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  • Parallel processing slower than sequential?

    - by zebediah49
    EDIT: For anyone who stumbles upon this in the future: Imagemagick uses a MP library. It's faster to use available cores if they're around, but if you have parallel jobs, it's unhelpful. Do one of the following: do your jobs serially (with Imagemagick in parallel mode) set MAGICK_THREAD_LIMIT=1 for your invocation of the imagemagick binary in question. By making Imagemagick use only one thread, it slows down by 20-30% in my test cases, but meant I could run one job per core without issues, for a significant net increase in performance. Original question: While converting some images using ImageMagick, I noticed a somewhat strange effect. Using xargs was significantly slower than a standard for loop. Since xargs limited to a single process should act like a for loop, I tested that, and found it to be about the same. Thus, we have this demonstration. Quad core (AMD Athalon X4, 2.6GHz) Working entirely on a tempfs (16g ram total; no swap) No other major loads Results: /media/ramdisk/img$ time for f in *.bmp; do echo $f ${f%bmp}png; done | xargs -n 2 -P 1 convert -auto-level real 0m3.784s user 0m2.240s sys 0m0.230s /media/ramdisk/img$ time for f in *.bmp; do echo $f ${f%bmp}png; done | xargs -n 2 -P 2 convert -auto-level real 0m9.097s user 0m28.020s sys 0m0.910s /media/ramdisk/img$ time for f in *.bmp; do echo $f ${f%bmp}png; done | xargs -n 2 -P 10 convert -auto-level real 0m9.844s user 0m33.200s sys 0m1.270s Can anyone think of a reason why running two instances of this program takes more than twice as long in real time, and more than ten times as long in processor time to complete the same task? After that initial hit, more processes do not seem to have as significant of an effect. I thought it might have to do with disk seeking, so I did that test entirely in ram. Could it have something to do with how Convert works, and having more than one copy at once means it cannot use processor cache as efficiently or something? EDIT: When done with 1000x 769KB files, performance is as expected. Interesting. /media/ramdisk/img$ time for f in *.bmp; do echo $f ${f%bmp}png; done | xargs -n 2 -P 1 convert -auto-level real 3m37.679s user 5m6.980s sys 0m6.340s /media/ramdisk/img$ time for f in *.bmp; do echo $f ${f%bmp}png; done | xargs -n 2 -P 1 convert -auto-level real 3m37.152s user 5m6.140s sys 0m6.530s /media/ramdisk/img$ time for f in *.bmp; do echo $f ${f%bmp}png; done | xargs -n 2 -P 2 convert -auto-level real 2m7.578s user 5m35.410s sys 0m6.050s /media/ramdisk/img$ time for f in *.bmp; do echo $f ${f%bmp}png; done | xargs -n 2 -P 4 convert -auto-level real 1m36.959s user 5m48.900s sys 0m6.350s /media/ramdisk/img$ time for f in *.bmp; do echo $f ${f%bmp}png; done | xargs -n 2 -P 10 convert -auto-level real 1m36.392s user 5m54.840s sys 0m5.650s

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  • Installation problem Ubuntu 12.04 Crashing hardware error

    - by user93640
    I am running on Ubuntu 8.04 for quite some time without many problems. About almost a year ago or so I have been trying to upgrade to 10.04 LTS, but without any success. Each time when trying to upgrade or even newly install the installation process crashed after about an hour or so (I forgot exactly how long). Now I wanted to try Ubuntu 12.04 (not even installing, but I only selected "Try Ubuntu without installing") and I got similar errors. I did not try to install it, because of earlier experience with 10.04 when after I also lost 8.04 and had to install from scratch again (after which it worked). I get the following screen (as I am not allowed to upload photos here the text): 26.767262] [Hardware Error]: CPU 0: Machine Check Exception: 0 Bank 5: b200001804000e0f 26.767279] [Hardware Error]: TSC 0 26.767287] [Hardware Error]: PROCESSOR 0:6f6 TIME 1349017924 SOCKET 0 APIC 0 microcode 44 26.767297] [Hardware Error]: Run the above through 'mcelog --ascii' 26.767307] [Hardware Error]: CPU 1: Machine Check Exception: 0 Bank 1: b200000000000175 26.767316] [Hardware Error]: TSC 0 26.767323] [Hardware Error]: PROCESSOR 0:6f6 TIME 1349017924 SOCKET 0 APIC 1 microcode 44 26.767331] [Hardware Error]: Run the above through 'mcelog --ascii' 26.767339] [Hardware Error]: CPU 1: Machine Check Exception: 0 Bank 5: b200003000000e0f 26.767348] [Hardware Error]: TSC 0 26.767354] [Hardware Error]: PROCESSOR 0:6f6 TIME 1349017924 SOCKET 0 APIC 1 microcode 44 26.767363] [Hardware Error]: Run the above through 'mcelog --ascii' 26.767371] [Hardware Error]: CPU 1: Machine Check Exception: 4 Bank 1: b200000000000175 26.767379] [Hardware Error]: TSC 1bf231e65f 26.767386] [Hardware Error]: PROCESSOR 0:6f6 TIME 1349017951 SOCKET 0 APIC 1 microcode 44 26.767395] [Hardware Error]: Run the above through 'mcelog --ascii' 26.767403] [Hardware Error]: CPU 1: Machine Check Exception: 4 Bank 5: b200003008000e0f 26.767413] [Hardware Error]: TSC 1bf231e65f 26.767421] [Hardware Error]: PROCESSOR 0:6f6 TIME 1349017951 SOCKET 0 APIC 1 microcode 44 26.767429] [Hardware Error]: Run the above through 'mcelog --ascii' 26.767437] [Hardware Error]: CPU 0: Machine Check Exception: 5 Bank 5: b200001806000e0f 26.767447] [Hardware Error]: RIP |INEXACT| 60:<00000000c1018b5c> {mwait_idle+0x7c/0x1d0} 26.767464] [Hardware Error]: TSC 1bf231e674 26.767471] [Hardware Error]: PROCESSOR 0:6f6 TIME 1349017951 SOCKET 0 APIC 0 microcode 44 26.767480] [Hardware Error]: Run the above through 'mcelog --ascii' 26.767487] [Hardware Error]: Machine check: Processor context corrupt 26.767495] Kernel panic - not syncing: Fatal Machine check 26.767505] Pid: 579, comm: debconf-communi Tainted: G M 3.2.0.29-generic-pae #46-Ubuntu 26.767515] Call Trace: 26.767525] [<c158f812>] ? printk+0x2d/0x2f 26.767534] [<c158f6e0>] panic+0x5c/0x161 26.767542] [<c10247ef>] mce_panic.part.14+0x13f/0x170 26.767551] [<c1024872>] mce_panic+0x52/0x90 26.767558] [<c1024a18>] mce_reign+0x168/0x170 26.767565] [<c1024bb5>] mce_end+0x105/0x110 26.767572] [<c10252db>] do_machine_check+0x32b/0x4f0 26.767581] [<c1024fb0>] ? mce_log+0x120/0x120 26.767590] [<c15a5e47>] error_code+0x67/0x6c 26.767602] panic occurred, switching back to text console 26.768498] Rebooting in 30 seconds.. For information, I have also tried earlier Arch Linux. I can install it, but when I try to install a window manager (LXDE) again I got similar errors. Fedora also crashes when installing and also Mandriva did not work for me. Therefore I think something deep in the machine might be wrong. But as stated above I can (clean) install 8.04 and also 9.10 can be installed without problems. Also updates for 8.04 can be installed. My machine is dual boot with XP next to it on a different partition. My HW: Memory : 2.0 GiB; Processor 0: Intel(R) Core(TM)2 CPU 6320 @ 1.86GHz; Processor 1: Intel(R) Core(TM)2 CPU 6320 @ 1.86GHz; How can I install Ubuntu 12.04? Last option would be to completely format my machine and install everything from scratch, but even I am not sure if that would solve it in the end. Can anybody help me out?

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  • 2012 Oracle Fusion Innovation Awards - Part 2

    - by Michelle Kimihira
    Author: Moazzam Chaudry Continuing from Friday's blog on 2012 Oracle Fusion Innovation Awards, this blog (Part 2) will provide more details around the customers. It was a tremendous honor to be in single room of winners. We only wish we could have had more time to share stories from all the winners.  We received great insight from all the innovative solutions that our customers deploy and would like to share them broadly, so that others can benefit from best practices. There was a customer panel session joined by Ingersoll Rand, Nike and Motability and here is what was discussed: Barry Bonar, Enterprise Architect from Ingersoll Rand shared details around their solution, comprised of Oracle Exalogic, Oracle WebLogic Server and Oracle SOA Suite. This combined solutoin enabled their business transformation to increase decision-making, speed and efficiency, resulting in 40% reduced IT spend, 41X Faster response time and huge cost savings. Ashok Balakrishnan, Architect from Nike shared how they leveraged Oracle Coherence to analyze their digital "footprint" of activities. This helps them compete, collaborate and compare athletic data over time. Lastly, Ashley Doodly, Head of IT from Motability shared details around their solution compromised of Oracle SOA Suite, Service Bus, ADF, Coherence, BO and E-Business Suite. This solution helped Motability achieve 100% ROI within the first few months, performance in seconds vs. 10's of minutes and tremendous improvement in throughput that increased up to 50%.  This year's winners by category are: Oracle Exalogic Customer Results using Fusion Middleware Netshoes ATG on Exalogic: 6X Reduced H/W foot print, 6.2X increased throughput and 3 weeks time to market Claro Part of America Movil, running mission critical Java Application on Exalogic with 35X Faster Java response time, 5X Throughput Underwriters Laboratories Exalogic as an Apps Consolidation platform to power tremendous growth Ingersoll Rand EBS on Exalogic: Up to 40% Reduction in overall IT budget, 3x reduced foot print Oracle Cloud Application Foundation Customer Results using Fusion Middleware  Mazda Motor Corporation Tuxedo ART Batch runtime environment to migrate their batch apps on new open environment and reduce main frame cost. HOTELBEDS Technology Open Source to WebLogic transformation Globalia Corporation Introduced Oracle Coherence to fully reengineer DTH system and provide multiple business and technical benefits Nike Nike+, digital sports platform, has 8M users and is expecting an 5X increase in users, many of who will carry multiple devices that frequently sync data with the Digital Sport platform Comcast Corporation The solution is expected to increase availability, continuity, performance, and simplify and make the code at the application layer more flexible. Oracle SOA and Oracle BPM Customer Results using Fusion Middleware NTT Docomo Network traffic solution based on Oracle event processing and coherence - massive in scale: 12M users (50M in future) - 800,000 events/sec. Schneider National, Inc. SOA/B2B/ADF/Data Integration to orchestrate key order processes across Siebel, OTM & EBS.  Platform runs 60M trans/day and  50 million composite SOA instances per day across 10G and 11G Amadeus Oracle BPM solution: Business Rules and processes vary across local (80), regional (~10) and corporate approval process. Up to 10 levels of approval. Plans to deploy across 20+ markets Navitar SOA solution integrates a fully non-Oracle legacy application/ERP environment using Oracle’s SOA Suite and Oracle AIA Foundation Pack. Motability Uses SOA Suite to synchronize data across the systems and to manage the vehicle remarketing process Oracle WebCenter Customer Results using Fusion Middleware  News Limited Single platform running websites for 50% of Australia's newspapers University of Louisville “Facebook for Medicine”: Oracle Webcenter platform and Oracle BIEE to analyze patient test data and uncover potential health issues. Expecting annualized ROI of 277% China Mobile Jiangsu Company portal (25k users) to drive collaboration & productivity Life Technologies Portal for remotely monitoring & repairing biotech instruments LA Dept. of Water & Power Oracle WebCenter Portal to power ladwp.com on desktop and mobile for 1.6million users Oracle Identity Management Customer Results using Fusion Middleware Education Testing Service Identity Management platform for provisioning & SSO of 6 million GRE, GMAT, TOEFL customers Avea Oracle Identity Manager allowing call center personnel to quickly change Identity Profile to handle varying call loads based on a user self service interface. Decreased Admin Cost by 30% Oracle Data Integration Customer Results using Fusion Middleware Raymond James Near real-time integration for improved systems (throughput & performance) and enhanced operational flexibility in a 24 X 7 environment Wm Morrison Supermarkets Electronic Point of Sale integration handling over 80 million transactions a day in near real time (15 min intervals) Oracle Application Development Framework and Oracle Fusion Development Customer Results using Fusion Middleware Qualcomm Incorporated Solution providing  immediate business value enabling a self-service model necessary for growing the new customer base, an increase in customer satisfaction, reduced “time-to-deliver” Micros Systems, Inc. ADF, SOA Suite, WebCenter  enables services that include managing distribution of hotel rooms availability and rates to channels such as Hotel Web-site, Expedia, etc. Marfin Egnatia Bank A new web 2.0 UI provides a much richer experience through the ADF solution with the end result being one of boosting end-user productivity    Business Analytics (Oracle BI, Oracle EPM, Oracle Exalytics) Customer Results using Fusion Middleware INC Research Self-service customer portal delivering 5–10% of the overall revenue - expected to grow fast with the BI solution Experian Reduction in Time to Complete the Financial Close Process Hologic Inc Solution, saving months of decision-making uncertainty! We look forward to seeing many more innovative nominations. The nominatation process for 2013 begins in April 2013.    Additional Information: Blog: Oracle WebCenter Award Winners Blog: Oracle Identity Management Winners Blog: Oracle Exalogic Winners Blog: SOA, BPM and Data Integration will be will feature award winners in its respective areas this week Subscribe to our regular Fusion Middleware Newsletter Follow us on Twitter and Facebook

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  • How John Got 15x Improvement Without Really Trying

    - by rchrd
    The following article was published on a Sun Microsystems website a number of years ago by John Feo. It is still useful and worth preserving. So I'm republishing it here.  How I Got 15x Improvement Without Really Trying John Feo, Sun Microsystems Taking ten "personal" program codes used in scientific and engineering research, the author was able to get from 2 to 15 times performance improvement easily by applying some simple general optimization techniques. Introduction Scientific research based on computer simulation depends on the simulation for advancement. The research can advance only as fast as the computational codes can execute. The codes' efficiency determines both the rate and quality of results. In the same amount of time, a faster program can generate more results and can carry out a more detailed simulation of physical phenomena than a slower program. Highly optimized programs help science advance quickly and insure that monies supporting scientific research are used as effectively as possible. Scientific computer codes divide into three broad categories: ISV, community, and personal. ISV codes are large, mature production codes developed and sold commercially. The codes improve slowly over time both in methods and capabilities, and they are well tuned for most vendor platforms. Since the codes are mature and complex, there are few opportunities to improve their performance solely through code optimization. Improvements of 10% to 15% are typical. Examples of ISV codes are DYNA3D, Gaussian, and Nastran. Community codes are non-commercial production codes used by a particular research field. Generally, they are developed and distributed by a single academic or research institution with assistance from the community. Most users just run the codes, but some develop new methods and extensions that feed back into the general release. The codes are available on most vendor platforms. Since these codes are younger than ISV codes, there are more opportunities to optimize the source code. Improvements of 50% are not unusual. Examples of community codes are AMBER, CHARM, BLAST, and FASTA. Personal codes are those written by single users or small research groups for their own use. These codes are not distributed, but may be passed from professor-to-student or student-to-student over several years. They form the primordial ocean of applications from which community and ISV codes emerge. Government research grants pay for the development of most personal codes. This paper reports on the nature and performance of this class of codes. Over the last year, I have looked at over two dozen personal codes from more than a dozen research institutions. The codes cover a variety of scientific fields, including astronomy, atmospheric sciences, bioinformatics, biology, chemistry, geology, and physics. The sources range from a few hundred lines to more than ten thousand lines, and are written in Fortran, Fortran 90, C, and C++. For the most part, the codes are modular, documented, and written in a clear, straightforward manner. They do not use complex language features, advanced data structures, programming tricks, or libraries. I had little trouble understanding what the codes did or how data structures were used. Most came with a makefile. Surprisingly, only one of the applications is parallel. All developers have access to parallel machines, so availability is not an issue. Several tried to parallelize their applications, but stopped after encountering difficulties. Lack of education and a perception that parallelism is difficult prevented most from trying. I parallelized several of the codes using OpenMP, and did not judge any of the codes as difficult to parallelize. Even more surprising than the lack of parallelism is the inefficiency of the codes. I was able to get large improvements in performance in a matter of a few days applying simple optimization techniques. Table 1 lists ten representative codes [names and affiliation are omitted to preserve anonymity]. Improvements on one processor range from 2x to 15.5x with a simple average of 4.75x. I did not use sophisticated performance tools or drill deep into the program's execution character as one would do when tuning ISV or community codes. Using only a profiler and source line timers, I identified inefficient sections of code and improved their performance by inspection. The changes were at a high level. I am sure there is another factor of 2 or 3 in each code, and more if the codes are parallelized. The study’s results show that personal scientific codes are running many times slower than they should and that the problem is pervasive. Computational scientists are not sloppy programmers; however, few are trained in the art of computer programming or code optimization. I found that most have a working knowledge of some programming language and standard software engineering practices; but they do not know, or think about, how to make their programs run faster. They simply do not know the standard techniques used to make codes run faster. In fact, they do not even perceive that such techniques exist. The case studies described in this paper show that applying simple, well known techniques can significantly increase the performance of personal codes. It is important that the scientific community and the Government agencies that support scientific research find ways to better educate academic scientific programmers. The inefficiency of their codes is so bad that it is retarding both the quality and progress of scientific research. # cacheperformance redundantoperations loopstructures performanceimprovement 1 x x 15.5 2 x 2.8 3 x x 2.5 4 x 2.1 5 x x 2.0 6 x 5.0 7 x 5.8 8 x 6.3 9 2.2 10 x x 3.3 Table 1 — Area of improvement and performance gains of 10 codes The remainder of the paper is organized as follows: sections 2, 3, and 4 discuss the three most common sources of inefficiencies in the codes studied. These are cache performance, redundant operations, and loop structures. Each section includes several examples. The last section summaries the work and suggests a possible solution to the issues raised. Optimizing cache performance Commodity microprocessor systems use caches to increase memory bandwidth and reduce memory latencies. Typical latencies from processor to L1, L2, local, and remote memory are 3, 10, 50, and 200 cycles, respectively. Moreover, bandwidth falls off dramatically as memory distances increase. Programs that do not use cache effectively run many times slower than programs that do. When optimizing for cache, the biggest performance gains are achieved by accessing data in cache order and reusing data to amortize the overhead of cache misses. Secondary considerations are prefetching, associativity, and replacement; however, the understanding and analysis required to optimize for the latter are probably beyond the capabilities of the non-expert. Much can be gained simply by accessing data in the correct order and maximizing data reuse. 6 out of the 10 codes studied here benefited from such high level optimizations. Array Accesses The most important cache optimization is the most basic: accessing Fortran array elements in column order and C array elements in row order. Four of the ten codes—1, 2, 4, and 10—got it wrong. Compilers will restructure nested loops to optimize cache performance, but may not do so if the loop structure is too complex, or the loop body includes conditionals, complex addressing, or function calls. In code 1, the compiler failed to invert a key loop because of complex addressing do I = 0, 1010, delta_x IM = I - delta_x IP = I + delta_x do J = 5, 995, delta_x JM = J - delta_x JP = J + delta_x T1 = CA1(IP, J) + CA1(I, JP) T2 = CA1(IM, J) + CA1(I, JM) S1 = T1 + T2 - 4 * CA1(I, J) CA(I, J) = CA1(I, J) + D * S1 end do end do In code 2, the culprit is conditionals do I = 1, N do J = 1, N If (IFLAG(I,J) .EQ. 0) then T1 = Value(I, J-1) T2 = Value(I-1, J) T3 = Value(I, J) T4 = Value(I+1, J) T5 = Value(I, J+1) Value(I,J) = 0.25 * (T1 + T2 + T5 + T4) Delta = ABS(T3 - Value(I,J)) If (Delta .GT. MaxDelta) MaxDelta = Delta endif enddo enddo I fixed both programs by inverting the loops by hand. Code 10 has three-dimensional arrays and triply nested loops. The structure of the most computationally intensive loops is too complex to invert automatically or by hand. The only practical solution is to transpose the arrays so that the dimension accessed by the innermost loop is in cache order. The arrays can be transposed at construction or prior to entering a computationally intensive section of code. The former requires all array references to be modified, while the latter is cost effective only if the cost of the transpose is amortized over many accesses. I used the second approach to optimize code 10. Code 5 has four-dimensional arrays and loops are nested four deep. For all of the reasons cited above the compiler is not able to restructure three key loops. Assume C arrays and let the four dimensions of the arrays be i, j, k, and l. In the original code, the index structure of the three loops is L1: for i L2: for i L3: for i for l for l for j for k for j for k for j for k for l So only L3 accesses array elements in cache order. L1 is a very complex loop—much too complex to invert. I brought the loop into cache alignment by transposing the second and fourth dimensions of the arrays. Since the code uses a macro to compute all array indexes, I effected the transpose at construction and changed the macro appropriately. The dimensions of the new arrays are now: i, l, k, and j. L3 is a simple loop and easily inverted. L2 has a loop-carried scalar dependence in k. By promoting the scalar name that carries the dependence to an array, I was able to invert the third and fourth subloops aligning the loop with cache. Code 5 is by far the most difficult of the four codes to optimize for array accesses; but the knowledge required to fix the problems is no more than that required for the other codes. I would judge this code at the limits of, but not beyond, the capabilities of appropriately trained computational scientists. Array Strides When a cache miss occurs, a line (64 bytes) rather than just one word is loaded into the cache. If data is accessed stride 1, than the cost of the miss is amortized over 8 words. Any stride other than one reduces the cost savings. Two of the ten codes studied suffered from non-unit strides. The codes represent two important classes of "strided" codes. Code 1 employs a multi-grid algorithm to reduce time to convergence. The grids are every tenth, fifth, second, and unit element. Since time to convergence is inversely proportional to the distance between elements, coarse grids converge quickly providing good starting values for finer grids. The better starting values further reduce the time to convergence. The downside is that grids of every nth element, n > 1, introduce non-unit strides into the computation. In the original code, much of the savings of the multi-grid algorithm were lost due to this problem. I eliminated the problem by compressing (copying) coarse grids into continuous memory, and rewriting the computation as a function of the compressed grid. On convergence, I copied the final values of the compressed grid back to the original grid. The savings gained from unit stride access of the compressed grid more than paid for the cost of copying. Using compressed grids, the loop from code 1 included in the previous section becomes do j = 1, GZ do i = 1, GZ T1 = CA(i+0, j-1) + CA(i-1, j+0) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) S1 = T1 + T4 - 4 * CA1(i+0, j+0) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 enddo enddo where CA and CA1 are compressed arrays of size GZ. Code 7 traverses a list of objects selecting objects for later processing. The labels of the selected objects are stored in an array. The selection step has unit stride, but the processing steps have irregular stride. A fix is to save the parameters of the selected objects in temporary arrays as they are selected, and pass the temporary arrays to the processing functions. The fix is practical if the same parameters are used in selection as in processing, or if processing comprises a series of distinct steps which use overlapping subsets of the parameters. Both conditions are true for code 7, so I achieved significant improvement by copying parameters to temporary arrays during selection. Data reuse In the previous sections, we optimized for spatial locality. It is also important to optimize for temporal locality. Once read, a datum should be used as much as possible before it is forced from cache. Loop fusion and loop unrolling are two techniques that increase temporal locality. Unfortunately, both techniques increase register pressure—as loop bodies become larger, the number of registers required to hold temporary values grows. Once register spilling occurs, any gains evaporate quickly. For multiprocessors with small register sets or small caches, the sweet spot can be very small. In the ten codes presented here, I found no opportunities for loop fusion and only two opportunities for loop unrolling (codes 1 and 3). In code 1, unrolling the outer and inner loop one iteration increases the number of result values computed by the loop body from 1 to 4, do J = 1, GZ-2, 2 do I = 1, GZ-2, 2 T1 = CA1(i+0, j-1) + CA1(i-1, j+0) T2 = CA1(i+1, j-1) + CA1(i+0, j+0) T3 = CA1(i+0, j+0) + CA1(i-1, j+1) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) T5 = CA1(i+2, j+0) + CA1(i+1, j+1) T6 = CA1(i+1, j+1) + CA1(i+0, j+2) T7 = CA1(i+2, j+1) + CA1(i+1, j+2) S1 = T1 + T4 - 4 * CA1(i+0, j+0) S2 = T2 + T5 - 4 * CA1(i+1, j+0) S3 = T3 + T6 - 4 * CA1(i+0, j+1) S4 = T4 + T7 - 4 * CA1(i+1, j+1) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 CA(i+1, j+0) = CA1(i+1, j+0) + DD * S2 CA(i+0, j+1) = CA1(i+0, j+1) + DD * S3 CA(i+1, j+1) = CA1(i+1, j+1) + DD * S4 enddo enddo The loop body executes 12 reads, whereas as the rolled loop shown in the previous section executes 20 reads to compute the same four values. In code 3, two loops are unrolled 8 times and one loop is unrolled 4 times. Here is the before for (k = 0; k < NK[u]; k++) { sum = 0.0; for (y = 0; y < NY; y++) { sum += W[y][u][k] * delta[y]; } backprop[i++]=sum; } and after code for (k = 0; k < KK - 8; k+=8) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (y = 0; y < NY; y++) { sum0 += W[y][0][k+0] * delta[y]; sum1 += W[y][0][k+1] * delta[y]; sum2 += W[y][0][k+2] * delta[y]; sum3 += W[y][0][k+3] * delta[y]; sum4 += W[y][0][k+4] * delta[y]; sum5 += W[y][0][k+5] * delta[y]; sum6 += W[y][0][k+6] * delta[y]; sum7 += W[y][0][k+7] * delta[y]; } backprop[k+0] = sum0; backprop[k+1] = sum1; backprop[k+2] = sum2; backprop[k+3] = sum3; backprop[k+4] = sum4; backprop[k+5] = sum5; backprop[k+6] = sum6; backprop[k+7] = sum7; } for one of the loops unrolled 8 times. Optimizing for temporal locality is the most difficult optimization considered in this paper. The concepts are not difficult, but the sweet spot is small. Identifying where the program can benefit from loop unrolling or loop fusion is not trivial. Moreover, it takes some effort to get it right. Still, educating scientific programmers about temporal locality and teaching them how to optimize for it will pay dividends. Reducing instruction count Execution time is a function of instruction count. Reduce the count and you usually reduce the time. The best solution is to use a more efficient algorithm; that is, an algorithm whose order of complexity is smaller, that converges quicker, or is more accurate. Optimizing source code without changing the algorithm yields smaller, but still significant, gains. This paper considers only the latter because the intent is to study how much better codes can run if written by programmers schooled in basic code optimization techniques. The ten codes studied benefited from three types of "instruction reducing" optimizations. The two most prevalent were hoisting invariant memory and data operations out of inner loops. The third was eliminating unnecessary data copying. The nature of these inefficiencies is language dependent. Memory operations The semantics of C make it difficult for the compiler to determine all the invariant memory operations in a loop. The problem is particularly acute for loops in functions since the compiler may not know the values of the function's parameters at every call site when compiling the function. Most compilers support pragmas to help resolve ambiguities; however, these pragmas are not comprehensive and there is no standard syntax. To guarantee that invariant memory operations are not executed repetitively, the user has little choice but to hoist the operations by hand. The problem is not as severe in Fortran programs because in the absence of equivalence statements, it is a violation of the language's semantics for two names to share memory. Codes 3 and 5 are C programs. In both cases, the compiler did not hoist all invariant memory operations from inner loops. Consider the following loop from code 3 for (y = 0; y < NY; y++) { i = 0; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += delta[y] * I1[i++]; } } } Since dW[y][u] can point to the same memory space as delta for one or more values of y and u, assignment to dW[y][u][k] may change the value of delta[y]. In reality, dW and delta do not overlap in memory, so I rewrote the loop as for (y = 0; y < NY; y++) { i = 0; Dy = delta[y]; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += Dy * I1[i++]; } } } Failure to hoist invariant memory operations may be due to complex address calculations. If the compiler can not determine that the address calculation is invariant, then it can hoist neither the calculation nor the associated memory operations. As noted above, code 5 uses a macro to address four-dimensional arrays #define MAT4D(a,q,i,j,k) (double *)((a)->data + (q)*(a)->strides[0] + (i)*(a)->strides[3] + (j)*(a)->strides[2] + (k)*(a)->strides[1]) The macro is too complex for the compiler to understand and so, it does not identify any subexpressions as loop invariant. The simplest way to eliminate the address calculation from the innermost loop (over i) is to define a0 = MAT4D(a,q,0,j,k) before the loop and then replace all instances of *MAT4D(a,q,i,j,k) in the loop with a0[i] A similar problem appears in code 6, a Fortran program. The key loop in this program is do n1 = 1, nh nx1 = (n1 - 1) / nz + 1 nz1 = n1 - nz * (nx1 - 1) do n2 = 1, nh nx2 = (n2 - 1) / nz + 1 nz2 = n2 - nz * (nx2 - 1) ndx = nx2 - nx1 ndy = nz2 - nz1 gxx = grn(1,ndx,ndy) gyy = grn(2,ndx,ndy) gxy = grn(3,ndx,ndy) balance(n1,1) = balance(n1,1) + (force(n2,1) * gxx + force(n2,2) * gxy) * h1 balance(n1,2) = balance(n1,2) + (force(n2,1) * gxy + force(n2,2) * gyy)*h1 end do end do The programmer has written this loop well—there are no loop invariant operations with respect to n1 and n2. However, the loop resides within an iterative loop over time and the index calculations are independent with respect to time. Trading space for time, I precomputed the index values prior to the entering the time loop and stored the values in two arrays. I then replaced the index calculations with reads of the arrays. Data operations Ways to reduce data operations can appear in many forms. Implementing a more efficient algorithm produces the biggest gains. The closest I came to an algorithm change was in code 4. This code computes the inner product of K-vectors A(i) and B(j), 0 = i < N, 0 = j < M, for most values of i and j. Since the program computes most of the NM possible inner products, it is more efficient to compute all the inner products in one triply-nested loop rather than one at a time when needed. The savings accrue from reading A(i) once for all B(j) vectors and from loop unrolling. for (i = 0; i < N; i+=8) { for (j = 0; j < M; j++) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (k = 0; k < K; k++) { sum0 += A[i+0][k] * B[j][k]; sum1 += A[i+1][k] * B[j][k]; sum2 += A[i+2][k] * B[j][k]; sum3 += A[i+3][k] * B[j][k]; sum4 += A[i+4][k] * B[j][k]; sum5 += A[i+5][k] * B[j][k]; sum6 += A[i+6][k] * B[j][k]; sum7 += A[i+7][k] * B[j][k]; } C[i+0][j] = sum0; C[i+1][j] = sum1; C[i+2][j] = sum2; C[i+3][j] = sum3; C[i+4][j] = sum4; C[i+5][j] = sum5; C[i+6][j] = sum6; C[i+7][j] = sum7; }} This change requires knowledge of a typical run; i.e., that most inner products are computed. The reasons for the change, however, derive from basic optimization concepts. It is the type of change easily made at development time by a knowledgeable programmer. In code 5, we have the data version of the index optimization in code 6. Here a very expensive computation is a function of the loop indices and so cannot be hoisted out of the loop; however, the computation is invariant with respect to an outer iterative loop over time. We can compute its value for each iteration of the computation loop prior to entering the time loop and save the values in an array. The increase in memory required to store the values is small in comparison to the large savings in time. The main loop in Code 8 is doubly nested. The inner loop includes a series of guarded computations; some are a function of the inner loop index but not the outer loop index while others are a function of the outer loop index but not the inner loop index for (j = 0; j < N; j++) { for (i = 0; i < M; i++) { r = i * hrmax; R = A[j]; temp = (PRM[3] == 0.0) ? 1.0 : pow(r, PRM[3]); high = temp * kcoeff * B[j] * PRM[2] * PRM[4]; low = high * PRM[6] * PRM[6] / (1.0 + pow(PRM[4] * PRM[6], 2.0)); kap = (R > PRM[6]) ? high * R * R / (1.0 + pow(PRM[4]*r, 2.0) : low * pow(R/PRM[6], PRM[5]); < rest of loop omitted > }} Note that the value of temp is invariant to j. Thus, we can hoist the computation for temp out of the loop and save its values in an array. for (i = 0; i < M; i++) { r = i * hrmax; TEMP[i] = pow(r, PRM[3]); } [N.B. – the case for PRM[3] = 0 is omitted and will be reintroduced later.] We now hoist out of the inner loop the computations invariant to i. Since the conditional guarding the value of kap is invariant to i, it behooves us to hoist the computation out of the inner loop, thereby executing the guard once rather than M times. The final version of the code is for (j = 0; j < N; j++) { R = rig[j] / 1000.; tmp1 = kcoeff * par[2] * beta[j] * par[4]; tmp2 = 1.0 + (par[4] * par[4] * par[6] * par[6]); tmp3 = 1.0 + (par[4] * par[4] * R * R); tmp4 = par[6] * par[6] / tmp2; tmp5 = R * R / tmp3; tmp6 = pow(R / par[6], par[5]); if ((par[3] == 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp5; } else if ((par[3] == 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp4 * tmp6; } else if ((par[3] != 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp5; } else if ((par[3] != 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp4 * tmp6; } for (i = 0; i < M; i++) { kap = KAP[i]; r = i * hrmax; < rest of loop omitted > } } Maybe not the prettiest piece of code, but certainly much more efficient than the original loop, Copy operations Several programs unnecessarily copy data from one data structure to another. This problem occurs in both Fortran and C programs, although it manifests itself differently in the two languages. Code 1 declares two arrays—one for old values and one for new values. At the end of each iteration, the array of new values is copied to the array of old values to reset the data structures for the next iteration. This problem occurs in Fortran programs not included in this study and in both Fortran 77 and Fortran 90 code. Introducing pointers to the arrays and swapping pointer values is an obvious way to eliminate the copying; but pointers is not a feature that many Fortran programmers know well or are comfortable using. An easy solution not involving pointers is to extend the dimension of the value array by 1 and use the last dimension to differentiate between arrays at different times. For example, if the data space is N x N, declare the array (N, N, 2). Then store the problem’s initial values in (_, _, 2) and define the scalar names new = 2 and old = 1. At the start of each iteration, swap old and new to reset the arrays. The old–new copy problem did not appear in any C program. In programs that had new and old values, the code swapped pointers to reset data structures. Where unnecessary coping did occur is in structure assignment and parameter passing. Structures in C are handled much like scalars. Assignment causes the data space of the right-hand name to be copied to the data space of the left-hand name. Similarly, when a structure is passed to a function, the data space of the actual parameter is copied to the data space of the formal parameter. If the structure is large and the assignment or function call is in an inner loop, then copying costs can grow quite large. While none of the ten programs considered here manifested this problem, it did occur in programs not included in the study. A simple fix is always to refer to structures via pointers. Optimizing loop structures Since scientific programs spend almost all their time in loops, efficient loops are the key to good performance. Conditionals, function calls, little instruction level parallelism, and large numbers of temporary values make it difficult for the compiler to generate tightly packed, highly efficient code. Conditionals and function calls introduce jumps that disrupt code flow. Users should eliminate or isolate conditionls to their own loops as much as possible. Often logical expressions can be substituted for if-then-else statements. For example, code 2 includes the following snippet MaxDelta = 0.0 do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) if (Delta > MaxDelta) MaxDelta = Delta enddo enddo if (MaxDelta .gt. 0.001) goto 200 Since the only use of MaxDelta is to control the jump to 200 and all that matters is whether or not it is greater than 0.001, I made MaxDelta a boolean and rewrote the snippet as MaxDelta = .false. do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) MaxDelta = MaxDelta .or. (Delta .gt. 0.001) enddo enddo if (MaxDelta) goto 200 thereby, eliminating the conditional expression from the inner loop. A microprocessor can execute many instructions per instruction cycle. Typically, it can execute one or more memory, floating point, integer, and jump operations. To be executed simultaneously, the operations must be independent. Thick loops tend to have more instruction level parallelism than thin loops. Moreover, they reduce memory traffice by maximizing data reuse. Loop unrolling and loop fusion are two techniques to increase the size of loop bodies. Several of the codes studied benefitted from loop unrolling, but none benefitted from loop fusion. This observation is not too surpising since it is the general tendency of programmers to write thick loops. As loops become thicker, the number of temporary values grows, increasing register pressure. If registers spill, then memory traffic increases and code flow is disrupted. A thick loop with many temporary values may execute slower than an equivalent series of thin loops. The biggest gain will be achieved if the thick loop can be split into a series of independent loops eliminating the need to write and read temporary arrays. I found such an occasion in code 10 where I split the loop do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do into two disjoint loops do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) end do end do do i = 1, n do j = 1, m C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do Conclusions Over the course of the last year, I have had the opportunity to work with over two dozen academic scientific programmers at leading research universities. Their research interests span a broad range of scientific fields. Except for two programs that relied almost exclusively on library routines (matrix multiply and fast Fourier transform), I was able to improve significantly the single processor performance of all codes. Improvements range from 2x to 15.5x with a simple average of 4.75x. Changes to the source code were at a very high level. I did not use sophisticated techniques or programming tools to discover inefficiencies or effect the changes. Only one code was parallel despite the availability of parallel systems to all developers. Clearly, we have a problem—personal scientific research codes are highly inefficient and not running parallel. The developers are unaware of simple optimization techniques to make programs run faster. They lack education in the art of code optimization and parallel programming. I do not believe we can fix the problem by publishing additional books or training manuals. To date, the developers in questions have not studied the books or manual available, and are unlikely to do so in the future. Short courses are a possible solution, but I believe they are too concentrated to be much use. The general concepts can be taught in a three or four day course, but that is not enough time for students to practice what they learn and acquire the experience to apply and extend the concepts to their codes. Practice is the key to becoming proficient at optimization. I recommend that graduate students be required to take a semester length course in optimization and parallel programming. We would never give someone access to state-of-the-art scientific equipment costing hundreds of thousands of dollars without first requiring them to demonstrate that they know how to use the equipment. Yet the criterion for time on state-of-the-art supercomputers is at most an interesting project. Requestors are never asked to demonstrate that they know how to use the system, or can use the system effectively. A semester course would teach them the required skills. Government agencies that fund academic scientific research pay for most of the computer systems supporting scientific research as well as the development of most personal scientific codes. These agencies should require graduate schools to offer a course in optimization and parallel programming as a requirement for funding. About the Author John Feo received his Ph.D. in Computer Science from The University of Texas at Austin in 1986. After graduate school, Dr. Feo worked at Lawrence Livermore National Laboratory where he was the Group Leader of the Computer Research Group and principal investigator of the Sisal Language Project. In 1997, Dr. Feo joined Tera Computer Company where he was project manager for the MTA, and oversaw the programming and evaluation of the MTA at the San Diego Supercomputer Center. In 2000, Dr. Feo joined Sun Microsystems as an HPC application specialist. He works with university research groups to optimize and parallelize scientific codes. Dr. Feo has published over two dozen research articles in the areas of parallel parallel programming, parallel programming languages, and application performance.

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  • How to convert my backup.cmd into something I can run in Linux?

    - by blade19899
    Back in the day when i was using windows(and a noob at everything IT) i liked batch scripting so much that i wrote a lot of them and one i am pretty proud of that is my backup.cmd(see below). I am pretty basic with the linux bash sudo/apt-get/sl/ls/locate/updatedb/etc... I don't really know the full power of the terminal. If you see the code below can i get it to work under (Ubuntu)linux :) by rewriting some of the windows code with the linux equivalent (btw:this works under xp/vista/7 | dutch/english) @echo off title back it up :home cls echo ÉÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ» echo º º echo º typ A/B for the options º echo º º echo ÌÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ͹ echo º º echo º "A"=backup options º echo º º echo º "B"=HARDDISK Options º echo º º echo º º echo ÈÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍŒ set /p selection=Choose: Goto %selection% :A cls echo ÉÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ» echo º º echo º typ 1 to start that backup º echo º º echo ÌÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ͹ echo º º echo º "A"=backup options º echo º È1=Documents,Pictures,Music,Videos,Downloads º echo º º echo º "B"=HARDDISK Options º echo º º echo ÈÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍŒ set /p selection=Choose: Goto %selection% :B cls echo ÉÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ» echo º º echo º typ HD to start the disk check º echo º º echo ÌÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍ͹ echo º º echo º "A"=backup options º echo º º echo º "B"=HARDDISK Options º echo º ÈHD=find and repair bad sectors º echo º º echo ÈÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍÍŒ set /p selection=Choose: Goto %selection% :1 cls if exist "%userprofile%\desktop" (set desk=desktop) else (set desk=Bureaublad) if exist "%userprofile%\documents" (set docs=documents) else (set docs=mijn documenten) if exist "%userprofile%\pictures" (set pics=pictures) else (echo cant find %userprofile%\pictures) if exist "%userprofile%\music" (set mus=music) else (echo cant find %userprofile%\music) if exist "%userprofile%\Videos" (set vids=videos) else (echo cant find %userprofile%\videos) if exist "%userprofile%\Downloads" (set down=downloads) else (echo cant find %userprofile%\Downloads) cls echo. examples (D:\) (D:\Backup) (D:\Backup\18-4-2011) echo. echo. if there is no "D:\backup" folder then the folder will be created echo. set drive= set /p drive=storage: echo start>>backup.log echo Name:%username%>>backup.log echo Date:%date%>>backup.log echo Time:%time%>>backup.log echo ========================================%docs%===========================================>>backup.log echo %docs% echo Source:"%userprofile%\%docs%" echo Destination:"%drive%\%username%\%docs%" echo %time%>>backup.log xcopy "%userprofile%\%docs%" "%drive%\%username%\%docs%" /E /I>>Backup.log echo 20%% cls echo ========================================"%pics%"=========================================>>backup.log echo "%pics%" echo Source:"%userprofile%\%pics%" echo Destination:"%drive%\%username%\%pics%" echo %time%>>backup.log xcopy "%userprofile%\%pics%" "%drive%\%username%\%pics%" /E /I>>Backup.log echo 40%% cls echo ========================================"%mus%"=========================================>>backup.log echo "%mus%" echo Source:"%userprofile%\%mus%" echo Destination:"%drive%\%username%\%mus%" echo %time%>>backup.log xcopy "%userprofile%\%mus%" "%drive%\%username%\%mus%" /E /I>>Backup.log echo 60%% cls echo ========================================"%vids%"========================================>>backup.log echo %vids% echo Source:"%userprofile%\%vids%" echo Destination:"%drive%\%username%\%vids%" echo %time%>>backup.log xcopy "%userprofile%\%vids%" "%drive%\%username%\%vids%" /E /I>>Backup.log echo 80%% cls echo ========================================"%down%"========================================>>backup.log echo "%down%" echo Source:"%userprofile%\%down%" echo Destination:"%drive%\%username%\%down%" echo %time%>>backup.log xcopy "%userprofile%\%down%" "%drive%\%username%\%down%" /E /I>>Backup.log echo end>>backup.log echo %username% %date% %time%>>backup.log echo 100%% cls echo backup Compleet copy "backup.log" "%drive%\%username%" del "backup.log" pushd "%drive%\%username%" echo close backup.log to continue with backup script "backup.log" echo press any key to retun to the main menu pause>nul goto :home :HD echo finds and repairs bad sectors echo typ in harddisk letter (C: D: E:) set HD= set /p HD=Hard Disk: chkdsk %HD% /F /R /X pause goto :home

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  • WPF: Timers

    - by Ilya Verbitskiy
    I believe, once your WPF application will need to execute something periodically, and today I would like to discuss how to do that. There are two possible solutions. You can use classical System.Threading.Timer class or System.Windows.Threading.DispatcherTimer class, which is the part of WPF. I have created an application to show you how to use the API.     Let’s take a look how you can implement timer using System.Threading.Timer class. First of all, it has to be initialized.   1: private Timer timer; 2:   3: public MainWindow() 4: { 5: // Form initialization code 6: 7: timer = new Timer(OnTimer, null, Timeout.InfiniteTimeSpan, Timeout.InfiniteTimeSpan); 8: }   Timer’s constructor accepts four parameters. The first one is the callback method which is executed when timer ticks. I will show it to you soon. The second parameter is a state which is passed to the callback. It is null because there is nothing to pass this time. The third parameter is the amount of time to delay before the callback parameter invokes its methods. I use System.Threading.Timeout helper class to represent infinite timeout which simply means the timer is not going to start at the moment. And the final fourth parameter represents the time interval between invocations of the methods referenced by callback. Infinite timeout timespan means the callback method will be executed just once. Well, the timer has been created. Let’s take a look how you can start the timer.   1: private void StartTimer(object sender, RoutedEventArgs e) 2: { 3: timer.Change(TimeSpan.Zero, new TimeSpan(0, 0, 1)); 4:   5: // Disable the start buttons and enable the reset button. 6: }   The timer is started by calling its Change method. It accepts two arguments: the amount of time to delay before the invoking the callback method and the time interval between invocations of the callback. TimeSpan.Zero means we start the timer immediately and TimeSpan(0, 0, 1) tells the timer to tick every second. There is one method hasn’t been shown yet. This is the callback method OnTimer which does a simple task: it shows current time in the center of the screen. Unfortunately you cannot simple write something like this:   1: clock.Content = DateTime.Now.ToString("hh:mm:ss");   The reason is Timer runs callback method on a separate thread, and it is not possible to access GUI controls from a non-GUI thread. You can avoid the problem using System.Windows.Threading.Dispatcher class.   1: private void OnTimer(object state) 2: { 3: Dispatcher.Invoke(() => ShowTime()); 4: } 5:   6: private void ShowTime() 7: { 8: clock.Content = DateTime.Now.ToString("hh:mm:ss"); 9: }   You can build similar application using System.Windows.Threading.DispatcherTimer class. The class represents a timer which is integrated into the Dispatcher queue. It means that your callback method is executed on GUI thread and you can write a code which updates your GUI components directly.   1: private DispatcherTimer dispatcherTimer; 2:   3: public MainWindow() 4: { 5: // Form initialization code 6:   7: dispatcherTimer = new DispatcherTimer { Interval = new TimeSpan(0, 0, 1) }; 8: dispatcherTimer.Tick += OnDispatcherTimer; 9: } Dispatcher timer has nicer and cleaner API. All you need is to specify tick interval and Tick event handler. The you just call Start method to start the timer.   private void StartDispatcher(object sender, RoutedEventArgs e) { dispatcherTimer.Start(); // Disable the start buttons and enable the reset button. } And, since the Tick event handler is executed on GUI thread, the code which sets the actual time is straightforward.   1: private void OnDispatcherTimer(object sender, EventArgs e) 2: { 3: ShowTime(); 4: } We’re almost done. Let’s take a look how to stop the timers. It is easy with the Dispatcher Timer.   1: dispatcherTimer.Stop(); And slightly more complicated with the Timer. You should use Change method again.   1: timer.Change(Timeout.InfiniteTimeSpan, Timeout.InfiniteTimeSpan); What is the best way to add timer into an application? The Dispatcher Timer has simple interface, but its advantages are disadvantages at the same time. You should not use it if your Tick event handler executes time-consuming operations. It freezes your window which it is executing the event handler method. You should think about using System.Threading.Timer in this case. The code is available on GitHub.

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  • How to store a shmup level?

    - by pek
    I am developing a 2D shmup (i.e. Aero Fighters) and I was wondering what are the various ways to store a level. Assuming that enemies are defined in their own xml file, how would you define when an enemy spawns in the level? Would it be based on time? Updates? Distance? Currently I do this based on "level time" (the amount of time the level is running - pausing doesn't update the time). Here is an example (the serialization was done by XNA): <?xml version="1.0" encoding="utf-8"?> <XnaContent xmlns:level="pekalicious.xanor.XanorContentShared.content.level"> <Asset Type="level:Level"> <Enemies> <Enemy> <EnemyType>data/enemies/smallenemy</EnemyType> <SpawnTime>PT0S</SpawnTime> <NumberOfSpawns>60</NumberOfSpawns> <SpawnOffset>PT0.2S</SpawnOffset> </Enemy> <Enemy> <EnemyType>data/enemies/secondenemy</EnemyType> <SpawnTime>PT0S</SpawnTime> <NumberOfSpawns>10</NumberOfSpawns> <SpawnOffset>PT0.5S</SpawnOffset> </Enemy> <Enemy> <EnemyType>data/enemies/secondenemy</EnemyType> <SpawnTime>PT20S</SpawnTime> <NumberOfSpawns>10</NumberOfSpawns> <SpawnOffset>PT0.5S</SpawnOffset> </Enemy> <Enemy> <EnemyType>data/enemies/boss1</EnemyType> <SpawnTime>PT30S</SpawnTime> <NumberOfSpawns>1</NumberOfSpawns> <SpawnOffset>PT0S</SpawnOffset> </Enemy> </Enemies> </Asset> </XnaContent> Each Enemy element is basically a wave of specific enemy types. The type is defined in EnemyType while SpawnTime is the "level time" this wave should appear. NumberOfSpawns and SpawnOffset is the number of enemies that will show up and the time it takes between each spawn respectively. This could be a good idea or there could be better ones out there. I'm not sure. I would like to see some opinions and ideas. I have two problems with this: spawning an enemy correctly and creating a level editor. The level editor thing is an entirely different problem (which I will probably post in the future :P). As for spawning correctly, the problem lies in the fact that I have a variable update time and so I need to make sure I don't miss an enemy spawn because the spawn offset is too small, or because the update took a little more time. I kinda fixed it for the most part, but it seems to me that the problem is with how I store the level. So, any ideas? Comments? Thank you in advance.

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  • JDeveloper 11g R1 (11.1.1.4.0) - New Features on ADF Desktop Integration Explained

    - by juan.ruiz
    One of the areas that introduced many new features on the latest release (11.1.1.4.0)  of JDeveloper 11g R1 is ADF Desktop integration - in this article I’ll provide an overview of these new features. New ADF Desktop Integration Ribbon in Excel - After installing the ADF desktop integration add-in and depending on the mode in which you open the desktop integration workbook, the ADF Desktop integration ribbon for design time and runtime are displayed as a separate tab within Excel. In previous version the ADF Desktop integration environment used to be placed inside the add-ins tab. Above you can see both, design time ribbon as well as runtime ribbon. On the design time ribbon you can manage the workbook and worksheet properties, worksheet component properties, diagnostics, execution and publication of the workbook. The runtime version of the ribbon is totally customizable and represents what it used to be the runtime menu on the spreadsheet, in this ribbon you can include all the operations and actions that could be executed by the end user while working with the spreadsheet data. Diagnostics - A very important aspect for developers is how to debug or verify the interactions of the client with the server, for that ADF desktop integration has provided since day one a series of diagnostics tools. In this release the diagnostics tools are more visible and are really easy to configure. You can access the client console while testing the workbook, or you can simple dump all the messages to a log file – having the ability of setting the output level for both. Security - There are a number of enhancements on security but the one with more impact for developers is tha security now is optional when using ADF Desktop Integration. Until this version every time that you wanted to work with ADFdi it was a must that the application was previously secured. In this release security is optional which means that if you have previously defined security on your application, then you must secure the ADFdi servlet as explained in one of my previous (ADD LINK) posts. In the other hand, if but the time that you start working with ADFdi you have not defined security, you can test and publish your workbooks without adding security. Support for Continuous Integration - In this release we have added tooling for continuous integration building. in the ADF desktop integration space, the concept translates to adding functionality that developers can use to publish ADFdi workbooks as part of their entire application build. For that purpose, we have a publish tool that can be easily invoke from an ANT task such that all the design time workbooks are re-published into the latest version of the application building process. Key Column - At runtime, on any worksheet containing editable tables you will notice a new additional column called the key column. The purpose of this column is to make the end user aware that all rows on the table need to be selected at the time of sorting. The users cannot alter the value of this column. From the developers points of view there are no steps required in order to have the key column included into the worksheets. Installation and Creation of New Workbooks - Both use cases can be executed now directly from JDeveloper. As part of the Tools menu options the developer can install the ADF desktop integration designer. Also, creating new workbooks that previously was done through that convert tool shipped with JDeveloper is now automatic done from the New Gallery. Creating a new ADFdi workbook adds metadata information information to the Excel workbook so you can work in design time. Other Enhancements Support for Excel 2010 and the ADF components ready-only enabled don’t allow to change its value – the cell in Excel is automatically protected, this could cause confusion among customers of previous releases.

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  • Sun Ray Hardware Last Order Dates & Extension of Premier Support for Desktop Virtualization Software

    - by Adam Hawley
    In light of the recent announcement  to end new feature development for Oracle Virtual Desktop Infrastructure Software (VDI), Oracle Sun Ray Software (SRS), Oracle Virtual Desktop Client (OVDC) Software, and Oracle Sun Ray Client hardware (3, 3i, and 3 Plus), there have been questions and concerns regarding what this means in terms of customers with new or existing deployments.  The following updates clarify some of these commonly asked questions. Extension of Premier Support for Software Though there will be no new feature additions to these products, customers will have access to maintenance update releases for Oracle Virtual Desktop Infrastructure and Sun Ray Software, including Oracle Virtual Desktop Client and Sun Ray Operating Software (SROS) until Premier Support Ends.  To ensure that customer investments for these products are protected, Oracle  Premier Support for these products has been extended by 3 years to following dates: Sun Ray Software - November 2017 Oracle Virtual Desktop Infrastructure - March 2017 Note that OVDC support is also extended to the above dates since OVDC is licensed by default as part the SRS and VDI products.   As a reminder, this only affects the products listed above.  Oracle Secure Global Desktop and Oracle VM VirtualBox will continue to be enhanced with new features from time-to-time and, as a result, they are not affected by the changes detailed in this message. The extension of support means that customers under a support contract will still be able to file service requests through Oracle Support, and Oracle will continue to provide the utmost level of support to our customers as expected,  until the published Premier Support end date.  Following the end of Premier Support, Sustaining Support remains an 'indefinite' period of time.   Sun Ray 3 Series Clients - Last Order Dates For Sun Ray Client hardware, customers can continue to purchase Sun Ray Client devices until the following last order dates: Product Marketing Part Number Last Order Date Last Ship Date Sun Ray 3 Plus TC3-P0Z-00, TC3-PTZ-00 (TAA) September 13, 2013 February 28, 2014 Sun Ray 3 Client TC3-00Z-00 February 28, 2014 August 31, 2014 Sun Ray 3i Client TC3-I0Z-00 February 28, 2014 August 31, 2014 Payflex Smart Cards X1403A-N, X1404A-N February 28, 2014 August 31, 2014 Note the difference in the Last Order Date for the Sun Ray 3 Plus (September 13, 2013) compared to the other products that have a Last Order Date of February 28, 2014. The rapidly approaching date for Sun Ray 3 Plus is due to a supplier phasing-out production of a key component of the 3 Plus.   Given September 13 is unfortunately quite soon, we strongly encourage you to place your last time buy as soon as possible to maximize Oracle's ability fulfill your order. Keep in mind you can schedule shipments to be delivered as late as the end of February 2014, but the last day to order is September 13, 2013. Customers wishing to purchase other models - Sun Ray 3 Clients and/or Sun Ray 3i Clients - have additional time (until February 28, 2014) to assess their needs and to allow fulfillment of last time orders.  Please note that availability of supply cannot be absolutely guaranteed up to the last order dates and we strongly recommend placing last time buys as early as possible.  Warranty replacements for Sun Ray Client hardware for customers covered by Oracle Hardware Systems Support contracts will be available beyond last order dates, per Oracle's policy found on Oracle.com here.  Per that policy, Oracle intends to provide replacement hardware for up to 5 years beyond the last ship date, but hardware may not be available beyond the 5 year period after the last ship date for reasons beyond Oracle's control. In any case, by design, Sun Ray Clients have an extremely long lifespan  and mean time between failures (MTBF) - much longer than PCs, and over the years we have continued to see first- and second generations of Sun Rays still in daily use.  This is no different for the Sun Ray 3, 3i, and 3 Plus.   Because of this, and in addition to Oracle's continued support for SRS, VDI, and SROS, Sun Ray and Oracle VDI deployments can continue to expand and exist as a viable solution for some time in the future. Continued Availability of Product Licenses and Support Oracle will continue to offer all existing software licenses, and software and hardware support including: Product licenses and Premier Support for Sun Ray Software and Oracle Virtual Desktop Infrastructure Premier Support for Operating Systems (for Sun Ray Operating Software maintenance upgrades/support)  Premier Support for Systems (for Sun Ray Operating Software maintenance upgrades/support and hardware warranty) Support renewals For More Information For more information, please refer to the following documents for specific dates and policies associated with the support of these products: Document 1478170.1 - Oracle Desktop Virtualization Software and Hardware Lifetime Support Schedule Document 1450710.1 - Sun Ray Client Hardware Lifetime schedule Document 1568808.1 - Document Support Policies for Discontinued Oracle Virtual Desktop Infrastructure, Sun Ray Software and Hardware and Oracle Virtual Desktop Client Development For Sales Orders and Questions Please contact your Oracle Sales Representative or Saurabh Vijay ([email protected])

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  • Organization &amp; Architecture UNISA Studies &ndash; Chap 6

    - by MarkPearl
    Learning Outcomes Discuss the physical characteristics of magnetic disks Describe how data is organized and accessed on a magnetic disk Discuss the parameters that play a role in the performance of magnetic disks Describe different optical memory devices Magnetic Disk The way data is stored on and retried from magnetic disks Data is recorded on and later retrieved form the disk via a conducting coil named the head (in many systems there are two heads) The writ mechanism exploits the fact that electricity flowing through a coil produces a magnetic field. Electric pulses are sent to the write head, and the resulting magnetic patterns are recorded on the surface below with different patterns for positive and negative currents The physical characteristics of a magnetic disk   Summarize from book   The factors that play a role in the performance of a disk Seek time – the time it takes to position the head at the track Rotational delay / latency – the time it takes for the beginning of the sector to reach the head Access time – the sum of the seek time and rotational delay Transfer time – the time it takes to transfer data RAID The rate of improvement in secondary storage performance has been considerably less than the rate for processors and main memory. Thus secondary storage has become a bit of a bottleneck. RAID works on the concept that if one disk can be pushed so far, additional gains in performance are to be had by using multiple parallel components. Points to note about RAID… RAID is a set of physical disk drives viewed by the operating system as a single logical drive Data is distributed across the physical drives of an array in a scheme known as striping Redundant disk capacity is used to store parity information, which guarantees data recoverability in case of a disk failure (not supported by RAID 0 or RAID 1) Interesting to note that the increase in the number of drives, increases the probability of failure. To compensate for this decreased reliability RAID makes use of stored parity information that enables the recovery of data lost due to a disk failure.   The RAID scheme consists of 7 levels…   Category Level Description Disks Required Data Availability Large I/O Data Transfer Capacity Small I/O Request Rate Striping 0 Non Redundant N Lower than single disk Very high Very high for both read and write Mirroring 1 Mirrored 2N Higher than RAID 2 – 5 but lower than RAID 6 Higher than single disk Up to twice that of a signle disk for read Parallel Access 2 Redundant via Hamming Code N + m Much higher than single disk Highest of all listed alternatives Approximately twice that of a single disk Parallel Access 3 Bit interleaved parity N + 1 Much higher than single disk Highest of all listed alternatives Approximately twice that of a single disk Independent Access 4 Block interleaved parity N + 1 Much higher than single disk Similar to RAID 0 for read, significantly lower than single disk for write Similar to RAID 0 for read, significantly lower than single disk for write Independent Access 5 Block interleaved parity N + 1 Much higher than single disk Similar to RAID 0 for read, lower than single disk for write Similar to RAID 0 for read, generally  lower than single disk for write Independent Access 6 Block interleaved parity N + 2 Highest of all listed alternatives Similar to RAID 0 for read; lower than RAID 5 for write Similar to RAID 0 for read, significantly lower than RAID 5  for write   Read page 215 – 221 for detailed explanation on RAID levels Optical Memory There are a variety of optical-disk systems available. Read through the table on page 222 – 223 Some of the devices include… CD CD-ROM CD-R CD-RW DVD DVD-R DVD-RW Blue-Ray DVD Magnetic Tape Most modern systems use serial recording – data is lade out as a sequence of bits along each track. The typical recording used in serial is referred to as serpentine recording. In this technique when data is being recorded, the first set of bits is recorded along the whole length of the tape. When the end of the tape is reached the heads are repostioned to record a new track, and the tape is again recorded on its whole length, this time in the opposite direction. That process continued back and forth until the tape is full. To increase speed, the read-write head is capable of reading and writing a number of adjacent tracks simultaneously. Data is still recorded serially along individual tracks, but blocks in sequence are stored on adjacent tracks as suggested. A tape drive is a sequential access device. Magnetic tape was the first kind of secondary memory. It is still widely used as the lowest-cost, slowest speed member of the memory hierarchy.

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  • WebLogic Server Performance and Tuning: Part II - Thread Management

    - by Gokhan Gungor
    WebLogic Server, like any other java application server, provides resources so that your applications use them to provide services. Unfortunately none of these resources are unlimited and they must be managed carefully. One of these resources is threads which are pooled to provide better throughput and performance along with the fast response time and to avoid deadlocks. Threads are execution points that WebLogic Server delivers its power and execute work. Managing threads is very important because it may affect the overall performance of the entire system. In previous releases of WebLogic Server 9.0 we had multiple execute queues and user defined thread pools. There were different queues for different type of work which had fixed number of execute threads.  Tuning of this thread pools and finding the proper number of threads was time consuming which required many trials. WebLogic Server 9.0 and the following releases use a single thread pool and a single priority-based execute queue. All type of work is executed in this single thread pool. Its size (thread count) is automatically decreased or increased (self-tuned). The new “self-tuning” system simplifies getting the proper number of threads and utilizing them.Work manager allows your applications to run concurrently in multiple threads. Work manager is a mechanism that allows you to manage and utilize threads and create rules/guidelines to follow when assigning requests to threads. We can set a scheduling guideline or priority a request with a work manager and then associate this work manager with one or more applications. At run-time, WebLogic Server uses these guidelines to assign pending work/requests to execution threads. The position of a request in the execute queue is determined by its priority. There is a default work manager that is provided. The default work manager should be sufficient for most applications. However there can be cases you want to change this default configuration. Your application(s) may be providing services that need mixture of fast response time and long running processes like batch updates. However wrong configuration of work managers can lead a performance penalty while expecting improvement.We can define/configure work managers at;•    Domain Level: config.xml•    Application Level: weblogic-application.xml •    Component Level: weblogic-ejb-jar.xml or weblogic.xml(For a specific web application use weblogic.xml)We can use the following predefined rules/constraints to manage the work;•    Fair Share Request Class: Specifies the average thread-use time required to process requests. The default is 50.•    Response Time Request Class: Specifies a response time goal in milliseconds.•    Context Request Class: Assigns request classes to requests based on context information.•    Min Threads Constraint: Limits the number of concurrent threads executing requests.•    Max Threads Constraint: Guarantees the number of threads the server will allocate to requests.•    Capacity Constraint: Causes the server to reject requests only when it has reached its capacity. Let’s create a work manager for our application for a long running work.Go to WebLogic console and select Environment | Work Managers from the domain structure tree. Click New button and select Work manager and click next. Enter the name for the work manager and click next. Then select the managed server instances(s) or clusters from available targets (the one that your long running application is deployed) and finish. Click on MyWorkManager, and open the Configuration tab and check Ignore Stuck Threads and save. This will prevent WebLogic to tread long running processes (that is taking more than a specified time) as stuck and enable to finish the process.

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  • I am trying to create an windows application watcher? [migrated]

    - by Broken_Code
    I recently started coding in c #(in may this year) and well I find it best to learn by working with code. this application http://www.c-sharpcorner.com/UploadFile/satisharveti/ActiveApplicationWatcher01252007024921AM/ActiveApplicationWatcher.aspx. I am trying to recreate it however mine will be saving the information into an sql database(new at this as well). I am having some coding problems though as it does not do what I expect it to do. THis is the main code I am using. private void GetTotalTimer() { DateTime now = DateTime.Now; IntPtr hwnd = APIFunc.getforegroundWindow(); Int32 pid = APIFunc.GetWindowProcessID(hwnd); Process p = Process.GetProcessById(pid); appName = p.ProcessName; const int nChars = 256; int handle = 0; StringBuilder Buff = new StringBuilder(nChars); handle = GetForegroundWindow(); appltitle = APIFunc.ActiveApplTitle().Trim().Replace("\0", ""); //if (GetWindowText(handle, Buff, nChars) > 0) //{ // string strbuff = Buff.ToString(); // StrWindow = strbuff; #region insert statement try { if (Conn.State == ConnectionState.Closed) { Conn.Open(); } if (Conn.State == ConnectionState.Open) { SqlCommand com = new SqlCommand("Select top 1 [Window Title] From TimerLogs ORDER BY [Time of Event] DESC", Conn); SqlDataReader reader = com.ExecuteReader(); startTime = DateTime.Now; string time = now.ToString(); if (!reader.HasRows) { reader.Close(); cmd = new SqlCommand("insert into [TimerLogs] values(@time,@appName,@appltitle,@Elapsed_Time,@userName)", Conn); cmd.Parameters.AddWithValue("@time", time); cmd.Parameters.AddWithValue("@appName", appName); cmd.Parameters.AddWithValue("@appltitle", appltitle); cmd.Parameters.AddWithValue("@Elapsed_Time", blank.ToString()); cmd.Parameters.AddWithValue("@userName", userName); cmd.ExecuteNonQuery(); Conn.Close(); } else if(reader.HasRows) { reader.Read(); if (appltitle != reader.ToString()) { reader.Close(); endTime = DateTime.Now; appduration = endTime.Subtract(startTime); cmd = new SqlCommand("insert into [TimerLogs] values (@time,@appName,@appltitle,@Elapsed_Time,@userName)", Conn); cmd.Parameters.AddWithValue("@time", time); cmd.Parameters.AddWithValue("@appName", appName); cmd.Parameters.AddWithValue("@appltitle", appltitle); cmd.Parameters.AddWithValue("@Elapsed_Time", appduration.ToString()); cmd.Parameters.AddWithValue("@userName", userName); cmd.ExecuteNonQuery(); reader.Close(); Conn.Close(); } } } } catch (Exception) { } //} #endregion ActivityTimer.Start(); Processing = "Working"; } Unfortunately this is the result. it is not saving the data as I expect it to. What am i doing wrong I had thought that with the sql reader it would first check for a value and only save if they do not match however it is saving whether there is a match or not.

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  • Lessons learnt in implementing Scrum in a Large Organization that has traditional values

    - by MarkPearl
    I recently had the experience of being involved in a “test” scrum implementation in a large organization that was used to a traditional project management approach. Here are some lessons that I learnt from it. Don’t let the Project Manager be the Product Owner First lesson learnt is to identify the correct product owner – in this instance the product manager assumed the role of the product owner which was a mistake. The product owner is the one who has the most to loose if the project fails. With a methodology that advocates removing the role of the project manager from the process then it is not in the interests of the person who is employed as a project manager to be the product owner – in fact they have the most to gain should the project fail. Know the time commitments of team members to the Project Second lesson learnt is to get a firm time commitment of the members on a team for the sprint and to hold them to it. In this project instance many of the issues we faced were with team members having to double up on supporting existing projects/systems and the scrum project. In many situations they just didn’t get round to doing any work on the scrum project for several days while they tried to meet other commitments. Initially this was not made transparent to the team – in stand up team members would say that had done some work but would be very vague on how much time they had actually spent using the blackhole of their other legacy projects as an excuse – putting up a time burn down chart made time allocations transparent and easy to hold the team to. In addition, how can you plan for a sprint without knowing the actual time available of the members – when I mean actual time, the exercise of getting them to go through all their appointments and lunch times and breaks and removing them from their time commitment helps get you to a realistic time that they can dedicate. Make sure you meet your minimum team sizes In a recent post I wrote about the difference between a partnership and a team. If you are going to do scrum in a large organization make sure you have a minimum team size of at least 3 developers. My experience with larger organizations is that people have a tendency to be sick more, take more leave and generally not be around – if you have a team size of two it is so easy to loose momentum on the project – the more people you have in the team (up to about 9) the more the momentum the project will have when people are not around. Swapping from one methodology to another can seem as waste to the customer It sounds bad, but most customers don’t care what methodology you use. Often they have bought into the “big plan upfront”. If you can, avoid taking a project on midstream from a traditional approach unless the customer has not bought into the process – with this particular project they had a detailed upfront planning breakaway with the customer using the traditional approach and then before the project started we moved onto a scrum implementation – this seemed as waste to the customer. We should have managed the customers expectation properly. Don’t play the role of the scrum master if you can’t be the scrum master With this particular implementation I was the “scrum master”. But all I did was go through the process of the formal meetings of scrum – I attended stand up, retrospectives and planning – but I was not hands on the ground. I was not performing the most important role of removing blockages – and by the end of the project there were a number of blockages “cropping up”. What could have been a better approach was to take someone on the team and train them to be the scrum master and be present to coach them. Alternatively actually be on the team on a fulltime basis and be the scrum master. By just going through the meetings of scrum didn’t mean we were doing scrum. So we failed with this one, if you fail look at it from an agile perspective As this particular project drew to a close and it became more and more apparent that it was not going to succeed the failure of it became depressing. Emotions were expressed by various people on the team that we not encouraging and enforced the failure. Embracing the failure and looking at it for what it is instead of taking it as the end of the world can change how you grow from the experience. Acknowledging that it failed and then focussing on learning from why and how to avoid the failure in the future can change how you feel emotionally about the team, the project and the organization.

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  • Get More Value From Your Oracle Premier Support Investment

    - by Get Proactive Customer Adoption Team
    Untitled Document The Return on Investment in Support Training I’m a typical software user. I’ve been using spreadsheets almost daily for the past 10 years or so. I know how to enter simple formulas, format cells, import files, and I can sort and filter. Sometimes I even use a pivot table. I never attended training. I learnt everything I know on the fly. Sometimes it was intuitive and easy, other times I had to spend minutes and even hours searching for a solution. Yet when I see what some other people can do with their spreadsheets, I know I’m utilizing maybe 15% of the functionality. Pity, one day I really have to sign up for training. Why haven’t I done it yet? Ah, you know, I’m a busy person, I have work to do. And if I need to use a feature that I am unfamiliar with, I’ll spend time on it only when I really need it. Now wait. When I recall how much time I spent trying to figure how things work compared to time I spent doing the productive work, I realize it was not insignificant. I’m unable to sum up all the time I spent ‘learning’ on the fly, but I’m sure it’s been days or even weeks. And after all this time, I’ve mastered 15% of its features. If only I had attended training years ago. That investment would have paid back 10 times! Working with My Oracle Support is no different. Our customers typically use simple search, create service requests, and download patches. They think they know how to use My Oracle Support. And they’re right. They know something but often they’re utilizing only a fragment of My Oracle Support’s potential. For the investment that has been made, using only a small subset of the capabilities offered in My Oracle Support leaves value on the table. There is much more available in My Oracle Support. Dozens of diagnostic tools and proactive health checks will keep verifying your Oracle environments against best practices that Oracle gathers every day thanks to our comprehensive knowledge management process. Automated patch recommendations will help prevent known issues, and upgrade planning and more is included in My Oracle Support. Why are you not utilizing all of these best practices, capabilities and tools? Is it because you don’t have time to invest 2-3 hours of your time to learn about the features? Simply because you think you can learn on the fly like I thought I could? Does learning on the fly how to properly use the Service Request escalation process when you already have critical issue sound like a good idea? My advice is: Invest your time now to learn how My Oracle Support can help you prevent issues on your systems. Learn how to find answers faster and resolve problems more efficiently. Understand how to properly complete a service request. Invest in Support training, offered at no additional cost to Oracle Premier Support customers. It will pay back quicker than you think. It will bring you more value than you think. Discover your advantage with Oracle Premier Support's Proactive Portfolio.

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  • How many bits for sequence number using Go-Back-N protocol.

    - by Mike
    Hi Everyone, I'm a regular over at Stack Overflow (Software developer) that is trying to get through a networking course. I got a homework problem I'd like to have a sanity check on. Here is what I got. Q: A 3000-km-long T1 trunk is used to transmit 64-byte frames using Go-Back-N protocol. If the propagation speed is 6 microseconds/km, how many bits should the sequence numbers be? My Answer: For this questions what we need to do is lay the base knowledge. What we are trying to find is the size of the largest sequence number we should us using Go-Back-N. To figure this out we need to figure out how many packets can fit into our link at a time and then subtract one from that number. This will ensure that we never have two packets with the same sequence number at the same time in the link. Length of link: 3,000km Speed: 6 microseconds / km Frame size: 64 bytes T1 transmission speed: 1544kb/s (http://ckp.made-it.com/t1234.html) Propagation time = 6 microseconds / km * 3000 km = 18,000 microseconds (18ms). Convert 1544kb to bytes = 1544 * 1024 = 1581056 bytes Transmission time = 64 bytes / 1581056bytes / second = 0.000040479 seconds (0.4ms) So then if we take the 18ms propagation time and divide it by the 0.4ms transmission time we will see that we are going to be able to stuff ( 18 / 0.4) 45 packets into the link at a time. That means that our sequence number should be 2 ^ 45 bits long! Am I going in the right direction with this? Thanks, Mike

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  • nokia cell phone not accepting IP from dnsmasq dhcp server

    - by samix
    Hello, I having problem connecting a NOkia cell phone to my home wifi network. The wifi network is provided by a wireless card in a machine running Debian Testing and 2.6.26-2-686 kernel. The cars is D-Link DWL-G520 working in ap mode and has WPA encryption enabled. The wireless network is provided by hostapd using madwifi driver. Windows and Mac machines work properly with this wifi network. When I try to get the Nokia phone to connect to the wifi network, I get these lines in my dnsmasq log (to see lines without wrapping, here is the pastebin link for convenience - http://pastebin.com/m466c8fd2): Oct 27 13:25:21 red hostapd: ath0: STA 11:22:33:44:55:66 IEEE 802.11: disassociated Oct 27 13:25:21 red hostapd: ath0: STA 11:22:33:44:55:66 IEEE 802.11: associated Oct 27 13:25:21 red hostapd: ath0: STA 11:22:33:44:55:66 RADIUS: starting accounting session 4AE664FA-00000036 Oct 27 13:25:21 red hostapd: ath0: STA 11:22:33:44:55:66 WPA: pairwise key handshake completed (WPA) Oct 27 13:25:21 red hostapd: ath0: STA 11:22:33:44:55:66 WPA: group key handshake completed (WPA) Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 Available DHCP range: 192.168.5.150 -- 192.168.5.199 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 DHCPDISCOVER(ath0) 0.0.0.0 11:22:33:44:55:66 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 DHCPOFFER(ath0) 192.168.5.21 11:22:33:44:55:66 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 requested options: 12:hostname, 6:dns-server, 15:domain-name, Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 requested options: 1:netmask, 3:router, 28:broadcast, 120:sip-server Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 tags: known, ath0 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 next server: 192.168.5.1 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 sent size: 1 option: 53:message-type 02 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 sent size: 4 option: 54:server-identifier 192.168.5.1 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 sent size: 4 option: 51:lease-time 00:00:46:50 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 sent size: 4 option: 58:T1 00:00:23:28 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 sent size: 4 option: 59:T2 00:00:3d:86 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 sent size: 4 option: 1:netmask 255.255.255.0 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 sent size: 4 option: 28:broadcast 192.168.5.255 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 sent size: 4 option: 3:router 192.168.5.1 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 sent size: 4 option: 6:dns-server 192.168.5.1 Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 sent size: 8 option: 15:domain-name home.pvt Oct 27 13:25:21 red dnsmasq-dhcp[11451]: 3875439214 sent size: 3 option: 12:hostname NokiaCellPhone Anybody know the problem might be? If I switch off dnsmasq dhcp queries logging, i.e. if I decrease the verbosity of the log, all I see are two lines of DHCPDISCOVER(ath0) and DHCPOFFER(ath0) repeatedly in the log with no acceptance by the cell phone. It appears as though the phone is not accepting the dhcp offer. However, if I give the phone a static IP address in its configuration, it works properly on the wifi network. So it appears as though the problem is dhcp related. Hints? Suggestions? Installed stuff: $ dpkg -l dnsmasq hostap* | grep ^i ii dnsmasq 2.50-1 A small caching DNS proxy and DHCP/TFTP server ii dnsmasq-base 2.50-1 A small caching DNS proxy and DHCP/TFTP server ii hostapd 1:0.6.9-3 user space IEEE 802.11 AP and IEEE 802.1X/WPA/ Thanks. PS: Here is the DHCP tcp dump for more information (with mac addresses changed): $ sudo dhcpdump -i ath0 -h ^11:22:33:44:55:66 TIME: 2009-10-30 12:15:32.916 IP: 0.0.0.0 (1:22:33:44:55:66) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 1 (BOOTPREQUEST) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: c3f93d53 SECS: 0 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 0.0.0.0 SIADDR: 0.0.0.0 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 1 (DHCPDISCOVER) OPTION: 50 ( 4) Request IP address 0.0.0.0 OPTION: 61 ( 7) Client-identifier 01:11:22:33:44:55:66 OPTION: 55 ( 7) Parameter Request List 12 (Host name) 6 (DNS server) 15 (Domainname) 1 (Subnet mask) 3 (Routers) 28 (Broadcast address) 120 (SIP Servers DHCP Option) OPTION: 57 ( 2) Maximum DHCP message size 576 TIME: 2009-10-30 12:15:32.918 IP: 0.0.0.0 (1:22:33:44:55:66) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 1 (BOOTPREQUEST) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: c3f93d53 SECS: 0 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 0.0.0.0 SIADDR: 0.0.0.0 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 1 (DHCPDISCOVER) OPTION: 50 ( 4) Request IP address 0.0.0.0 OPTION: 61 ( 7) Client-identifier 01:11:22:33:44:55:66 OPTION: 55 ( 7) Parameter Request List 12 (Host name) 6 (DNS server) 15 (Domainname) 1 (Subnet mask) 3 (Routers) 28 (Broadcast address) 120 (SIP Servers DHCP Option) OPTION: 57 ( 2) Maximum DHCP message size 576 TIME: 2009-10-30 12:15:32.918 IP: 192.168.5.1 (a:bb:cc:dd:ee:ff) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 2 (BOOTPREPLY) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: c3f93d53 SECS: 0 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 192.168.5.21 SIADDR: 192.168.5.1 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 2 (DHCPOFFER) OPTION: 54 ( 4) Server identifier 192.168.5.1 OPTION: 51 ( 4) IP address leasetime 18000 (5h) OPTION: 58 ( 4) T1 9000 (2h30m) OPTION: 59 ( 4) T2 15750 (4h22m30s) OPTION: 1 ( 4) Subnet mask 255.255.255.0 OPTION: 28 ( 4) Broadcast address 192.168.5.255 OPTION: 3 ( 4) Routers 192.168.5.1 OPTION: 6 ( 4) DNS server 192.168.5.1 OPTION: 15 ( 8) Domainname home.pvt OPTION: 12 ( 3) Host name Nokia_E63 TIME: 2009-10-30 12:15:34.922 IP: 0.0.0.0 (1:22:33:44:55:66) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 1 (BOOTPREQUEST) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: c3f93d53 SECS: 2 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 0.0.0.0 SIADDR: 0.0.0.0 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 1 (DHCPDISCOVER) OPTION: 50 ( 4) Request IP address 0.0.0.0 OPTION: 61 ( 7) Client-identifier 01:11:22:33:44:55:66 OPTION: 55 ( 7) Parameter Request List 12 (Host name) 6 (DNS server) 15 (Domainname) 1 (Subnet mask) 3 (Routers) 28 (Broadcast address) 120 (SIP Servers DHCP Option) OPTION: 57 ( 2) Maximum DHCP message size 576 TIME: 2009-10-30 12:15:34.922 IP: 0.0.0.0 (1:22:33:44:55:66) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 1 (BOOTPREQUEST) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: c3f93d53 SECS: 2 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 0.0.0.0 SIADDR: 0.0.0.0 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 1 (DHCPDISCOVER) OPTION: 50 ( 4) Request IP address 0.0.0.0 OPTION: 61 ( 7) Client-identifier 01:11:22:33:44:55:66 OPTION: 55 ( 7) Parameter Request List 12 (Host name) 6 (DNS server) 15 (Domainname) 1 (Subnet mask) 3 (Routers) 28 (Broadcast address) 120 (SIP Servers DHCP Option) OPTION: 57 ( 2) Maximum DHCP message size 576 TIME: 2009-10-30 12:15:34.923 IP: 192.168.5.1 (a:bb:cc:dd:ee:ff) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 2 (BOOTPREPLY) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: c3f93d53 SECS: 2 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 192.168.5.21 SIADDR: 192.168.5.1 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 2 (DHCPOFFER) OPTION: 54 ( 4) Server identifier 192.168.5.1 OPTION: 51 ( 4) IP address leasetime 18000 (5h) OPTION: 58 ( 4) T1 9000 (2h30m) OPTION: 59 ( 4) T2 15750 (4h22m30s) OPTION: 1 ( 4) Subnet mask 255.255.255.0 OPTION: 28 ( 4) Broadcast address 192.168.5.255 OPTION: 3 ( 4) Routers 192.168.5.1 OPTION: 6 ( 4) DNS server 192.168.5.1 OPTION: 15 ( 8) Domainname home.pvt OPTION: 12 ( 3) Host name Nokia_E63 TIME: 2009-10-30 12:15:38.919 IP: 0.0.0.0 (1:22:33:44:55:66) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 1 (BOOTPREQUEST) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: c3f93d53 SECS: 6 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 0.0.0.0 SIADDR: 0.0.0.0 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 1 (DHCPDISCOVER) OPTION: 50 ( 4) Request IP address 0.0.0.0 OPTION: 61 ( 7) Client-identifier 01:11:22:33:44:55:66 OPTION: 55 ( 7) Parameter Request List 12 (Host name) 6 (DNS server) 15 (Domainname) 1 (Subnet mask) 3 (Routers) 28 (Broadcast address) 120 (SIP Servers DHCP Option) OPTION: 57 ( 2) Maximum DHCP message size 576 TIME: 2009-10-30 12:15:38.920 IP: 0.0.0.0 (1:22:33:44:55:66) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 1 (BOOTPREQUEST) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: c3f93d53 SECS: 6 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 0.0.0.0 SIADDR: 0.0.0.0 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 1 (DHCPDISCOVER) OPTION: 50 ( 4) Request IP address 0.0.0.0 OPTION: 61 ( 7) Client-identifier 01:11:22:33:44:55:66 OPTION: 55 ( 7) Parameter Request List 12 (Host name) 6 (DNS server) 15 (Domainname) 1 (Subnet mask) 3 (Routers) 28 (Broadcast address) 120 (SIP Servers DHCP Option) OPTION: 57 ( 2) Maximum DHCP message size 576 TIME: 2009-10-30 12:15:38.921 IP: 192.168.5.1 (a:bb:cc:dd:ee:ff) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 2 (BOOTPREPLY) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: c3f93d53 SECS: 6 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 192.168.5.21 SIADDR: 192.168.5.1 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 2 (DHCPOFFER) OPTION: 54 ( 4) Server identifier 192.168.5.1 OPTION: 51 ( 4) IP address leasetime 18000 (5h) OPTION: 58 ( 4) T1 9000 (2h30m) OPTION: 59 ( 4) T2 15750 (4h22m30s) OPTION: 1 ( 4) Subnet mask 255.255.255.0 OPTION: 28 ( 4) Broadcast address 192.168.5.255 OPTION: 3 ( 4) Routers 192.168.5.1 OPTION: 6 ( 4) DNS server 192.168.5.1 OPTION: 15 ( 8) Domainname home.pvt OPTION: 12 ( 3) Host name Nokia_E63 TIME: 2009-10-30 12:15:46.944 IP: 0.0.0.0 (1:22:33:44:55:66) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 1 (BOOTPREQUEST) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: ccafe769 SECS: 14 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 0.0.0.0 SIADDR: 0.0.0.0 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 1 (DHCPDISCOVER) OPTION: 50 ( 4) Request IP address 0.0.0.0 OPTION: 61 ( 7) Client-identifier 01:11:22:33:44:55:66 OPTION: 55 ( 7) Parameter Request List 12 (Host name) 6 (DNS server) 15 (Domainname) 1 (Subnet mask) 3 (Routers) 28 (Broadcast address) 120 (SIP Servers DHCP Option) OPTION: 57 ( 2) Maximum DHCP message size 576 TIME: 2009-10-30 12:15:46.944 IP: 0.0.0.0 (1:22:33:44:55:66) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 1 (BOOTPREQUEST) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: ccafe769 SECS: 14 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 0.0.0.0 SIADDR: 0.0.0.0 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 1 (DHCPDISCOVER) OPTION: 50 ( 4) Request IP address 0.0.0.0 OPTION: 61 ( 7) Client-identifier 01:11:22:33:44:55:66 OPTION: 55 ( 7) Parameter Request List 12 (Host name) 6 (DNS server) 15 (Domainname) 1 (Subnet mask) 3 (Routers) 28 (Broadcast address) 120 (SIP Servers DHCP Option) OPTION: 57 ( 2) Maximum DHCP message size 576 TIME: 2009-10-30 12:15:46.945 IP: 192.168.5.1 (a:bb:cc:dd:ee:ff) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 2 (BOOTPREPLY) HTYPE: 1 (Ethernet) HLEN: 6 HOPS: 0 XID: ccafe769 SECS: 14 FLAGS: 7f80 CIADDR: 0.0.0.0 YIADDR: 192.168.5.21 SIADDR: 192.168.5.1 GIADDR: 0.0.0.0 CHADDR: 11:22:33:44:55:66:00:00:00:00:00:00:00:00:00:00 SNAME: . FNAME: . OPTION: 53 ( 1) DHCP message type 2 (DHCPOFFER) OPTION: 54 ( 4) Server identifier 192.168.5.1 OPTION: 51 ( 4) IP address leasetime 18000 (5h) OPTION: 58 ( 4) T1 9000 (2h30m) OPTION: 59 ( 4) T2 15750 (4h22m30s) OPTION: 1 ( 4) Subnet mask 255.255.255.0 OPTION: 28 ( 4) Broadcast address 192.168.5.255 OPTION: 3 ( 4) Routers 192.168.5.1 OPTION: 6 ( 4) DNS server 192.168.5.1 OPTION: 15 ( 8) Domainname home.pvt OPTION: 12 ( 3) Host name Nokia_E63 TIME: 2009-10-30 12:15:48.952 IP: 0.0.0.0 (1:22:33:44:55:66) 255.255.255.255 (ff:ff:ff:ff:ff:ff) OP: 1 (BOOTPREQUEST) HTYPE: 1 (Ethernet) HLEN: 6 ... and so on ...

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  • iPod touch has extremely slow wifi, drops packets - only on my router

    - by mskfisher
    I just purchased an iPod Touch. I am having a lot of trouble with its speeds on my Tenda W311R, but it has no speed problems on my neighbor's Netgear router. It will connect and authenticate to my network, but the Speed Test app from speedtest.net shows rates near 20-50 kbps. If I run the speed test immediately after powering the iPod on, it will get speeds of 10-20 Mbps, like it should - but the speeds slow down to the kbps range abut 10-15 seconds afterward. I get the same behavior with encryption and without encryption, and regardless of N, G, or B compatibility settings in the router. I've tried rebooting the iPod and resetting the network settings, but it's still slow. I've tried pinging the iPod from another computer, and it shows about 40% packet loss: $ ping 192.168.0.111 PING 192.168.0.111 (192.168.0.111): 56 data bytes 64 bytes from 192.168.0.111: icmp_seq=0 ttl=64 time=14.188 ms 64 bytes from 192.168.0.111: icmp_seq=1 ttl=64 time=11.556 ms 64 bytes from 192.168.0.111: icmp_seq=2 ttl=64 time=5.675 ms 64 bytes from 192.168.0.111: icmp_seq=3 ttl=64 time=5.721 ms Request timeout for icmp_seq 4 64 bytes from 192.168.0.111: icmp_seq=5 ttl=64 time=6.491 ms Request timeout for icmp_seq 6 64 bytes from 192.168.0.111: icmp_seq=7 ttl=64 time=8.065 ms Request timeout for icmp_seq 8 Request timeout for icmp_seq 9 Request timeout for icmp_seq 10 64 bytes from 192.168.0.111: icmp_seq=11 ttl=64 time=9.605 ms Signal strength is good - I'm never more than 20 feet from my access point, and it exhibits the same behavior if I'm standing next to the router. It works just well enough to receive text, but videos don't work at all. App downloads are hit and miss. I've tweaked just about all of the settings I can see to tweak, and I'm at a loss. I have also been searching Google for the past three days, all to no avail. Any suggestions?

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  • How to achieve the following RTO & RPO with logshipping only using SQL Server?

    - by Jimmy Chandra
    Trying to come up with viable backup restore & logshipping solution for achieving the following: 15 minutes Recovery Point Objective (no more than 15 minutes data loss at any time) 5 minutes Recovery Time Objective (must be able to get the db up and running back by 5 minutes) Considering using logshipping only (which I think is kind of pushing it, but I want to know if anyone else know how to achieve this). Some other info for consideration: Using 40 Gbit / sec fiber channel between the primary and disaster recovery (DRC) sites The sites are about 600 km apart. At close of business, the amount of data generated is predicted to be about 150 MB/sec. Log backup is planned for every 5 min. Doing some rough calculation I came up w/ the following numbers: 40 Gbit / sec = 5 MB / sec @ 100% network efficiency. 5 MB / sec = 300 MB / min. @ 300 MB / min, the total amount of data that can be transfer considering the 5min RTO is about 1.5GB, but that will left no time for the actual backup and restore, so if we cut it down to 3min logshipping time, which equals to ~900 MB over 3 minutes at 100% network efficiency, that will left about 1 min backup time and 1 minute restore time. Currently don't have any information if the system being used is capable of restoring 900 MB in 1 min, but assume it can. for COB scenario... 150 MB/sec, and considering the 3 min logshipping time, which should equal to about 27 GB of data over 3 mins...??? I think this is where the SLA will break... since there is no way to transfer 27 GB of data over a 40Gbit/sec line in 3 min. Can I get someone else opinion? I am thinking database mirroring might be a better answer for this...

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  • How to debug slow queries in Django+Postgres

    - by lacker
    My database queries from Django are starting to take 1-2 seconds and I'm having trouble figuring out why. Not too big a site, about 1-2 requests per second (that hit Django; static files are just served from nginx.) The thing that confuses me is, I can replicate the slowness in the Django shell using debug mode. But when I issue the exact same queries at an sql prompt they are fast. It takes about a second for a query to return, but when I check connection.queries it reports the time as under 10 ms. Here's an example (from the Django shell): >>> p = PlayerData.objects.get(uid="100000521952372") >>> a = time.time(); p.save(); print time.time() - a 1.96812295914 >>> for d in connection.queries: print d["time"] ... 0.002 0.000 0.000 How can I figure out where this extra time is being spent? I'm using Apache+mod_wsgi in daemon mode, but this happens with just the django shell as well, so I figure it is not apache-related.

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  • AWS elastic load balancer basic issues

    - by Jones
    I have an array of EC2 t1.micro instances behind a load balancer and each node can manage ~100 concurrent users before it starts to get wonky. i would THINK if i have 2 such instances it would allow my network to manage 200 concurrent users... apparently not. When i really slam the server (blitz.io) with a full 275 concurrents, it behaves the same as if there is just one node. it goes from 400ms response time to 1.6 seconds (which for a single t1.micro is expected, but not 6). So the question is, am i simply not doing something right or is ELB effectively worthless? Anyone have some wisdom on this? AB logs: Loadbalancer (3x m1.medium) Document Path: /ping/index.html Document Length: 185 bytes Concurrency Level: 100 Time taken for tests: 11.668 seconds Complete requests: 50000 Failed requests: 0 Write errors: 0 Non-2xx responses: 50001 Total transferred: 19850397 bytes HTML transferred: 9250185 bytes Requests per second: 4285.10 [#/sec] (mean) Time per request: 23.337 [ms] (mean) Time per request: 0.233 [ms] (mean, across all concurrent requests) Transfer rate: 1661.35 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 1 2 4.3 2 63 Processing: 2 21 15.1 19 302 Waiting: 2 21 15.0 19 261 Total: 3 23 15.7 21 304 Single instance (1x m1.medium direct connection) Document Path: /ping/index.html Document Length: 185 bytes Concurrency Level: 100 Time taken for tests: 9.597 seconds Complete requests: 50000 Failed requests: 0 Write errors: 0 Non-2xx responses: 50001 Total transferred: 19850397 bytes HTML transferred: 9250185 bytes Requests per second: 5210.19 [#/sec] (mean) Time per request: 19.193 [ms] (mean) Time per request: 0.192 [ms] (mean, across all concurrent requests) Transfer rate: 2020.01 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 1 9 128.9 3 3010 Processing: 1 10 8.7 9 141 Waiting: 1 9 8.7 8 140 Total: 2 19 129.0 12 3020

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  • How can I disable 'natural breaks' in Workrave?

    - by Pixelastic
    I've just discovered Workrave, and was trying to use it along the Pomodoro technique (5mn break every 25mn). But the concept of 'natural breaks' of Workrave seems to interfere with what I'm trying to achieve. Workrave tries to guess that I'm doing a natural break if I stop using my mouse and keyboard for longer than 5s. It then stops the work timer, and start counting time as if I was doing my break. Here is a typical example : I've configured a 5mn rest break every 25mn. I start working. 10mn later, I receive a phone call, or start talking with a colleague, or any work-related action that do not need either keyboard nor mouse. Workrave then stops counting my time as work time, and starts its rest timer. If my phone call is shorter than 5mn, then Workrave will resume its timer where it stopped it. Meaning that my time on the phone is not counted as work time, and so my break time is pushed a few minutes later than it should be. Even worse, if my phone call is longer than 5mn, then Workrave count it as a complete rest break, and when I'll resume working, it will restart its timer completly. I'm looking for either a way to disable the natural breaks, or increase the 'inactivity time' from 5s to maybe ~1mn. Or maybe an other angle to look at the natural breaks that might work with the Pomodoro technique (forced 5mn breaks every 25mn). I'm using Ubuntu 11.10.

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  • Expire Files In A Folder: Delete Files After x Days

    - by Brett G
    I'm looking to make a "Drop Folder" in a windows shared drive that is accessible to everyone. I'd like files to be deleted automagically if they sit in the folder for more than X days. However, it seems like all methods I've found to do this, use the last modified date, last access time, or creation date of a file. I'm trying to make this a folder that a user can drop files in to share with somebody. If someone copies or moves files into here, I'd like the clock to start ticking at this point. However, the last modified date and creation date of a file will not be updated unless someone actually modifies the file. The last access time is updated too frequently... it seems that just opening a directory in windows explorer will update the last access time. Anyone know of a solution to this? I'm thinking that cataloging the hash of files on a daily basis and then expiring files based on hashes older than a certain date might be a solution.... but taking hashes of files can be time consuming. Any ideas would be greatly appreciated! Note: I've already looked at quite a lot of answers on here... looked into File Server Resource Monitor, powershell scripts, batch scripts, etc. They still use the last access time, last modified time or creation time... which, as described, do not fit the above needs.

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  • How can I make an AdvancedDataGrid re-display its labels when the results of the labelFunction chang

    - by Chris R
    I have an AdvancedDataGrid with a custom label function whose value can change based on other form status (specifically, there's a drop down to choose the time display format for some columns). Right now, I have this labelFunction: internal function formatColumnTime(item: Object, column: AdvancedDataGridColumn): String { var seconds: Number = item[column.dataField]; return timeFormat.selectedItem.labelFunction(seconds); } internal function formatTimeAsInterval(time: Number): String { if (isNaN(time)) return ""; var integerTime: int = Math.round(time); var seconds: int = integerTime % 60; integerTime = integerTime / 60; var minutes: int = integerTime % 60; var hours: int = integerTime / 60; return printf("%02d:%02d:%02d", hours, minutes, seconds); } internal function formatTimeAsFractions(time: Number): String { if (isNaN(time)) return ""; var hours: Number = time / 3600.0; return new String(Math.round(hours * 100) / 100); } ... and the timeFormat object is a combo box with items whose labelFunction attributes are formatTimeAsFractions and formatTimeAsInterval. The columns that have time formats have formatColumnTime as their labelFunction value, because extracting the seconds in that function and passing it in to the formatters made for a more testable app (IMHO). So, when the timeFormat.selectedItem value changes, I want to force my grid to re-calculate the labels of these colums. What method must I call on it? invalidateProperties() didn't work, so that's out.

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  • Search multiple datepicker on same grid

    - by DHF
    I'm using multiple datepicker on same grid and I face the problem to get a proper result. I used 3 datepicker in 1 grid. Only the first datepicker (Order Date)is able to output proper result while the other 2 datepicker (Start Date & End Date) are not able to generate proper result. There is no problem with the query, so could you find out what's going on here? Thanks in advance! php wrapper <?php ob_start(); require_once 'config.php'; // include the jqGrid Class require_once "php/jqGrid.php"; // include the PDO driver class require_once "php/jqGridPdo.php"; // include the datepicker require_once "php/jqCalendar.php"; // Connection to the server $conn = new PDO(DB_DSN,DB_USER,DB_PASSWORD); // Tell the db that we use utf-8 $conn->query("SET NAMES utf8"); // Create the jqGrid instance $grid = new jqGridRender($conn); // Write the SQL Query $grid->SelectCommand = "SELECT c.CompanyID, c.CompanyCode, c.CompanyName, c.Area, o.OrderCode, o.Date, m.maID ,m.System, m.Status, m.StartDate, m.EndDate, m.Type FROM company c, orders o, maintenance_agreement m WHERE c.CompanyID = o.CompanyID AND o.OrderID = m.OrderID "; // Set the table to where you update the data $grid->table = 'maintenance_agreement'; // set the ouput format to json $grid->dataType = 'json'; // Let the grid create the model $grid->setPrimaryKeyId('maID'); // Let the grid create the model $grid->setColModel(); // Set the url from where we obtain the data $grid->setUrl('grouping_ma_details.php'); // Set grid caption using the option caption $grid->setGridOptions(array( "sortable"=>true, "rownumbers"=>true, "caption"=>"Group by Maintenance Agreement", "rowNum"=>20, "height"=>'auto', "width"=>1300, "sortname"=>"maID", "hoverrows"=>true, "rowList"=>array(10,20,50), "footerrow"=>false, "userDataOnFooter"=>false, "grouping"=>true, "groupingView"=>array( "groupField" => array('CompanyName'), "groupColumnShow" => array(true), //show or hide area column "groupText" =>array('<b> Company Name: {0}</b>',), "groupDataSorted" => true, "groupSummary" => array(true) ) )); if(isset($_SESSION['login_admin'])) { $grid->addCol(array( "name"=>"Action", "formatter"=>"actions", "editable"=>false, "sortable"=>false, "resizable"=>false, "fixed"=>true, "width"=>60, "formatoptions"=>array("keys"=>true), "search"=>false ), "first"); } // Change some property of the field(s) $grid->setColProperty("CompanyID", array("label"=>"ID","hidden"=>true,"width"=>30,"editable"=>false,"editoptions"=>array("readonly"=>"readonly"))); $grid->setColProperty("CompanyName", array("label"=>"Company Name","hidden"=>true,"editable"=>false,"width"=>150,"align"=>"center","fixed"=>true)); $grid->setColProperty("CompanyCode", array("label"=>"Company Code","hidden"=>true,"width"=>50,"align"=>"center")); $grid->setColProperty("OrderCode", array("label"=>"Order Code","width"=>110,"editable"=>false,"align"=>"center","fixed"=>true)); $grid->setColProperty("maID", array("hidden"=>true)); $grid->setColProperty("System", array("width"=>150,"fixed"=>true,"align"=>"center")); $grid->setColProperty("Type", array("width"=>280,"fixed"=>true)); $grid->setColProperty("Status", array("width"=>70,"align"=>"center","edittype"=>"select","editoptions"=>array("value"=>"Yes:Yes;No:No"),"fixed"=>true)); $grid->setSelect('System', "SELECT DISTINCT System, System AS System FROM master_ma_system ORDER BY System", false, true, true, array(""=>"All")); $grid->setSelect('Type', "SELECT DISTINCT Type, Type AS Type FROM master_ma_type ORDER BY Type", false, true, true, array(""=>"All")); $grid->setColProperty("StartDate", array("label"=>"Start Date","width"=>120,"align"=>"center","fixed"=>true, "formatter"=>"date", "formatoptions"=>array("srcformat"=>"Y-m-d H:i:s","newformat"=>"d M Y") )); // this is only in this case since the orderdate is set as date time $grid->setUserTime("d M Y"); $grid->setUserDate("d M Y"); $grid->setDatepicker("StartDate",array("buttonOnly"=>false)); $grid->datearray = array('StartDate'); $grid->setColProperty("EndDate", array("label"=>"End Date","width"=>120,"align"=>"center","fixed"=>true, "formatter"=>"date", "formatoptions"=>array("srcformat"=>"Y-m-d H:i:s","newformat"=>"d M Y") )); // this is only in this case since the orderdate is set as date time $grid->setUserTime("d M Y"); $grid->setUserDate("d M Y"); $grid->setDatepicker("EndDate",array("buttonOnly"=>false)); $grid->datearray = array('EndDate'); $grid->setColProperty("Date", array("label"=>"Order Date","width"=>100,"editable"=>false,"align"=>"center","fixed"=>true, "formatter"=>"date", "formatoptions"=>array("srcformat"=>"Y-m-d H:i:s","newformat"=>"d M Y") )); // this is only in this case since the orderdate is set as date time $grid->setUserTime("d M Y"); $grid->setUserDate("d M Y"); $grid->setDatepicker("Date",array("buttonOnly"=>false)); $grid->datearray = array('Date'); // This command is executed after edit $maID = jqGridUtils::GetParam('maID'); $Status = jqGridUtils::GetParam('Status'); $StartDate = jqGridUtils::GetParam('StartDate'); $EndDate = jqGridUtils::GetParam('EndDate'); $Type = jqGridUtils::GetParam('Type'); // This command is executed immediatley after edit occur. $grid->setAfterCrudAction('edit', "UPDATE maintenance_agreement SET m.Status=?, m.StartDate=?, m.EndDate=?, m.Type=? WHERE m.maID=?", array($Status,$StartDate,$EndDate,$Type,$maID)); $selectorder = <<<ORDER function(rowid, selected) { if(rowid != null) { jQuery("#detail").jqGrid('setGridParam',{postData:{CompanyID:rowid}}); jQuery("#detail").trigger("reloadGrid"); // Enable CRUD buttons in navigator when a row is selected jQuery("#add_detail").removeClass("ui-state-disabled"); jQuery("#edit_detail").removeClass("ui-state-disabled"); jQuery("#del_detail").removeClass("ui-state-disabled"); } } ORDER; // We should clear the grid data on second grid on sorting, paging, etc. $cleargrid = <<<CLEAR function(rowid, selected) { // clear the grid data and footer data jQuery("#detail").jqGrid('clearGridData',true); // Disable CRUD buttons in navigator when a row is not selected jQuery("#add_detail").addClass("ui-state-disabled"); jQuery("#edit_detail").addClass("ui-state-disabled"); jQuery("#del_detail").addClass("ui-state-disabled"); } CLEAR; $grid->setGridEvent('onSelectRow', $selectorder); $grid->setGridEvent('onSortCol', $cleargrid); $grid->setGridEvent('onPaging', $cleargrid); $grid->setColProperty("Area", array("width"=>100,"hidden"=>false,"editable"=>false,"fixed"=>true)); $grid->setColProperty("HeadCount", array("label"=>"Head Count","align"=>"center", "width"=>100,"hidden"=>false,"fixed"=>true)); $grid->setSelect('Area', "SELECT DISTINCT AreaName, AreaName AS Area FROM master_area ORDER BY AreaName", false, true, true, array(""=>"All")); $grid->setSelect('CompanyName', "SELECT DISTINCT CompanyName, CompanyName AS CompanyName FROM company ORDER BY CompanyName", false, true, true, array(""=>"All")); $custom = <<<CUSTOM jQuery("#getselected").click(function(){ var selr = jQuery('#grid').jqGrid('getGridParam','selrow'); if(selr) { window.open('http://www.smartouch-cdms.com/order.php?CompanyID='+selr); } else alert("No selected row"); return false; }); CUSTOM; $grid->setJSCode($custom); // Enable toolbar searching $grid->toolbarfilter = true; $grid->setFilterOptions(array("stringResult"=>true,"searchOnEnter"=>false,"defaultSearch"=>"cn")); // Enable navigator $grid->navigator = true; // disable the delete operation programatically for that table $grid->del = false; // we need to write some custom code when we are in delete mode. // get the grid operation parameter to see if we are in delete mode // jqGrid sends the "oper" parameter to identify the needed action $deloper = $_POST['oper']; // det the company id $cid = $_POST['CompanyID']; // if the operation is del and the companyid is set if($deloper == 'del' && isset($cid) ) { // the two tables are linked via CompanyID, so let try to delete the records in both tables try { jqGridDB::beginTransaction($conn); $comp = jqGridDB::prepare($conn, "DELETE FROM company WHERE CompanyID= ?", array($cid)); $cont = jqGridDB::prepare($conn,"DELETE FROM contact WHERE CompanyID = ?", array($cid)); jqGridDB::execute($comp); jqGridDB::execute($cont); jqGridDB::commit($conn); } catch(Exception $e) { jqGridDB::rollBack($conn); echo $e->getMessage(); } } // Enable only deleting if(isset($_SESSION['login_admin'])) { $grid->setNavOptions('navigator', array("pdf"=>true, "excel"=>true,"add"=>false,"edit"=>true,"del"=>false,"view"=>true, "search"=>true)); } else $grid->setNavOptions('navigator', array("pdf"=>true, "excel"=>true,"add"=>false,"edit"=>false,"del"=>false,"view"=>true, "search"=>true)); // In order to enable the more complex search we should set multipleGroup option // Also we need show query roo $grid->setNavOptions('search', array( "multipleGroup"=>false, "showQuery"=>true )); // Set different filename $grid->exportfile = 'Company.xls'; // Close the dialog after editing $grid->setNavOptions('edit',array("closeAfterEdit"=>true,"editCaption"=>"Update Company","bSubmit"=>"Update","dataheight"=>"auto")); $grid->setNavOptions('add',array("closeAfterAdd"=>true,"addCaption"=>"Add New Company","bSubmit"=>"Update","dataheight"=>"auto")); $grid->setNavOptions('view',array("Caption"=>"View Company","dataheight"=>"auto","width"=>"1100")); ob_end_clean(); //solve TCPDF error // Enjoy $grid->renderGrid('#grid','#pager',true, null, null, true,true); $conn = null; ?> javascript code jQuery(document).ready(function ($) { jQuery('#grid').jqGrid({ "width": 1300, "hoverrows": true, "viewrecords": true, "jsonReader": { "repeatitems": false, "subgrid": { "repeatitems": false } }, "xmlReader": { "repeatitems": false, "subgrid": { "repeatitems": false } }, "gridview": true, "url": "session_ma_details.php", "editurl": "session_ma_details.php", "cellurl": "session_ma_details.php", "sortable": true, "rownumbers": true, "caption": "Group by Maintenance Agreement", "rowNum": 20, "height": "auto", "sortname": "maID", "rowList": [10, 20, 50], "footerrow": false, "userDataOnFooter": false, "grouping": true, "groupingView": { "groupField": ["CompanyName"], "groupColumnShow": [false], "groupText": ["<b> Company Name: {0}</b>"], "groupDataSorted": true, "groupSummary": [true] }, "onSelectRow": function (rowid, selected) { if (rowid != null) { jQuery("#detail").jqGrid('setGridParam', { postData: { CompanyID: rowid } }); jQuery("#detail").trigger("reloadGrid"); // Enable CRUD buttons in navigator when a row is selected jQuery("#add_detail").removeClass("ui-state-disabled"); jQuery("#edit_detail").removeClass("ui-state-disabled"); jQuery("#del_detail").removeClass("ui-state-disabled"); } }, "onSortCol": function (rowid, selected) { // clear the grid data and footer data jQuery("#detail").jqGrid('clearGridData', true); // Disable CRUD buttons in navigator when a row is not selected jQuery("#add_detail").addClass("ui-state-disabled"); jQuery("#edit_detail").addClass("ui-state-disabled"); jQuery("#del_detail").addClass("ui-state-disabled"); }, "onPaging": function (rowid, selected) { // clear the grid data and footer data jQuery("#detail").jqGrid('clearGridData', true); // Disable CRUD buttons in navigator when a row is not selected jQuery("#add_detail").addClass("ui-state-disabled"); jQuery("#edit_detail").addClass("ui-state-disabled"); jQuery("#del_detail").addClass("ui-state-disabled"); }, "datatype": "json", "colModel": [ { "name": "Action", "formatter": "actions", "editable": false, "sortable": false, "resizable": false, "fixed": true, "width": 60, "formatoptions": { "keys": true }, "search": false }, { "name": "CompanyID", "index": "CompanyID", "sorttype": "int", "label": "ID", "hidden": true, "width": 30, "editable": false, "editoptions": { "readonly": "readonly" } }, { "name": "CompanyCode", "index": "CompanyCode", "sorttype": "string", "label": "Company Code", "hidden": true, "width": 50, "align": "center", "editable": true }, { "name": "CompanyName", "index": "CompanyName", "sorttype": "string", "label": "Company Name", "hidden": true, "editable": false, "width": 150, "align": "center", "fixed": true, "edittype": "select", "editoptions": { "value": "Aquatex Industries:Aquatex Industries;Benithem Sdn Bhd:Benithem Sdn Bhd;Daily Bakery Sdn Bhd:Daily Bakery Sdn Bhd;Eurocor Asia Sdn Bhd:Eurocor Asia Sdn Bhd;Evergrown Technology:Evergrown Technology;Goldpar Precision:Goldpar Precision;MicroSun Technologies Asia:MicroSun Technologies Asia;NCI Industries Sdn Bhd:NCI Industries Sdn Bhd;PHHP Marketing:PHHP Marketing;Smart Touch Technology:Smart Touch Technology;THOSCO Treatech:THOSCO Treatech;YHL Trading (Johor) Sdn Bhd:YHL Trading (Johor) Sdn Bhd;Zenxin Agri-Organic Food:Zenxin Agri-Organic Food", "separator": ":", "delimiter": ";" }, "stype": "select", "searchoptions": { "value": ":All;Aquatex Industries:Aquatex Industries;Benithem Sdn Bhd:Benithem Sdn Bhd;Daily Bakery Sdn Bhd:Daily Bakery Sdn Bhd;Eurocor Asia Sdn Bhd:Eurocor Asia Sdn Bhd;Evergrown Technology:Evergrown Technology;Goldpar Precision:Goldpar Precision;MicroSun Technologies Asia:MicroSun Technologies Asia;NCI Industries Sdn Bhd:NCI Industries Sdn Bhd;PHHP Marketing:PHHP Marketing;Smart Touch Technology:Smart Touch Technology;THOSCO Treatech:THOSCO Treatech;YHL Trading (Johor) Sdn Bhd:YHL Trading (Johor) Sdn Bhd;Zenxin Agri-Organic Food:Zenxin Agri-Organic Food", "separator": ":", "delimiter": ";" } }, { "name": "Area", "index": "Area", "sorttype": "string", "width": 100, "hidden": true, "editable": false, "fixed": true, "edittype": "select", "editoptions": { "value": "Cemerlang:Cemerlang;Danga Bay:Danga Bay;Kulai:Kulai;Larkin:Larkin;Masai:Masai;Nusa Cemerlang:Nusa Cemerlang;Nusajaya:Nusajaya;Pasir Gudang:Pasir Gudang;Pekan Nenas:Pekan Nenas;Permas Jaya:Permas Jaya;Pontian:Pontian;Pulai:Pulai;Senai:Senai;Skudai:Skudai;Taman Gaya:Taman Gaya;Taman Johor Jaya:Taman Johor Jaya;Taman Molek:Taman Molek;Taman Pelangi:Taman Pelangi;Taman Sentosa:Taman Sentosa;Tebrau 4:Tebrau 4;Ulu Tiram:Ulu Tiram", "separator": ":", "delimiter": ";" }, "stype": "select", "searchoptions": { "value": ":All;Cemerlang:Cemerlang;Danga Bay:Danga Bay;Kulai:Kulai;Larkin:Larkin;Masai:Masai;Nusa Cemerlang:Nusa Cemerlang;Nusajaya:Nusajaya;Pasir Gudang:Pasir Gudang;Pekan Nenas:Pekan Nenas;Permas Jaya:Permas Jaya;Pontian:Pontian;Pulai:Pulai;Senai:Senai;Skudai:Skudai;Taman Gaya:Taman Gaya;Taman Johor Jaya:Taman Johor Jaya;Taman Molek:Taman Molek;Taman Pelangi:Taman Pelangi;Taman Sentosa:Taman Sentosa;Tebrau 4:Tebrau 4;Ulu Tiram:Ulu Tiram", "separator": ":", "delimiter": ";" } }, { "name": "OrderCode", "index": "OrderCode", "sorttype": "string", "label": "Order No.", "width": 110, "editable": false, "align": "center", "fixed": true }, { "name": "Date", "index": "Date", "sorttype": "date", "label": "Order Date", "width": 100, "editable": false, "align": "center", "fixed": true, "formatter": "date", "formatoptions": { "srcformat": "Y-m-d H:i:s", "newformat": "d M Y" }, "editoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } }, "searchoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } } }, { "name": "maID", "index": "maID", "sorttype": "int", "key": true, "hidden": true, "editable": true }, { "name": "System", "index": "System", "sorttype": "string", "width": 150, "fixed": true, "align": "center", "edittype": "select", "editoptions": { "value": "Payroll:Payroll;TMS:TMS;TMS & Payroll:TMS & Payroll", "separator": ":", "delimiter": ";" }, "stype": "select", "searchoptions": { "value": ":All;Payroll:Payroll;TMS:TMS;TMS & Payroll:TMS & Payroll", "separator": ":", "delimiter": ";" }, "editable": true }, { "name": "Status", "index": "Status", "sorttype": "string", "width": 70, "align": "center", "edittype": "select", "editoptions": { "value": "Yes:Yes;No:No" }, "fixed": true, "editable": true }, { "name": "StartDate", "index": "StartDate", "sorttype": "date", "label": "Start Date", "width": 120, "align": "center", "fixed": true, "formatter": "date", "formatoptions": { "srcformat": "Y-m-d H:i:s", "newformat": "d M Y" }, "editoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } }, "searchoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } }, "editable": true }, { "name": "EndDate", "index": "EndDate", "sorttype": "date", "label": "End Date", "width": 120, "align": "center", "fixed": true, "formatter": "date", "formatoptions": { "srcformat": "Y-m-d H:i:s", "newformat": "d M Y" }, "editoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } }, "searchoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } }, "editable": true }, { "name": "Type", "index": "Type", "sorttype": "string", "width": 530, "fixed": true, "edittype": "select", "editoptions": { "value": "Comprehensive MA:Comprehensive MA;FOC service, 20% spare part discount:FOC service, 20% spare part discount;Standard Package, FOC 1 time service, 20% spare part discount:Standard Package, FOC 1 time service, 20% spare part discount;Standard Package, FOC 2 time service, 20% spare part discount:Standard Package, FOC 2 time service, 20% spare part discount;Standard Package, FOC 3 time service, 20% spare part discount:Standard Package, FOC 3 time service, 20% spare part discount;Standard Package, FOC 4 time service, 20% spare part discount:Standard Package, FOC 4 time service, 20% spare part discount;Standard Package, FOC 6 time service, 20% spare part discount:Standard Package, FOC 6 time service, 20% spare part discount;Standard Package, no free:Standard Package, no free", "separator": ":", "delimiter": ";" }, "stype": "select", "searchoptions": { "value": ":All;Comprehensive MA:Comprehensive MA;FOC service, 20% spare part discount:FOC service, 20% spare part discount;Standard Package, FOC 1 time service, 20% spare part discount:Standard Package, FOC 1 time service, 20% spare part discount;Standard Package, FOC 2 time service, 20% spare part discount:Standard Package, FOC 2 time service, 20% spare part discount;Standard Package, FOC 3 time service, 20% spare part discount:Standard Package, FOC 3 time service, 20% spare part discount;Standard Package, FOC 4 time service, 20% spare part discount:Standard Package, FOC 4 time service, 20% spare part discount;Standard Package, FOC 6 time service, 20% spare part discount:Standard Package, FOC 6 time service, 20% spare part discount;Standard Package, no free:Standard Package, no free", "separator": ":", "delimiter": ";" }, "editable": true } ], "postData": { "oper": "grid" }, "prmNames": { "page": "page", "rows": "rows", "sort": "sidx", "order": "sord", "search": "_search", "nd": "nd", "id": "maID", "filter": "filters", "searchField": "searchField", "searchOper": "searchOper", "searchString": "searchString", "oper": "oper", "query": "grid", "addoper": "add", "editoper": "edit", "deloper": "del", "excel": "excel", "subgrid": "subgrid", "totalrows": "totalrows", "autocomplete": "autocmpl" }, "loadError": function(xhr, status, err) { try { jQuery.jgrid.info_dialog(jQuery.jgrid.errors.errcap, '<div class="ui-state-error">' + xhr.responseText + '</div>', jQuery.jgrid.edit.bClose, { buttonalign: 'right' } ); } catch(e) { alert(xhr.responseText); } }, "pager": "#pager" }); jQuery('#grid').jqGrid('navGrid', '#pager', { "edit": true, "add": false, "del": false, "search": true, "refresh": true, "view": true, "excel": true, "pdf": true, "csv": false, "columns": false }, { "drag": true, "resize": true, "closeOnEscape": true, "dataheight": "auto", "errorTextFormat": function (r) { return r.responseText; }, "closeAfterEdit": true, "editCaption": "Update Company", "bSubmit": "Update" }, { "drag": true, "resize": true, "closeOnEscape": true, "dataheight": "auto", "errorTextFormat": function (r) { return r.responseText; }, "closeAfterAdd": true, "addCaption": "Add New Company", "bSubmit": "Update" }, { "errorTextFormat": function (r) { return r.responseText; } }, { "drag": true, "closeAfterSearch": true, "multipleSearch": true }, { "drag": true, "resize": true, "closeOnEscape": true, "dataheight": "auto", "Caption": "View Company", "width": "1100" } ); jQuery('#grid').jqGrid('navButtonAdd', '#pager', { id: 'pager_excel', caption: '', title: 'Export To Excel', onClickButton: function (e) { try { jQuery("#grid").jqGrid('excelExport', { tag: 'excel', url: 'session_ma_details.php' }); } catch (e) { window.location = 'session_ma_details.php?oper=excel'; } }, buttonicon: 'ui-icon-newwin' }); jQuery('#grid').jqGrid('navButtonAdd', '#pager', { id: 'pager_pdf', caption: '', title: 'Export To Pdf', onClickButton: function (e) { try { jQuery("#grid").jqGrid('excelExport', { tag: 'pdf', url: 'session_ma_details.php' }); } catch (e) { window.location = 'session_ma_details.php?oper=pdf'; } }, buttonicon: 'ui-icon-print' }); jQuery('#grid').jqGrid('filterToolbar', { "stringResult": true, "searchOnEnter": false, "defaultSearch": "cn" }); jQuery("#getselected").click(function () { var selr = jQuery('#grid').jqGrid('getGridParam', 'selrow'); if (selr) { window.open('http://www.smartouch-cdms.com/order.php?CompanyID=' + selr); } else alert("No selected row"); return false; }); });

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