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  • How to setup a site that works in a home network

    - by Nrew
    Please help, I don't have any idea on how to host a webpage on a home network. Its just in a home network with 3-4 computers connected with a hub. And I've already installed wampserver on the computer that will host the pages. Im using windows 7 ultimate(the 2 computers)

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  • Foreach PHP Error

    - by Logan
    I am receiving the following foreach error on my PHP file and I have no idea how to fix it. Does anyone have any ideas? When I load the page I get this: Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 61 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Warning: Invalid argument supplied for foreach() in /home/mysite/public_html/merge/class/global_functions.php on line 89 Line 61 and 89 of my /class/global_functions.php are as followed: Here is my code from line 61 to line 98: foreach($GLOBALS['userpermbit'] as $v) { if(strstr($v['perm'],'|'.$pageperm_id[0]['id'].'|')) return true; } //if they dont have perms and we're not externally including functions return false if ($GLOBALS['external'] != true) return false; return true; } //FUNCTION: quick perm check using perm info from the onload perm check function stealthPermCheck($req) { #if theyre an admin give them perms if(@in_array($GLOBALS['user'][0]['id'], $GLOBALS['superAdmins'])) return true; if(!is_numeric($req)) { #if the req is numeric we need to match a title, not a permid. So try to do that foreach($GLOBALS['userpermbit'] as $v) { if(stristr($v['title'],$req)) return true; } }else{ #check if they have perms numerically if so return true foreach($GLOBALS['userpermbit'] as $v) { if(strstr($v['perm'],'|'.$req.'|')) return true; } } #if none of this returned true they dont have perms, return false return false; }

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  • Suggestions on the best home server rack cabinet

    - by allentown
    I have a lot of gear in a colocation facility right now. Some of it is going to come home with me now. I do not know anything about the "rack mount" side of the industry. I lease a rack, and I put my stuff in it. I have a few 1U boxes, a few 2U boxes, and a few 4U boxes. 1U switch. One is a new Xserve, which means it is deep. I think I can get by with around 12U to 18U. I want to keep it as small as possible, since I do not have a lot of spare space at my home. I will not be able to bolt to the wall, floor etc, so it should not be tall. This is something I would love to more or less just be a box that sits on the floor but gives me the ability to mount nicely, do nice cable management etc. Are the "post" style racks junk? I am liking the open space, and the no limitations on depth of something like this: http://www.rackmountsolutions.net/images/products/Martin-relay-rack.jpg However, that thing is way too tall, and probably way too expensive. I am looking to be around $300.00 or less. More if I have to, though I would prefer not to. These look near perfect: (See comment for this link, the system will not let me post a second url) but I am worried the Xserve will not fit in it. If anyone has any good links, or website recommendations of good past experience, I would appreciate it. I am almost considering that I may be able to build something with random scraps of stuff at Home Depot as well.

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  • Setting Up My Home Network

    - by Skizz
    I currently have five PCs at home, three running WinXP and two running Ubuntu. They are set up like this: ISP ----- Modem ---- Switch ---- Ubuntu1 -- B&W Printer | |--WinXP1 | |--WinXP2 Wireless |--Colour Printer | |---------Ubuntu2 |---------WinXP3 (laptop) The Ubuntu1 machine is set up as a PDC using Samba and runs fetchmail, procmail, dovecot to get my e-mail and allow me to access the e-mail via imap so I can read the e-mail on any PC. I'd like to set up the network like this: ISP ----- Modem ---- Ubuntu1 ---- Switch ------WinXP1 | | |--WinXP2 B&W Printer Wireless |--Colour Printer | |---------Ubuntu2 |---------WinXP3 (laptop) My questions are: How to configure Ubuntu1 to act as a firewall. How to configure Ubuntu1 to provide a consistant user authentication across the network, at the moment Samba provides roaming profiles for the XP machines but the Ubuntu2 machine has it's own user lists. I'd like to have a single authentication for both XP machines and linux machines so that users added to the server list will propagate to all PCs (i.e. new users can log on using any PC without modifying any of the client PCs). How to configure a linux client (Ubuntu2 above) to access files on the server (Ubuntu1), some of which are in user specific folders, effectively sharing /home/{user} per user (read and write access) and stuff like /home/media/photos with read access for everyone and limited write access. How to configure the XP machines (if it is different from a the Samba method). How to set up e-mail filtering. I'd like to have a whitelist/blacklist system for incoming e-mails for some of the e-mail accounts (mainly, my kids' accounts) with filtered e-mails being put into quaranteen until a sysadmin either adds the sender to a blacklist or whitelist. OK, that's a lot of stuff. For now, I don't want config files*, rather, what services / applications to use and how they interact. For example, LDAP could be used for authentication but what else would be useful to make the administration of the LDAP easier. Once I have a general idea for the overall configuration, I can ask other questions about the specifics. Skizz I have looked around for information, but most answers are usually in the form of abstract config files and lists of packages to install.

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  • Copying windows home server backup offsite

    - by Simon
    What ways are there to copy a windows home server backup to an offsite location? I'm talking specifically (and only) about the automated backup of my entire machine, and not the shared network folders. I am 90% working away from home on my laptop which has a 640GB drive so the shared folders are essentially useless to me. I backup every night, but if my house burns down or broken into the I'm in serious serious trouble ! I'm really looking for some alternative way to back up my entire machine - which much not interfere with the reliability or speed by which my WHS backs up my laptop every night. Either a way to 'export' a complete machine backup from the server, or recommendations on non-conflicting software I can backup to a 1TB drive at work are what I'm looking for. Note: I believe that WHS uses its own completely proprietary backup and doesn't use things like any 'backup bit' or 'archive bit'. I just dont want to install some other backup software that will conflict. PS I'm now running Windows 7 and just realized that I should probably check out the backup functionality it gives me. I assume that won't conflict right! Edit: Thanks for the hosted solutions. I'd also appreciate ways to backup to an 'offsite' location that I control - like my office vs. my home. The hosted solutions I think will be too slow or expensive for my needs.

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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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  • Looking for home networking hardware and software advice

    - by phobos7
    Note: I originally wrote this up in a blog post. I've removed any affiliate links that I put in my original post to ensure I don't annoy anybody. I've recently moved home and I now need to go to the trouble of sorting out my home network yet again. We had Virgin broadband in Hertford but you can't get Virgin in the street we've moved to so I've had to go with O2 Broadband. Normally I prefer to use my own hardward, and previously used the DLink DIR-655 router which was great, but in this situation I am using the O2 Wirelss Box III since I only have an old Netgear DG834PN Wireless G modem router and I'd rather be using Wireless N. Anyway, the place we have moved into has only one phone point in the hallway, has the best TV point in one room and the best place to put the TV and other entertainment stuff in yet another room. So, networking the house up for Internet and TV is required. The diagram below shows the things that I'll have in my home network but there are three points where I'm not quite sure what hardware to us. Wireless Access Point/Bridge, that acts only as a wireless to wire bridge and not an AP, that links up a Media Centre/PC and a couple of consoles to the network. I'm pretty much settled on us an Acer Aspire Revo R3600 as my media PC, probably with Ubuntu or Windows and XBMC installed. Wireless Access Point/Bridge, that acts only as a wireless to wire bridge and not an AP, that links up a device that can decode and stream TV from a TV aerial across the network. The device that is connected to 2). At the moment I'm considering a HDHomeRun by SiliconDust. At the moment I'm considering either the TP LINK TL-WA701ND 150Mbps Wireless Lite N Access Point (very cheap at Amazon) or the Netgear 5 GHz Wireless-N HD Access Point/Bridge. I'd love to get some insight into what you would do in my situation. What Wireless Access Point/Bridge should I put at points 1) and 2)? What device should I choose for point 3) that can decode and stream a TV signal? Is the Acer Aspire Revo R3600 a good choice? ![alt text][6] Note 2: I've also posted this question on AVForums.

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  • How to change local user home folder on Windows 2000 and above

    - by Adi Roiban
    I was using a local account on a Windows 7 desktop that is not connected to any Active Directory. After a while it was required to rename the local account. Renaming the account was simple using Local users and groups management tool. After renaming the user, the user home folder was not renamed and I could not find any information about how to change user home folder. I found the ProfileList registry folder but maybe there is a command line for doing such changes. HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\ProfileList Any help is much appreciated. Thanks!

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  • How to change local user home folder on Windows 2000 and above

    - by Adi Roiban
    I was using a local account on a Windows 7 desktop that is not connected to any Active Directory. After a while it was required to rename the local account. Renaming the account was simple using Local users and groups management tool. After renaming the user, the user home folder was not renamed and I could not find any information about how to change user home folder. I found the ProfileList registry folder but maybe there is a command line for doing such changes. HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\ProfileList Any help is much appreciated. Thanks!

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  • What is a usable throughput for a home media server

    - by Craig
    I am looking to setup a home server that will act as a media server. This will include both video (possibly HD) and audio. The clients will be a fun mix of hardware but that is a different question. What I want to know is what is the minimum throughput for streaming video without hitches? Is there a "sweet" spot for throughput (price vs. throughput)? I am determining my budget for this "upgrade" and I need to evaluate wether or not upgrading to a 1 Gbps home LAN is required. Sure, it would be sweet and easily handle the traffic but I don't want to do it unless it is necesary.

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  • Finding the A Record for a Home Server [closed]

    - by Ryan Allred
    I have a hosted website that allows me to add subdomains and point them to different locations on the server. I also know that I can change the 'A Record' to point it to a different IP address. Now, here's the question, I have a home server that I need to access though this hosted website's domain name. How would I go about setting up the 'A Record' on the server to point to my home server. I'm usually pretty good about keyword searches but on this one, I'm pretty lost as to what to search for.

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  • I can't get router and switches configured properly for my home office network

    - by BernicusMaximus
    Networking Gurus, I recently built a new detached garage, with an office above. As such I had it tied into my existing home ethernet wiring. The ethernet signal is coming into the garage just fine, but I can not get my network configured the way I want because of problems trying to link the various router/switch devices. Please see the following links for the network diagrams: Home Network So basically, I can't my future state to work. I'm not sure if I'm using incompatible switches or what, but I tried the future state with some 4 port switches from best buy and had no luck. I resorted to setting up the Current State so I could operate. What I am looking for is help on how best to get my future state to work. Is this possible with my current configuration, and if not, what should I do? Any help is appreciated. Thanks, Bernie

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  • How low-power can a home server get?

    - by Halik
    I've got quite simple question actually. How green, low-power and efficient x86 home server can I build using consumer parts with rather constrained budget. After looking through some Google hits I've found out that system based on dual-core atom, some modest mITX board (gigabit lan, integrated audio and gfx etc), one RAM module and one 'green' WD HDD, powered by picoITX PSU uses about 30W at idle up to 40 at load. Can you get lower (or how much lower) then that? Maybe some VIA nano chips, or single core atom? My home server would take care of some back-upping mixed with little ftp/http traffic.

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  • How to Automatically Create Home Directory for Active Directory User on Solaris (using PowerBroker)

    - by neildeadman
    I have a number of existing users in Active Directory that need a home directory created. They don't log directly in to Solaris but into a service running on that box. If I login as them their home directory gets created and then they can login. This is the same for new users too! As there are a lot of users, I need a way to automate this so new users and existing users have it created automatically. Is this possible??

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  • Use one home directory for more than one operating system

    - by Just Jake
    I want to configure the same user account across multiple operating systems. Right now, I'm set up for general use in Mac OS 10.6.6 "Snow Leopard," and I have about 200gb of files in my home directory (/Users/justjake/). I want to use this user (and home directory) for other operating systems on other partitions. For example, I have Mac OS 10.5 installed on a 12gb partition. How can I share permissions, user accounts across my two operating systems? Would moving the my /Users directory from 10.6 to it's own partition then mounting it using /etc/fstab solve my issue?

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  • Home Server restore fails cannot find boot device

    - by Tim Heuer
    I am using Windows Home Server to backup my PCs. I recently had a hard drive failure on one of my WHS connected PCs and obtained an identical sized/speed drive for my laptop. I used the latest home server restore CD and did the restore. It said it completed successfully. Upon reboot, it says 'cannot find boot device' and lists all my drives (hard drive, cd, network book) indicating no valid operating system was found. I boot using the Win7 repair disk and while it doesn't see the operating system, it sees the drive and if I go into a command prompt, I can see all my data on the drive. My laptop is Windows 7 64-bit Ultimate. I've tried most everything I can think of. I'm a technical user (software developer) so I'm pretty aware of how things work (or should). I don't feel like I'm missing a simple step here.

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  • Speed up file access on home network

    - by kurasa
    I have 2 PCs (Windows 7 Ultimate) and a Mac running Windows 7 using vmware fusion on my home network tied together using WRN1000 NETGEAR Router On one of the PC's I have a set of file (MYOB .myo). These use a data source to access the data in the files. Operations (reading,writing) to the .myo on the PC which hosts the files is fine but the other 2 it is painfully slow/unreliable and I am wondering what I can do to speed this up. Some ideas I have are 1. Turn off the Windows firewall on all the windows installations on the home network 2. Buy another router. Specifically a router which I can connect a USB flash drive on the back where I can put the .myo files and all the PC can access the files from the USB flash drive on the router (does this speed things up?) Any advice greatly appreciated on how I can speed up this access to data

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  • MacOS X 10.6 Portable Home Directory sync fails due to FileSync agent crashing

    - by tegbains
    On one of our cleanly installed MacPro machines running MacOS X 10.6.6 connected to our MacOS X 10.6.6 Server, syncing data using Portable Home Directories fails. It seems to be due to the filesync agent crashing during the home sync. We get -41 and -8026 errors, which we are suspecting are indicating that there is too much data or filesync agent can't read the files. The user is the owner of the files and can read/write to all of the files. < Logout 0:: [11/02/04 13:10:42.751] Error -41 copying /Volumes/RCAUsers/earlpeng/Library/Mail/Mailboxes/email from old imac./Attachments/12081/2.2. (source = NO) < Logout 0:: [11/02/04 13:10:42.758] Error -8062 copying /Volumes/RCAUsers/earlpeng/Library/Mail/Mailboxes/email from old imac./Attachments/12081/2.2/[email protected]. (source = NO) < Logout 1:: [11/02/04 13:10:42.758] -[DeepCopyContext deepCopyError:sourceError:sourceRef:]: error = -8062, wasSource = NO: return shouldContinue = NO

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  • Setting up Raid 1 Array for Home Server

    - by user1048116
    I'm not sure if this is even possible, but it's worth asking on here! Essentially I have a old machine at home (well, not old hardware wise, but I recently built a new gaming rig), which I decided to install a copy of W2008 R2 on and use as a file/backup server and media center'ish machine. As of now, it has a single drive partitioned into C and D, with D being the Data partition. I have happened to find an old 1TB SATA drive lying around at home, and was wondering if it's possible to setup a Raid 1 array in my rig within Windows without needing to lose everything on my first drive (or maybe even just mirror a specific partition, say the Data partition, as this is just what stores my photos etc). Maybe this isn't possible, but you never know :) Regards, T.C

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  • Windows Home Server is showing signs of death

    - by Guy
    I have a Windows Home Server (HP EX485 MediaSmart Home Server) and it started acting up about 4 weeks ago and a few days ago complained of a corrupt database and would I like to try and recover it? Yes, I would but I ended up losing all backups. I have to reboot it frequently for the client machines to be able to see it. I have 4 hard disks in the computer. I suspect that the primary hard disk is going bad. My first question: How can I confirm if it's going bad or not? I'm thinking about removing the primary disk and replacing it with one of the others and reloading the OS with the server restore disk. I know that I'll lose everything (but I recently did anyway) but is there any other reason why I should not do this?

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