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  • Faster Memory Allocation Using vmtasks

    - by Steve Sistare
    You may have noticed a new system process called "vmtasks" on Solaris 11 systems: % pgrep vmtasks 8 % prstat -p 8 PID USERNAME SIZE RSS STATE PRI NICE TIME CPU PROCESS/NLWP 8 root 0K 0K sleep 99 -20 9:10:59 0.0% vmtasks/32 What is vmtasks, and why should you care? In a nutshell, vmtasks accelerates creation, locking, and destruction of pages in shared memory segments. This is particularly helpful for locked memory, as creating a page of physical memory is much more expensive than creating a page of virtual memory. For example, an ISM segment (shmflag & SHM_SHARE_MMU) is locked in memory on the first shmat() call, and a DISM segment (shmflg & SHM_PAGEABLE) is locked using mlock() or memcntl(). Segment operations such as creation and locking are typically single threaded, performed by the thread making the system call. In many applications, the size of a shared memory segment is a large fraction of total physical memory, and the single-threaded initialization is a scalability bottleneck which increases application startup time. To break the bottleneck, we apply parallel processing, harnessing the power of the additional CPUs that are always present on modern platforms. For sufficiently large segments, as many of 16 threads of vmtasks are employed to assist an application thread during creation, locking, and destruction operations. The segment is implicitly divided at page boundaries, and each thread is given a chunk of pages to process. The per-page processing time can vary, so for dynamic load balancing, the number of chunks is greater than the number of threads, and threads grab chunks dynamically as they finish their work. Because the threads modify a single application address space in compressed time interval, contention on locks protecting VM data structures locks was a problem, and we had to re-scale a number of VM locks to get good parallel efficiency. The vmtasks process has 1 thread per CPU and may accelerate multiple segment operations simultaneously, but each operation gets at most 16 helper threads to avoid monopolizing CPU resources. We may reconsider this limit in the future. Acceleration using vmtasks is enabled out of the box, with no tuning required, and works for all Solaris platform architectures (SPARC sun4u, SPARC sun4v, x86). The following tables show the time to create + lock + destroy a large segment, normalized as milliseconds per gigabyte, before and after the introduction of vmtasks: ISM system ncpu before after speedup ------ ---- ------ ----- ------- x4600 32 1386 245 6X X7560 64 1016 153 7X M9000 512 1196 206 6X T5240 128 2506 234 11X T4-2 128 1197 107 11x DISM system ncpu before after speedup ------ ---- ------ ----- ------- x4600 32 1582 265 6X X7560 64 1116 158 7X M9000 512 1165 152 8X T5240 128 2796 198 14X (I am missing the data for T4 DISM, for no good reason; it works fine). The following table separates the creation and destruction times: ISM, T4-2 before after ------ ----- create 702 64 destroy 495 43 To put this in perspective, consider creating a 512 GB ISM segment on T4-2. Creating the segment would take 6 minutes with the old code, and only 33 seconds with the new. If this is your Oracle SGA, you save over 5 minutes when starting the database, and you also save when shutting it down prior to a restart. Those minutes go directly to your bottom line for service availability.

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  • Is the Leptonica implementation of 'Modified Median Cut' not using the median at all?

    - by TheCodeJunkie
    I'm playing around a bit with image processing and decided to read up on how color quantization worked and after a bit of reading I found the Modified Median Cut Quantization algorithm. I've been reading the code of the C implementation in Leptonica library and came across something I thought was a bit odd. Now I want to stress that I am far from an expert in this area, not am I a math-head, so I am predicting that this all comes down to me not understanding all of it and not that the implementation of the algorithm is wrong at all. The algorithm states that the vbox should be split along the lagest axis and that it should be split using the following logic The largest axis is divided by locating the bin with the median pixel (by population), selecting the longer side, and dividing in the center of that side. We could have simply put the bin with the median pixel in the shorter side, but in the early stages of subdivision, this tends to put low density clusters (that are not considered in the subdivision) in the same vbox as part of a high density cluster that will outvote it in median vbox color, even with future median-based subdivisions. The algorithm used here is particularly important in early subdivisions, and 3is useful for giving visible but low population color clusters their own vbox. This has little effect on the subdivision of high density clusters, which ultimately will have roughly equal population in their vboxes. For the sake of the argument, let's assume that we have a vbox that we are in the process of splitting and that the red axis is the largest. In the Leptonica algorithm, on line 01297, the code appears to do the following Iterate over all the possible green and blue variations of the red color For each iteration it adds to the total number of pixels (population) it's found along the red axis For each red color it sum up the population of the current red and the previous ones, thus storing an accumulated value, for each red note: when I say 'red' I mean each point along the axis that is covered by the iteration, the actual color may not be red but contains a certain amount of red So for the sake of illustration, assume we have 9 "bins" along the red axis and that they have the following populations 4 8 20 16 1 9 12 8 8 After the iteration of all red bins, the partialsum array will contain the following count for the bins mentioned above 4 12 32 48 49 58 70 78 86 And total would have a value of 86 Once that's done it's time to perform the actual median cut and for the red axis this is performed on line 01346 It iterates over bins and check they accumulated sum. And here's the part that throws me of from the description of the algorithm. It looks for the first bin that has a value that is greater than total/2 Wouldn't total/2 mean that it is looking for a bin that has a value that is greater than the average value and not the median ? The median for the above bins would be 49 The use of 43 or 49 could potentially have a huge impact on how the boxes are split, even though the algorithm then proceeds by moving to the center of the larger side of where the matched value was.. Another thing that puzzles me a bit is that the paper specified that the bin with the median value should be located, but does not mention how to proceed if there are an even number of bins.. the median would be the result of (a+b)/2 and it's not guaranteed that any of the bins contains that population count. So this is what makes me thing that there are some approximations going on that are negligible because of how the split actually takes part at the center of the larger side of the selected bin. Sorry if it got a bit long winded, but I wanted to be as thoroughas I could because it's been driving me nuts for a couple of days now ;)

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  • Random servers in Citrix servers suddenly bluescreens (mostly 0x0000008e and 0x0000007e)

    - by Rasmus Rask
    I'm responsible for a Citrix Presentation Server 4.5 farm. Starting Friday 30. November, my servers started to crash randomly. So far we've experienced 80 crashes, so it's obviously becoming an increasingly big problem for us. I have 12+ years experience with IT, so I know the difference between 0 and 1, but I have a hard time cracking this. We've rolled back any recent changes I can think of for different groups of servers, but all groups still seem to crash. I don't have the skills to interpret the memory dumps to find the culprit. Has anyone encountered the same or a similar problem? - might be a generic Windows issue Other than executing "analyze -v" in WinDbg, how do I work my way through the memory dumps to see what actually triggered the BSOD? Any suggested steps in getting to the bottom of this? Any help is greatly appreciated. I can also provide links to kernel memory dumps or WinDbg output if necessary. Thanks! Problem description The majority of the STOP errors we encounter are: 0x0000008e KERNEL_MODE_EXCEPTION_NOT_HANDLED (50%) 0x0000007e SYSTEM_THREAD_EXCEPTION_NOT_HANDLED (26%) 0x00000050 PAGE_FAULT_IN_NONPAGED_AREA (21%) We also see a few 0x0000000a IRQL_NOT_LESS_OR_EQUAL (3%). For both 0x0000008e and 0x0000007e bug checks, the exception code is 0xc0000005 (Access Violation). When opening dump files in WinDbg, most details are exactly the same, for all the 0x0000008e and 0x0000007e bug checks respectively: 0x0000008e Exception address: 0x808bc9e3 Trap frame: [varies] FAILURE_BUCKET_ID: 0x8E_nt!HvpGetCellMapped+97 Probably Caused by (IMAGE_NAME): ntkrpamp.exe 0x0000007e Exception address: 0x808369b6 Exception record address: 0xf70d3be0 Context record address: 0xf70d38dc FAILURE_BUCKET_ID: 0x7E_nt!MmPurgeSection+14 Probably Caused by: memory_corruption About 30% of the crashes happens between 17:00 and 19:00, which leads me to believe this tend to happen more often during logoffs. But then again, only ~15% occurs between 15:00 and 17:00. Summary of farm Citrix Presentation Server 4.5 R06 on Windows Server 2003 R2 SP2 All high priority patches, at least as of October installed Virtualized using VMWare ESX/vSphere 4.1 on HP Proliant BL460c G6 blade servers About 53 Presentation Servers in production, divided into three silos - only one of which, the largest, is affected 2 vCPU's (5 GHz reserved), 8 GB RAM (all reserved) for each Presentation Server Plenty of free disk space Very few printer drivers - automated deletion of non-approved drivers every night ~1.000 peak concurrent users, which is reached at around 10:30 (on weekdays) Number of sessions steadily decline between 15:00 and 19:00 to ~230

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  • Revolutionary brand powder packing machine price from affecting marketplace boom and put on uniform in addition to a lengthy service life

    - by user74606
    In mining in stone crushing, our machinery company's encounter becomes much more apparent. As a consequence of production capacity in between 600~800t/h of mining stone crusher, stone is mine Mobile Cone Crushing Plant Price 25~40 times, effectively solved the initially mining stone crusher operation because of low yield prices, no upkeep problems. Full chunk of mining stone crusher. Maximum particle size for crushing 1000x1200mm, an effective answer for the original side is mine stone provide, storing significant chunks of stone can not use complications in mines. Completed goods granularity is modest, only 2~15mm, an effective option for the original mine stone size, generally blocking chute production was an issue even the grinding machine. Two types of material mixed great uniformity, desulfurization of mining stone by adding weight considerably. Present quantity added is often reached 60%, effectively minimizing the cost of raw supplies. Electrical energy consumption has fallen. Dropped 1~2KWh/t tons of mining stone electrical energy consumption, annual electricity savings of one hundred,000 yuan. Efficient labor intensity of workers and also the atmosphere. Due to mine stone powder packing machine price a high degree of automation, with out human make contact with supplies, workers working circumstances enhanced significantly. Positive aspects, and along with mine for stone crushing, CS series cone Crusher has the following efficiency traits. CS series cone Crusher Chamber is divided into 3 unique designs, the user is usually chosen in accordance with the scenario on site crushing efficiency is high, uniform item size, grain shape, rolling mortar wall friction and put on uniform in addition to a extended service life of crushing cavity-. CS series cone Crusher utilizes a one of a kind dust-proof seal, sealing dependable, properly extend the service life of the lubricant replacement cycle and parts. CS series Sprial Sand washer price manufacture of important components to choose unique materials. Each and every stroke left rolling mortar wall of broken cone distances, by permitting a lot more products into the crushing cavity, as well as the formation of big discharge volume, speed of supplies by way of the crushing Chamber. This machine makes use of the principle of crushing cavity, also as unique laminated crushing, particle fragmentation, so that the completed product drastically improved the proportions of a cube, needle-shaped stones to lower particle levels extra evenly.

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  • Pushing Large Files to 500+ Computers [closed]

    - by WMIF
    I work with a team to manage 500-600 rented Windows 7 computers for an annual conference. We have a large amount of data that needs to be synced to these computers, up to 1 TiB. The computers are divided into rooms and connected through unmanaged gigabit switches. We prepare these computers ahead of time with the Windows installation and configuration, plus any files that we have available to us before we send the base image in for replication by the rental company. Every year, we have presenters approach on site with up to gigs of data that need to be pushed to the room that they will be presenting in. Sometimes they only have a few files that are small sizes, such as a slide PDF, but can sometimes be much larger 5 GiB. Our current strategy for pushing these files is using batch scripts and RoboCopy. For the large pushes, we actually use a BitTorrent client to generate a torrent file, and then we use the batch-RoboCopy to push the torrent into a folder on the remote machines that is being monitored by an installed BT client. Often times, this data needs to be pushed immediately with a small time window. We have several machines in a control room that are identical to the machines on the floor that we use for these pushes. We occasionally have a need to execute a program on the remote machines, and we currently use batch and PSexec to handle this task. We would love to be able to respond to these last minute pushes with "sorry, your own fault", but it won't happen. The BT method has allowed us to have a much faster response time, but the whole batch process can get messy when there are multiple jobs being pushed. We use Enterprise Ghost for other processes, and it doesn't work well in this large of scale, plus it is really quite expensive for a once-a-year task like this. EDIT: There is a hard requirement that the remote machines on the floor are running Windows. The control machines do not have a hard OS requirement. I would really like to stay away from Multicast because of complications with upstream routers. Is Multicast or BitTorrent the better way to go on this? Is there another protocol that might work better?

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  • BizTalk host throttling &ndash; Singleton pattern and High database size

    - by S.E.R.
    Originally posted on: http://geekswithblogs.net/SERivas/archive/2013/06/30/biztalk-host-throttling-ndash-singleton-pattern-and-high-database-size.aspxI have worked for some days around the singleton pattern (for those unfamiliar with it, read this post by Victor Fehlberg) and have come across a few very interesting posts, among which one dealt with performance issues (here, also by Victor Fehlberg). Simply put: if you have an orchestration which implements the singleton pattern, then performances will continuously decrease as the orchestration receives and consumes messages, and that behavior is more obvious when the orchestration never ends (ie : it keeps looping and never terminates or completes). As I experienced the same kind of problem (actually I was alerted by SCOM, which told me that the host was being throttled because of High database size), I thought it would be a good idea to dig a little bit a see what happens deep inside BizTalk and thus understand the reasons for this behavior. NOTE: in this article, I will focus on this High database size throttling condition. I will try and work on the other conditions in some not too distant future… Test conditions The singleton orchestration For the purpose of this study, I have created the following orchestration, which is a very basic implementation of a singleton that piles up incoming messages, then does something else when a certain timeout has been reached without receiving another message: Throttling settings I have two distinct hosts : one that hosts the receive port (basic FILE port) : Ports_ReceiveHostone that hosts the orchestration : ProcessingHost In order to emphasize the throttling mechanism, I have modified the throttling settings for each of these hosts are as follows (all other parameters are set to the default value): [Throttling thresholds] Message count in database: 500 (default value : 50000) Evolution of performance counters when submitting messages Since we are investigating the High database size throttling condition, here are the performance counter that we should take a look at (all of them are in the BizTalk:Message Agent performance object): Database sizeHigh database sizeMessage delivery throttling stateMessage publishing throttling stateMessage delivery delay (ms)Message publishing delay (ms)Message delivery throttling state durationMessage publishing throttling state duration (If you are not used to Perfmon, I strongly recommend that you start using it right now: it is a wonderful tool that allows you to open the hood and see what is going on inside BizTalk – and other systems) Database size It is quite obvious that we will start by watching the database size and high database size counters, just to see when the first reaches the configured threshold (500) and when the second rings the alarm. NOTE : During this test I submitted 600 messages, one message at a time every 10ms to see the evolution of the counters we have previously selected. It might not show very well on this screenshot, but here is what happened: From 15:46:50 to 15:47:50, the database size for the Ports_ReceiveHost host (blue line) kept growing until it reached a maximum of 504.At 15:47:50, the high database size alert fires At first I was surprised by this result: why is it the database size of the receiving host that keeps growing since it is the processing host that piles up messages? Actually, it makes total sense. This counter measures the size of the database queue that is being filled by the host, not consumed. Therefore, the high database size alert is raised on the host that fills the queue: Ports_ReceiveHost. More information is available on the Public MPWiki page. Now, looking at the Message publishing throttling state for the receiving host (green line), we can see that a throttling condition has been reached at 15:47:50: We can also see that the Message publishing delay(ms) (blue line) has begun growing slowly from this point. All of this explains why performances keep decreasing when a singleton keeps processing new messages: the database size grows and when it has exceeded the Message count in database threshold, the host is throttled and the publishing delay keeps increasing. Digging further So, what happens to the database queue then? Is it flushed some day or does it keep growing and growing indefinitely? The real question being: will the host be throttled forever because of this singleton? To answer this question, I set the Message count in database threshold to 20 (this value is very low in order not to wait for too long, otherwise I certainly would have fallen asleep in front of my screen) and I submitted 30 messages. The test was started at 18:26. At 18:56 (ie : exactly 30min later) the throttling was stopped and the database size was divided by 2. 30 min later again, the database size had dropped to almost zero: I guess I’ll have to find some documentation and do some more testing before I sort this out! My guess is that some maintenance job is at work here, though I cannot tell which one Digging even further If we take a look at the Message delivery throttling state counter for the processing host, we can see that this host was also throttled during the submission of the 600 documents: The value for the counter was 1, meaning that Message delivery incoming rate for the host instance exceeds the Message delivery outgoing rate * the specified Rate overdrive factor (percent) value. We will see this another day… :) A last word Let’s end this article with a warning: DO NOT CHANGE THE THROTTLING SETTINGS LIGHTLY! The temptation can be great to just bypass throttling by setting very high values for each parameter (or zero in some cases, which simply disables throttling). Nevertheless, always keep in mind that this mechanism is here for a very good reason: prevent your BizTalk infrastructure from exploding!! So whatever you do with those settings, do a lot of testing and benchmarking!

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  • SQLAuthority News – A Real Story of Book Getting ‘Out of Stock’ to A 25% Discount Story Available

    - by pinaldave
    As many of my readers may know, I have recently written a few books.  Right now I’d like to talk about SQL Server Interview Questions and Answers (http://bit.ly/sqlinterviewbook ), my newest release. What inspired me to write this book was similar to my motivations for my previous titles – I wanted to help people understand SQL Server concepts and ace interview questions so that they could get a great job they love, as much as I love my own job. If you are new to SQL Server, don’t think I left you out of my book writing efforts. If you are new to the subject or have not had to deal with SQL Server in a long time, this book is perfect for someone who wants or needs a last minute refresher. If you are facing an upcoming interview and want to impress your future bosses, this book is perfect for getting you up to speed in a short time. However, if you are already an expert, you will still find a lot to learn and many pointers and suggestions that go deep into the subject. As I said before, I wrote this book in order to help my community, and I certainly hoped that this book would become popular. However, we decided to print a very limited number of copies to begin with. We did not think that it would sell out since much of the information is available for free online. We could not have been more wrong! We incorrectly estimated what people wanted. We did not realize that there is still a need and an interest for structured learning. So, with great reservations, we printed quite a large number of copies – and it still ran out in 36 hours! We got call from the online store with a request for more copies within 12 hours. But we had printed only as many as we had sent them. There were no extra copies. We finally talked to the printer to get more copies. However, due to festivals and holidays the copies could not be shipped to the online retailer for two days. We knew for sure that they were going to be out of the book for 48 hours. 48 hours – this was very difficult as the book was very highly anticipated. Many people wanted to buy this book quickly, and receive it soon in order to meet a deadline or to study for an upcoming test of their knowledge. But now this book was out of stock on the retail store. The way the online store works is that if the Indian-priced book is not there they list the US version of the book so that buyers will not be disappointed. The problem was that the US price of the book is three times more than the Indian price – which means one has to pay three times as much to buy this book instead of the previous very low price. We received a lot of communication on this subject, here are some examples: We are now businessmen and only focusing on money Why has the price tripled in 36 hours Why we are not honest with the price If the prices will ever come down And some of the letters we cannot post here! Well, finally after 48 hours the Indian stock was finally available online. Thanks to our printer who worked day and night to get all the copies printed. He divided the complete stock in two parts. The first part they sent immediately to online retailer  and the second part they kept with them to sell. Finally, the online retailer got them online promptly as well, and the price returned to normal. Our book once again got in business and became the eighth most popular new release in 36 hours. We appreciate your love and support. Without all of your interest and love we would have never come this far and the book would not be so successful. After thinking about all your support and how patient you were with our online troubles, the online retailer has decided to give an extra 25% discount for a limited time only. I think the 48 hours when the book was out of stock were very horrible and stressful and I’d like to apologize to my loyal readers for the mishap. I hope that the 25% off is enough to sooth any remaining hurt feelings, and that everyone will continue to learn and discover things in the book. Once again thank you so much and I truly hope that you all enjoy reading the book as much as I enjoyed writing it. My book SQL Server Interview Questions and Answers is available now. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Interview Questions and Answers, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Book Review, SQLAuthority News, T SQL, Technology

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  • Partition Wise Joins

    - by jean-pierre.dijcks
    Some say they are the holy grail of parallel computing and PWJ is the basis for a shared nothing system and the only join method that is available on a shared nothing system (yes this is oversimplified!). The magic in Oracle is of course that is one of many ways to join data. And yes, this is the old flexibility vs. simplicity discussion all over, so I won't go there... the point is that what you must do in a shared nothing system, you can do in Oracle with the same speed and methods. The Theory A partition wise join is a join between (for simplicity) two tables that are partitioned on the same column with the same partitioning scheme. In shared nothing this is effectively hard partitioning locating data on a specific node / storage combo. In Oracle is is logical partitioning. If you now join the two tables on that partitioned column you can break up the join in smaller joins exactly along the partitions in the data. Since they are partitioned (grouped) into the same buckets, all values required to do the join live in the equivalent bucket on either sides. No need to talk to anyone else, no need to redistribute data to anyone else... in short, the optimal join method for parallel processing of two large data sets. PWJ's in Oracle Since we do not hard partition the data across nodes in Oracle we use the Partitioning option to the database to create the buckets, then set the Degree of Parallelism (or run Auto DOP - see here) and get our PWJs. The main questions always asked are: How many partitions should I create? What should my DOP be? In a shared nothing system the answer is of course, as many partitions as there are nodes which will be your DOP. In Oracle we do want you to look at the workload and concurrency, and once you know that to understand the following rules of thumb. Within Oracle we have more ways of joining of data, so it is important to understand some of the PWJ ideas and what it means if you have an uneven distribution across processes. Assume we have a simple scenario where we partition the data on a hash key resulting in 4 hash partitions (H1 -H4). We have 2 parallel processes that have been tasked with reading these partitions (P1 - P2). The work is evenly divided assuming the partitions are the same size and we can scan this in time t1 as shown below. Now assume that we have changed the system and have a 5th partition but still have our 2 workers P1 and P2. The time it takes is actually 50% more assuming the 5th partition has the same size as the original H1 - H4 partitions. In other words to scan these 5 partitions, the time t2 it takes is not 1/5th more expensive, it is a lot more expensive and some other join plans may now start to look exciting to the optimizer. Just to post the disclaimer, it is not as simple as I state it here, but you get the idea on how much more expensive this plan may now look... Based on this little example there are a few rules of thumb to follow to get the partition wise joins. First, choose a DOP that is a factor of two (2). So always choose something like 2, 4, 8, 16, 32 and so on... Second, choose a number of partitions that is larger or equal to 2* DOP. Third, make sure the number of partitions is divisible through 2 without orphans. This is also known as an even number... Fourth, choose a stable partition count strategy, which is typically hash, which can be a sub partitioning strategy rather than the main strategy (range - hash is a popular one). Fifth, make sure you do this on the join key between the two large tables you want to join (and this should be the obvious one...). Translating this into an example: DOP = 8 (determined based on concurrency or by using Auto DOP with a cap due to concurrency) says that the number of partitions >= 16. Number of hash (sub) partitions = 32, which gives each process four partitions to work on. This number is somewhat arbitrary and depends on your data and system. In this case my main reasoning is that if you get more room on the box you can easily move the DOP for the query to 16 without repartitioning... and of course it makes for no leftovers on the table... And yes, we recommend up-to-date statistics. And before you start complaining, do read this post on a cool way to do stats in 11.

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  • Unit testing is… well, flawed.

    - by Dewald Galjaard
    Hey someone had to say it. I clearly recall my first IT job. I was appointed Systems Co-coordinator for a leading South African retailer at store level. Don’t get me wrong, there is absolutely nothing wrong with an honest day’s labor and in fact I highly recommend it, however I’m obliged to refer to the designation cautiously; in reality all I had to do was monitor in-store prices and two UNIX front line controllers. If anything went wrong – I only had to phone it in… Luckily that wasn’t all I did. My duties extended to some other interesting annual occurrence – stock take. Despite a bit more curious affair, it was still a tedious process that took weeks of preparation and several nights to complete.  Then also I remember that no matter how elaborate our planning was, the entire exercise would be rendered useless if we couldn’t get the basics right – that being the act of counting. Sounds simple right? We’ll with a store which could potentially carry over tens of thousands of different items… we’ll let’s just say I believe that’s when I first became a coffee addict. In those days the act of counting stock was a very humble process. Nothing like we have today. A staff member would be assigned a bin or shelve filled with items he or she had to sort then count. Thereafter they had to record their findings on a complementary piece of paper. Every night I would manage several teams. Each team was divided into two groups - counters and auditors. Both groups had the same task, only auditors followed shortly on the heels of the counters, recounting stock levels, making sure the original count correspond to their findings. It was a simple yet hugely responsible orchestration of people and thankfully there was one fundamental and golden rule I could always abide by to ensure things run smoothly – No-one was allowed to audit their own work. Nope, not even on nights when I didn’t have enough staff available. This meant I too at times had to get up there and get counting, or have the audit stand over until the next evening. The reason for this was obvious - late at night and with so much to do we were prone to make some mistakes, then on the recount, without a fresh set of eyes, you were likely to repeat the offence. Now years later this rule or guideline still holds true as we develop software (as far removed as software development from counting stock may be). For some reason it is a fundamental guideline we’re simply ignorant of. We write our code, we write our tests and thus commit the same horrendous offence. Yes, the procedure of writing unit tests as practiced in most development houses today – is flawed. Most if not all of the tests we write today exercise application logic – our logic. They are based on the way we believe an application or method should/may/will behave or function. As we write our tests, our unit tests mirror our best understanding of the inner workings of our application code. Unfortunately these tests will therefore also include (or be unaware of) any imperfections and errors on our part. If your logic is flawed as you write your initial code, chances are, without a fresh set of eyes, you will commit the same error second time around too. Not even experience seems to be a suitable solution. It certainly helps to have deeper insight, but is that really the answer we should be looking for? Is that really failsafe? What about code review? Code review is certainly an answer. You could have one developer coding away and another (or team) making sure the logic is sound. The practice however has its obvious drawbacks. Firstly and mainly it is resource intensive and from what I’ve seen in most development houses, given heavy deadlines, this guideline is seldom adhered to. Hardly ever do we have the resources, money or time readily available. So what other options are out there? A quest to find some solution revealed a project by Microsoft Research called PEX. PEX is a framework which creates several test scenarios for each method or class you write, automatically. Think of it as your own personal auditor. Within a few clicks the framework will auto generate several unit tests for a given class or method and save them to a single project. PEX help to audit your work. It lends a fresh set of eyes to any project you’re working on and best of all; it is cost effective and fast. Check them out at http://research.microsoft.com/en-us/projects/pex/ In upcoming posts we’ll dive deeper into how it works and how it can help you.   Certainly there are more similar frameworks out there and I would love to hear from you. Please share your experiences and insights.

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  • Optimizing collision engine bottleneck

    - by Vittorio Romeo
    Foreword: I'm aware that optimizing this bottleneck is not a necessity - the engine is already very fast. I, however, for fun and educational purposes, would love to find a way to make the engine even faster. I'm creating a general-purpose C++ 2D collision detection/response engine, with an emphasis on flexibility and speed. Here's a very basic diagram of its architecture: Basically, the main class is World, which owns (manages memory) of a ResolverBase*, a SpatialBase* and a vector<Body*>. SpatialBase is a pure virtual class which deals with broad-phase collision detection. ResolverBase is a pure virtual class which deals with collision resolution. The bodies communicate to the World::SpatialBase* with SpatialInfo objects, owned by the bodies themselves. There currenly is one spatial class: Grid : SpatialBase, which is a basic fixed 2D grid. It has it's own info class, GridInfo : SpatialInfo. Here's how its architecture looks: The Grid class owns a 2D array of Cell*. The Cell class contains two collection of (not owned) Body*: a vector<Body*> which contains all the bodies that are in the cell, and a map<int, vector<Body*>> which contains all the bodies that are in the cell, divided in groups. Bodies, in fact, have a groupId int that is used for collision groups. GridInfo objects also contain non-owning pointers to the cells the body is in. As I previously said, the engine is based on groups. Body::getGroups() returns a vector<int> of all the groups the body is part of. Body::getGroupsToCheck() returns a vector<int> of all the groups the body has to check collision against. Bodies can occupy more than a single cell. GridInfo always stores non-owning pointers to the occupied cells. After the bodies move, collision detection happens. We assume that all bodies are axis-aligned bounding boxes. How broad-phase collision detection works: Part 1: spatial info update For each Body body: Top-leftmost occupied cell and bottom-rightmost occupied cells are calculated. If they differ from the previous cells, body.gridInfo.cells is cleared, and filled with all the cells the body occupies (2D for loop from the top-leftmost cell to the bottom-rightmost cell). body is now guaranteed to know what cells it occupies. For a performance boost, it stores a pointer to every map<int, vector<Body*>> of every cell it occupies where the int is a group of body->getGroupsToCheck(). These pointers get stored in gridInfo->queries, which is simply a vector<map<int, vector<Body*>>*>. body is now guaranteed to have a pointer to every vector<Body*> of bodies of groups it needs to check collision against. These pointers are stored in gridInfo->queries. Part 2: actual collision checks For each Body body: body clears and fills a vector<Body*> bodiesToCheck, which contains all the bodies it needs to check against. Duplicates are avoided (bodies can belong to more than one group) by checking if bodiesToCheck already contains the body we're trying to add. const vector<Body*>& GridInfo::getBodiesToCheck() { bodiesToCheck.clear(); for(const auto& q : queries) for(const auto& b : *q) if(!contains(bodiesToCheck, b)) bodiesToCheck.push_back(b); return bodiesToCheck; } The GridInfo::getBodiesToCheck() method IS THE BOTTLENECK. The bodiesToCheck vector must be filled for every body update because bodies could have moved meanwhile. It also needs to prevent duplicate collision checks. The contains function simply checks if the vector already contains a body with std::find. Collision is checked and resolved for every body in bodiesToCheck. That's it. So, I've been trying to optimize this broad-phase collision detection for quite a while now. Every time I try something else than the current architecture/setup, something doesn't go as planned or I make assumption about the simulation that later are proven to be false. My question is: how can I optimize the broad-phase of my collision engine maintaining the grouped bodies approach? Is there some kind of magic C++ optimization that can be applied here? Can the architecture be redesigned in order to allow for more performance? Actual implementation: SSVSCollsion Body.h, Body.cpp World.h, World.cpp Grid.h, Grid.cpp Cell.h, Cell.cpp GridInfo.h, GridInfo.cpp

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  • Getting Started with Prism (aka Composite Application Guidance for WPF and Silverlight)

    - by dotneteer
    Overview Prism is a framework from the Microsoft Patterns and Practice team that allow you to create WPF and Silverlight in a modular way. It is especially valuable for larger projects in which a large number of developers can develop in parallel. Prism achieves its goal by supplying several services: · Dependency Injection (DI) and Inversion of control (IoC): By using DI, Prism takes away the responsibility of instantiating and managing the life time of dependency objects from individual components to a container. Prism relies on containers to discover, manage and compose large number of objects. By varying the configuration, the container can also inject mock objects for unit testing. Out of the box, Prism supports Unity and MEF as container although it is possible to use other containers by subclassing the Bootstrapper class. · Modularity and Region: Prism supplies the framework to split application into modules from the application shell. Each module is a library project that contains both UI and code and is responsible to initialize itself when loaded by the shell. Each window can be further divided into regions. A region is a user control with associated model. · Model, view and view-model (MVVM) pattern: Prism promotes the user MVVM. The use of DI container makes it much easier to inject model into view. WPF already has excellent data binding and commanding mechanism. To be productive with Prism, it is important to understand WPF data binding and commanding well. · Event-aggregation: Prism promotes loosely coupled components. Prism discourages for components from different modules to communicate each other, thus leading to dependency. Instead, Prism supplies an event-aggregation mechanism that allows components to publish and subscribe events without knowing each other. Architecture In the following, I will go into a little more detail on the services provided by Prism. Bootstrapper In a typical WPF application, application start-up is controls by App.xaml and its code behind. The main window of the application is typically specified in the App.xaml file. In a Prism application, we start a bootstrapper in the App class and delegate the duty of main window to the bootstrapper. The bootstrapper will start a dependency-injection container so all future object instantiations are managed by the container. Out of box, Prism provides the UnityBootstrapper and MefUnityBootstrapper abstract classes. All application needs to either provide a concrete implementation of one of these bootstrappers, or alternatively, subclass the Bootstrapper class with another DI container. A concrete bootstrapper class must implement the CreateShell method. Its responsibility is to resolve and create the Shell object through the DI container to serve as the main window for the application. The other important method to override is ConfigureModuleCatalog. The bootstrapper can register modules for the application. In a more advance scenario, an application does not have to know all its modules at compile time. Modules can be discovered at run time. Readers to refer to one of the Open Modularity Quick Starts for more information. Modules Once modules are registered with or discovered by Prism, they are instantiated by the DI container and their Initialize method is called. The DI container can inject into a module a region registry that implements IRegionViewRegistry interface. The module, in its Initialize method, can then call RegisterViewWithRegion method of the registry to register its regions. Regions Regions, once registered, are managed by the RegionManager. The shell can then load regions either through the RegionManager.RegionName attached property or dynamically through code. When a view is created by the region manager, the DI container can inject view model and other services into the view. The view then has a reference to the view model through which it can interact with backend services. Service locator Although it is possible to inject services into dependent classes through a DI container, an alternative way is to use the ServiceLocator to retrieve a service on demard. Prism supplies a service locator implementation and it is possible to get an instance of the service by calling: ServiceLocator.Current.GetInstance<IServiceType>() Event aggregator Prism supplies an IEventAggregator interface and implementation that can be injected into any class that needs to communicate with each other in a loosely-coupled fashion. The event aggregator uses a publisher/subscriber model. A class can publishes an event by calling eventAggregator.GetEvent<EventType>().Publish(parameter) to raise an event. Other classes can subscribe the event by calling eventAggregator.GetEvent<EventType>().Subscribe(EventHandler, other options). Getting started The easiest way to get started with Prism is to go through the Prism Hands-On labs and look at the Hello World QuickStart. The Hello World QuickStart shows how bootstrapper, modules and region works. Next, I would recommend you to look at the Stock Trader Reference Implementation. It is a more in depth example that resemble we want to set up an application. Several other QuickStarts cover individual Prism services. Some scenarios, such as dynamic module discovery, are more advanced. Apart from the official prism document, you can get an overview by reading Glen Block’s MSDN Magazine article. I have found the best free training material is from the Boise Code Camp. To be effective with Prism, it is important to understands key concepts of WPF well first, such as the DependencyProperty system, data binding, resource, theme and ICommand. It is also important to know your DI container of choice well. I will try to explorer these subjects in depth in the future. Testimony Recently, I worked on a desktop WPF application using Prism. I had a wonderful experience with Prism. The Prism is flexible enough even in the presence of third party controls such as Telerik WPF controls. We have never encountered any significant obstacle.

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  • Ranking - an Introduction

    - by PointsToShare
    © 2011 By: Dov Trietsch. All rights reserved Ranking Ranking is quite common in the internet. Readers are asked to rank their latest reading by clicking on one of 5 (sometimes 10) stars. The number of stars is then converted to a number and the average number of stars as selected by all the readers is proudly (or shamefully) displayed for future readers. SharePoint 2007 lacked this feature altogether. SharePoint 2010 allows the users to rank items in a list or documents in a library (the two are actually the same because a library is actually a list). But in SP2010 the computation of the average is done later on a timer rather than on-the-spot as it should be. I suspect that the reason for this shortcoming is that they did not involve a mathematician! Let me explain. Ranking is kept in a related list. When a user rates a document the rank-list is added an item with the item id, the user name, and his number of stars. The fact that a user already ranked an item prevents him from ranking it again. This prevents the creator of the item from asking his mother to rank it a 5 and do it 753 times, thus stacking the ballot. Some systems will allow a user to change his rating and this will be done by updating the rank-list item. Now, when the timer kicks off, the list is spanned and for each item the rank-list items containing this id are summed up and divided by the number of votes thus yielding the new average. This is obviously very time consuming and very server intensive. In the 18th century an early actuary named James Dodson used what the great Augustus De Morgan (of De Morgan’s law) later named Commutation tables. The labor involved in computing a life insurance premium was staggering and also very error prone. Clerks with pencil and paper would multiply and add mountains of numbers to do the task. The more steps the greater the probability of error and the more expensive the process. Commutation tables created a “summary” of many steps and reduced the work 100 fold. So had Microsoft taken a lesson in the history of computation, they would have developed a much faster way for rating that may be done in real-time and is also 100 times faster and less CPU intensive. How do we do this? We use a form of commutation. We always keep the number of votes and the total of stars. One simple division gives us the average. So we write an event receiver. When a vote is added, we just add the stars to the total-stars and 1 to the number of votes. We then recomputed the average. When a vote is updated, we reduce the total by the old vote, increase it by the new vote and leave the number of votes the same. Again we do the division to get the new average. When a vote is deleted (highly unlikely and maybe even prohibited), we reduce the total by that vote and reduce the number of votes by 1… Gone are the days of spanning lists, counting items, and tallying votes and we have no need for a timer process to run it all. This is the first of a few treatises on ranking. Even though I discussed the math and the history thereof, in here I am only going to solve the presentation issue. I wanted to create the CSS and Jscript needed to display the stars, create the various effects like hovering and clicking (onmouseover, onmouseout, onclick, etc.) and I wanted to create a general solution with any number of stars. When I had it all done, I created the ranking game so that I could test it. The game is interesting in and on itself, so here it is (or go to the games page and select “rank the stars”). BTW, when you play it, look at the source code and see how it was all done.  Next, how the 5 stars are displayed in the New and Update forms. When the whole set of articles will be done, you’ll be able to create the complete solution. That’s all folks!

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  • Talend Enterprise Data Integration overperforms on Oracle SPARC T4

    - by Amir Javanshir
    The SPARC T microprocessor, released in 2005 by Sun Microsystems, and now continued at Oracle, has a good track record in parallel execution and multi-threaded performance. However it was less suited for pure single-threaded workloads. The new SPARC T4 processor is now filling that gap by offering a 5x better single-thread performance over previous generations. Following our long-term relationship with Talend, a fast growing ISV positioned by Gartner in the “Visionaries” quadrant of the “Magic Quadrant for Data Integration Tools”, we decided to test some of their integration components with the T4 chip, more precisely on a T4-1 system, in order to verify first hand if this new processor stands up to its promises. Several tests were performed, mainly focused on: Single-thread performance of the new SPARC T4 processor compared to an older SPARC T2+ processor Overall throughput of the SPARC T4-1 server using multiple threads The tests consisted in reading large amounts of data --ten's of gigabytes--, processing and writing them back to a file or an Oracle 11gR2 database table. They are CPU, memory and IO bound tests. Given the main focus of this project --CPU performance--, bottlenecks were removed as much as possible on the memory and IO sub-systems. When possible, the data to process was put into the ZFS filesystem cache, for instance. Also, two external storage devices were directly attached to the servers under test, each one divided in two ZFS pools for read and write operations. Multi-thread: Testing throughput on the Oracle T4-1 The tests were performed with different number of simultaneous threads (1, 2, 4, 8, 12, 16, 32, 48 and 64) and using different storage devices: Flash, Fibre Channel storage, two stripped internal disks and one single internal disk. All storage devices used ZFS as filesystem and volume management. Each thread read a dedicated 1GB-large file containing 12.5M lines with the following structure: customerID;FirstName;LastName;StreetAddress;City;State;Zip;Cust_Status;Since_DT;Status_DT 1;Ronald;Reagan;South Highway;Santa Fe;Montana;98756;A;04-06-2006;09-08-2008 2;Theodore;Roosevelt;Timberlane Drive;Columbus;Louisiana;75677;A;10-05-2009;27-05-2008 3;Andrew;Madison;S Rustle St;Santa Fe;Arkansas;75677;A;29-04-2005;09-02-2008 4;Dwight;Adams;South Roosevelt Drive;Baton Rouge;Vermont;75677;A;15-02-2004;26-01-2007 […] The following graphs present the results of our tests: Unsurprisingly up to 16 threads, all files fit in the ZFS cache a.k.a L2ARC : once the cache is hot there is no performance difference depending on the underlying storage. From 16 threads upwards however, it is clear that IO becomes a bottleneck, having a good IO subsystem is thus key. Single-disk performance collapses whereas the Sun F5100 and ST6180 arrays allow the T4-1 to scale quite seamlessly. From 32 to 64 threads, the performance is almost constant with just a slow decline. For the database load tests, only the best IO configuration --using external storage devices-- were used, hosting the Oracle table spaces and redo log files. Using the Sun Storage F5100 array allows the T4-1 server to scale up to 48 parallel JVM processes before saturating the CPU. The final result is a staggering 646K lines per second insertion in an Oracle table using 48 parallel threads. Single-thread: Testing the single thread performance Seven different tests were performed on both servers. Given the fact that only one thread, thus one file was read, no IO bottleneck was involved, all data being served from the ZFS cache. Read File ? Filter ? Write File: Read file, filter data, write the filtered data in a new file. The filter is set on the “Status” column: only lines with status set to “A” are selected. This limits each output file to about 500 MB. Read File ? Load Database Table: Read file, insert into a single Oracle table. Average: Read file, compute the average of a numeric column, write the result in a new file. Division & Square Root: Read file, perform a division and square root on a numeric column, write the result data in a new file. Oracle DB Dump: Dump the content of an Oracle table (12.5M rows) into a CSV file. Transform: Read file, transform, write the result data in a new file. The transformations applied are: set the address column to upper case and add an extra column at the end, which is the concatenation of two columns. Sort: Read file, sort a numeric and alpha numeric column, write the result data in a new file. The following table and graph present the final results of the tests: Throughput unit is thousand lines per second processed (K lines/second). Improvement is the % of improvement between the T5140 and T4-1. Test T4-1 (Time s.) T5140 (Time s.) Improvement T4-1 (Throughput) T5140 (Throughput) Read/Filter/Write 125 806 645% 100 16 Read/Load Database 195 1111 570% 64 11 Average 96 557 580% 130 22 Division & Square Root 161 1054 655% 78 12 Oracle DB Dump 164 945 576% 76 13 Transform 159 1124 707% 79 11 Sort 251 1336 532% 50 9 The improvement of single-thread performance is quite dramatic: depending on the tests, the T4 is between 5.4 to 7 times faster than the T2+. It seems clear that the SPARC T4 processor has gone a long way filling the gap in single-thread performance, without sacrifying the multi-threaded capability as it still shows a very impressive scaling on heavy-duty multi-threaded jobs. Finally, as always at Oracle ISV Engineering, we are happy to help our ISV partners test their own applications on our platforms, so don't hesitate to contact us and let's see what the SPARC T4-based systems can do for your application! "As describe in this benchmark, Talend Enterprise Data Integration has overperformed on T4. I was generally happy to see that the T4 gave scaling opportunities for many scenarios like complex aggregations. Row by row insertion in Oracle DB is faster with more than 650,000 rows per seconds without using any bulk Oracle capabilities !" Cedric Carbone, Talend CTO.

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  • SQL SERVER – What is Incremental Statistics? – Performance improvements in SQL Server 2014 – Part 1

    - by Pinal Dave
    This is the first part of the series Incremental Statistics. Here is the index of the complete series. What is Incremental Statistics? – Performance improvements in SQL Server 2014 – Part 1 Simple Example of Incremental Statistics – Performance improvements in SQL Server 2014 – Part 2 DMV to Identify Incremental Statistics – Performance improvements in SQL Server 2014 – Part 3 Statistics are considered one of the most important aspects of SQL Server Performance Tuning. You might have often heard the phrase, with related to performance tuning. “Update Statistics before you take any other steps to tune performance”. Honestly, I have said above statement many times and many times, I have personally updated statistics before I start to do any performance tuning exercise. You may agree or disagree to the point, but there is no denial that Statistics play an extremely vital role in the performance tuning. SQL Server 2014 has a new feature called Incremental Statistics. I have been playing with this feature for quite a while and I find that very interesting. After spending some time with this feature, I decided to write about this subject over here. New in SQL Server 2014 – Incremental Statistics Well, it seems like lots of people wants to start using SQL Server 2014′s new feature of Incremetnal Statistics. However, let us understand what actually this feature does and how it can help. I will try to simplify this feature first before I start working on the demo code. Code for all versions of SQL Server Here is the code which you can execute on all versions of SQL Server and it will update the statistics of your table. The keyword which you should pay attention is WITH FULLSCAN. It will scan the entire table and build brand new statistics for you which your SQL Server Performance Tuning engine can use for better estimation of your execution plan. UPDATE STATISTICS TableName(StatisticsName) WITH FULLSCAN Who should learn about this? Why? If you are using partitions in your database, you should consider about implementing this feature. Otherwise, this feature is pretty much not applicable to you. Well, if you are using single partition and your table data is in a single place, you still have to update your statistics the same way you have been doing. If you are using multiple partitions, this may be a very useful feature for you. In most cases, users have multiple partitions because they have lots of data in their table. Each partition will have data which belongs to itself. Now it is very common that each partition are populated separately in SQL Server. Real World Example For example, if your table contains data which is related to sales, you will have plenty of entries in your table. It will be a good idea to divide the partition into multiple filegroups for example, you can divide this table into 3 semesters or 4 quarters or even 12 months. Let us assume that we have divided our table into 12 different partitions. Now for the month of January, our first partition will be populated and for the month of February our second partition will be populated. Now assume, that you have plenty of the data in your first and second partition. Now the month of March has just started and your third partition has started to populate. Due to some reason, if you want to update your statistics, what will you do? In SQL Server 2012 and earlier version You will just use the code of WITH FULLSCAN and update the entire table. That means even though you have only data in third partition you will still update the entire table. This will be VERY resource intensive process as you will be updating the statistics of the partition 1 and 2 where data has not changed at all. In SQL Server 2014 You will just update the partition of Partition 3. There is a special syntax where you can now specify which partition you want to update now. The impact of this is that it is smartly merging the new data with old statistics and update the entire statistics without doing FULLSCAN of your entire table. This has a huge impact on performance. Remember that the new feature in SQL Server 2014 does not change anything besides the capability to update a single partition. However, there is one feature which is indeed attractive. Previously, when table data were changed 20% at that time, statistics update were triggered. However, now the same threshold is applicable to a single partition. That means if your partition faces 20% data, change it will also trigger partition level statistics update which, when merged to your final statistics will give you better performance. In summary If you are not using a partition, this feature is not applicable to you. If you are using a partition, this feature can be very helpful to you. Tomorrow: We will see working code of SQL Server 2014 Incremental Statistics. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: SQL Statistics, Statistics

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  • Highlights from the Oracle Customer Experience Summit @ OpenWorld

    - by Kathryn Perry
    A guest post by David Vap, Group Vice President, Oracle Applications Product Development The Oracle Customer Experience Summit was the first-ever event covering the full breadth of Oracle's CX portfolio -- Marketing, Sales, Commerce, and Service. The purpose of the Summit was to articulate the customer experience imperative and to showcase the suite of Oracle products that can help our customers create the best possible customer experience. This topic has always been a very important one, but now that there are so many alternative companies to do business with and because people have such public ways to voice their displeasure, it's necessary for vendors to have multiple listening posts in place to gauge consumer sentiment. They need to know what is going on in real time and be able to react quickly to turn negative situations into positive ones. Those can then be shared in a social manner to enhance the brand and turn the customer into a repeat customer. The Summit was focused on Oracle's portfolio of products and entirely dedicated to customers who are committed to building great customer experiences within their businesses. Rather than DBAs, the attendees were business people looking to collaborate with other like-minded experts and find out how Oracle can help in terms of technology, best practices, and expertise. The event was at the Westin St. Francis Hotel in San Francisco as part of Oracle OpenWorld. We had eight hundred people attend, which was great for the first year. Next year, there's no doubt in my mind, we can raise that number to 5,000. Alignment and Logic Oracle's Customer Experience portfolio is made up of a combination of acquired and organic products owned by many people who are new to Oracle. We include homegrown Fusion CRM, as well as RightNow, Inquira, OPA, Vitrue, ATG, Endeca, and many others. The attendees knew of the acquisitions, so naturally they wanted to see how the products all fit together and hear the logic behind the portfolio. To tell them about our alignment, we needed to be aligned. To accomplish that, a cross functional team at Oracle agreed on the messaging so that every single Oracle presenter could cover the big picture before going deep into a product or topic. Talking about the full suite of products in one session produced overflow value for other products. And even though this internal coordination was a huge effort, everyone saw the value for our customers and for our long-term cooperation and success. Keynotes, Workshops, and Tents of Innovation We scored by having Seth Godin as our keynote speaker ? always provocative and popular. The opening keynote was a session orchestrated by Mark Hurd, Anthony Lye, and me. Mark set the stage by giving real-world examples of bad customer experiences, Anthony clearly articulated the business imperative for addressing these experiences, and I brought it all to life by taking the audience around the Customer Lifecycle and showing demos and videos, with partners included at each of the stops around the lifecycle. Brian Curran, a VP for RightNow Product Strategy, presented a session that was in high demand called The Economics of Customer Experience. People loved hearing how to build a business case and justify the cost of building a better customer experience. John Kembel, another VP for RightNow Product Strategy, held a workshop that customers raved about. It was based on the journey mapping methodology he created, which is a way to talk to customers about where they want to make improvements to their customers' experiences. He divided the audience into groups led by facilitators. Each person had the opportunity to engage with experts and peers and construct some real takeaways. From left to right: Brian Curran, John Kembel, Seth Godin, and George Kembel The conference hotel was across from Union Square so we used that space to set up Innovation Tents. During the day we served lunch in the tents and partners showed their different innovative ideas. It was very interesting to see all the technologies and advancements. It also gave people a place to mix and mingle and to think about the fringe of where we could all take these ideas. Product Portfolio Plus Thought Leadership Of course there is always room for improvement, but the feedback on the format of the conference was positive. Ninety percent of the sessions had either a partner or a customer teamed with an Oracle presenter. The presentations weren't dry, one-way information dumps, but more interactive. I just followed up with a CEO who attended the conference with his Head of Marketing. He told me that they are using John Kembel's journey mapping methodology across the organization to pull people together. This sort of thought leadership in these highly competitive areas gives Oracle permission to engage around the technology. We have to differentiate ourselves and it's harder to do on the product side because everyone looks the same on paper. But on thought leadership ? we can, and did, take some really big steps. David VapGroup Vice PresidentOracle Applications Product Development

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  • Organization &amp; Architecture UNISA Studies &ndash; Chap 5

    - by MarkPearl
    Learning Outcomes Describe the operation of a memory cell Explain the difference between DRAM and SRAM Discuss the different types of ROM Explain the concepts of a hard failure and a soft error respectively Describe SDRAM organization Semiconductor Main Memory The two traditional forms of RAM used in computers are DRAM and SRAM DRAM (Dynamic RAM) Divided into two technologies… Dynamic Static Dynamic RAM is made with cells that store data as charge on capacitors. The presence or absence of charge in a capacitor is interpreted as a binary 1 or 0. Because capacitors have natural tendency to discharge, dynamic RAM requires periodic charge refreshing to maintain data storage. The term dynamic refers to the tendency of the stored charge to leak away, even with power continuously applied. Although the DRAM cell is used to store a single bit (0 or 1), it is essentially an analogue device. The capacitor can store any charge value within a range, a threshold value determines whether the charge is interpreted as a 1 or 0. SRAM (Static RAM) SRAM is a digital device that uses the same logic elements used in the processor. In SRAM, binary values are stored using traditional flip flop logic configurations. SRAM will hold its data as along as power is supplied to it. Unlike DRAM, no refresh is required to retain data. SRAM vs. DRAM DRAM is simpler and smaller than SRAM. Thus it is more dense and less expensive than SRAM. The cost of the refreshing circuitry for DRAM needs to be considered, but if the machine requires a large amount of memory, DRAM turns out to be cheaper than SRAM. SRAMS are somewhat faster than DRAM, thus SRAM is generally used for cache memory and DRAM is used for main memory. Types of ROM Read Only Memory (ROM) contains a permanent pattern of data that cannot be changed. ROM is non volatile meaning no power source is required to maintain the bit values in memory. While it is possible to read a ROM, it is not possible to write new data into it. An important application of ROM is microprogramming, other applications include library subroutines for frequently wanted functions, System programs, Function tables. A ROM is created like any other integrated circuit chip, with the data actually wired into the chip as part of the fabrication process. To reduce costs of fabrication, we have PROMS. PROMS are… Written only once Non-volatile Written after fabrication Another variation of ROM is the read-mostly memory, which is useful for applications in which read operations are far more frequent than write operations, but for which non volatile storage is required. There are three common forms of read-mostly memory, namely… EPROM EEPROM Flash memory Error Correction Semiconductor memory is subject to errors, which can be classed into two categories… Hard failure – Permanent physical defect so that the memory cell or cells cannot reliably store data Soft failure – Random error that alters the contents of one or more memory cells without damaging the memory (common cause includes power supply issues, etc.) Most modern main memory systems include logic for both detecting and correcting errors. Error detection works as follows… When data is to be read into memory, a calculation is performed on the data to produce a code Both the code and the data are stored When the previously stored word is read out, the code is used to detect and possibly correct errors The error checking provides one of 3 possible results… No errors are detected – the fetched data bits are sent out An error is detected, and it is possible to correct the error. The data bits plus error correction bits are fed into a corrector, which produces a corrected set of bits to be sent out An error is detected, but it is not possible to correct it. This condition is reported Hamming Code See wiki for detailed explanation. We will probably need to know how to do a hemming code – refer to the textbook (pg. 188 – 189) Advanced DRAM organization One of the most critical system bottlenecks when using high-performance processors is the interface to main memory. This interface is the most important pathway in the entire computer system. The basic building block of main memory remains the DRAM chip. In recent years a number of enhancements to the basic DRAM architecture have been explored, and some of these are now on the market including… SDRAM (Synchronous DRAM) DDR-DRAM RDRAM SDRAM (Synchronous DRAM) SDRAM exchanges data with the processor synchronized to an external clock signal and running at the full speed of the processor/memory bus without imposing wait states. SDRAM employs a burst mode to eliminate the address setup time and row and column line precharge time after the first access In burst mode a series of data bits can be clocked out rapidly after the first bit has been accessed SDRAM has a multiple bank internal architecture that improves opportunities for on chip parallelism SDRAM performs best when it is transferring large blocks of data serially There is now an enhanced version of SDRAM known as double data rate SDRAM or DDR-SDRAM that overcomes the once-per-cycle limitation of SDRAM

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  • Big Data – Buzz Words: What is HDFS – Day 8 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is MapReduce. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – HDFS. What is HDFS ? HDFS stands for Hadoop Distributed File System and it is a primary storage system used by Hadoop. It provides high performance access to data across Hadoop clusters. It is usually deployed on low-cost commodity hardware. In commodity hardware deployment server failures are very common. Due to the same reason HDFS is built to have high fault tolerance. The data transfer rate between compute nodes in HDFS is very high, which leads to reduced risk of failure. HDFS creates smaller pieces of the big data and distributes it on different nodes. It also copies each smaller piece to multiple times on different nodes. Hence when any node with the data crashes the system is automatically able to use the data from a different node and continue the process. This is the key feature of the HDFS system. Architecture of HDFS The architecture of the HDFS is master/slave architecture. An HDFS cluster always consists of single NameNode. This single NameNode is a master server and it manages the file system as well regulates access to various files. In additional to NameNode there are multiple DataNodes. There is always one DataNode for each data server. In HDFS a big file is split into one or more blocks and those blocks are stored in a set of DataNodes. The primary task of the NameNode is to open, close or rename files and directory and regulate access to the file system, whereas the primary task of the DataNode is read and write to the file systems. DataNode is also responsible for the creation, deletion or replication of the data based on the instruction from NameNode. In reality, NameNode and DataNode are software designed to run on commodity machine build in Java language. Visual Representation of HDFS Architecture Let us understand how HDFS works with the help of the diagram. Client APP or HDFS Client connects to NameSpace as well as DataNode. Client App access to the DataNode is regulated by NameSpace Node. NameSpace Node allows Client App to connect to the DataNode based by allowing the connection to the DataNode directly. A big data file is divided into multiple data blocks (let us assume that those data chunks are A,B,C and D. Client App will later on write data blocks directly to the DataNode. Client App does not have to directly write to all the node. It just has to write to any one of the node and NameNode will decide on which other DataNode it will have to replicate the data. In our example Client App directly writes to DataNode 1 and detained 3. However, data chunks are automatically replicated to other nodes. All the information like in which DataNode which data block is placed is written back to NameNode. High Availability During Disaster Now as multiple DataNode have same data blocks in the case of any DataNode which faces the disaster, the entire process will continue as other DataNode will assume the role to serve the specific data block which was on the failed node. This system provides very high tolerance to disaster and provides high availability. If you notice there is only single NameNode in our architecture. If that node fails our entire Hadoop Application will stop performing as it is a single node where we store all the metadata. As this node is very critical, it is usually replicated on another clustered as well as on another data rack. Though, that replicated node is not operational in architecture, it has all the necessary data to perform the task of the NameNode in the case of the NameNode fails. The entire Hadoop architecture is built to function smoothly even there are node failures or hardware malfunction. It is built on the simple concept that data is so big it is impossible to have come up with a single piece of the hardware which can manage it properly. We need lots of commodity (cheap) hardware to manage our big data and hardware failure is part of the commodity servers. To reduce the impact of hardware failure Hadoop architecture is built to overcome the limitation of the non-functioning hardware. Tomorrow In tomorrow’s blog post we will discuss the importance of the relational database in Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Highlights from the Oracle Customer Experience Summit @ OpenWorld

    - by Richard Lefebvre
    The Oracle Customer Experience Summit was the first-ever event covering the full breadth of Oracle's CX portfolio -- Marketing, Sales, Commerce, and Service. The purpose of the Summit was to articulate the customer experience imperative and to showcase the suite of Oracle products that can help our customers create the best possible customer experience. This topic has always been a very important one, but now that there are so many alternative companies to do business with and because people have such public ways to voice their displeasure, it's necessary for vendors to have multiple listening posts in place to gauge consumer sentiment. They need to know what is going on in real time and be able to react quickly to turn negative situations into positive ones. Those can then be shared in a social manner to enhance the brand and turn the customer into a repeat customer. The Summit was focused on Oracle's portfolio of products and entirely dedicated to customers who are committed to building great customer experiences within their businesses. Rather than DBAs, the attendees were business people looking to collaborate with other like-minded experts and find out how Oracle can help in terms of technology, best practices, and expertise. The event was at the Westin St. Francis Hotel in San Francisco as part of Oracle OpenWorld. We had eight hundred people attend, which was great for the first year. Next year, there's no doubt in my mind, we can raise that number to 5,000. Alignment and Logic Oracle's Customer Experience portfolio is made up of a combination of acquired and organic products owned by many people who are new to Oracle. We include homegrown Fusion CRM, as well as RightNow, Inquira, OPA, Vitrue, ATG, Endeca, and many others. The attendees knew of the acquisitions, so naturally they wanted to see how the products all fit together and hear the logic behind the portfolio. To tell them about our alignment, we needed to be aligned. To accomplish that, a cross functional team at Oracle agreed on the messaging so that every single Oracle presenter could cover the big picture before going deep into a product or topic. Talking about the full suite of products in one session produced overflow value for other products. And even though this internal coordination was a huge effort, everyone saw the value for our customers and for our long-term cooperation and success. Keynotes, Workshops, and Tents of Innovation We scored by having Seth Godin as our keynote speaker ? always provocative and popular. The opening keynote was a session orchestrated by Mark Hurd, Anthony Lye, and me. Mark set the stage by giving real-world examples of bad customer experiences, Anthony clearly articulated the business imperative for addressing these experiences, and I brought it all to life by taking the audience around the Customer Lifecycle and showing demos and videos, with partners included at each of the stops around the lifecycle. Brian Curran, a VP for RightNow Product Strategy, presented a session that was in high demand called The Economics of Customer Experience. People loved hearing how to build a business case and justify the cost of building a better customer experience. John Kembel, another VP for RightNow Product Strategy, held a workshop that customers raved about. It was based on the journey mapping methodology he created, which is a way to talk to customers about where they want to make improvements to their customers' experiences. He divided the audience into groups led by facilitators. Each person had the opportunity to engage with experts and peers and construct some real takeaways. The conference hotel was across from Union Square so we used that space to set up Innovation Tents. During the day we served lunch in the tents and partners showed their different innovative ideas. It was very interesting to see all the technologies and advancements. It also gave people a place to mix and mingle and to think about the fringe of where we could all take these ideas. Product Portfolio Plus Thought Leadership Of course there is always room for improvement, but the feedback on the format of the conference was positive. Ninety percent of the sessions had either a partner or a customer teamed with an Oracle presenter. The presentations weren't dry, one-way information dumps, but more interactive. I just followed up with a CEO who attended the conference with his Head of Marketing. He told me that they are using John Kembel's journey mapping methodology across the organization to pull people together. This sort of thought leadership in these highly competitive areas gives Oracle permission to engage around the technology. We have to differentiate ourselves and it's harder to do on the product side because everyone looks the same on paper. But on thought leadership ? we can, and did, take some really big steps. David Vap Group Vice President Oracle Applications Product Development

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  • 2D isometric picking

    - by Bikonja
    I'm trying to implement picking in my isometric 2D game, however, I am failing. First of all, I've searched for a solution and came to several, different equations and even a solution using matrices. I tried implementing every single one, but none of them seem to work for me. The idea is that I have an array of tiles, with each tile having it's x and y coordinates specified (in this simplified example it's by it's position in the array). I'm thinking that the tile (0, 0) should be on the left, (max, 0) on top, (0, max) on the bottom and (max, max) on the right. I came up with this loop for drawing, which googling seems to have verified as the correct solution, as has the rendered scene (ofcourse, it could still be wrong, also, forgive the messy names and stuff, it's just a WIP proof of concept code) // Draw code int col = 0; int row = 0; for (int i = 0; i < nrOfTiles; ++i) { // XOffset and YOffset are currently hardcoded values, but will represent camera offset combined with HUD offset Point tile = IsoToScreen(col, row, TileWidth / 2, TileHeight / 2, XOffset, YOffset); int x = tile.X; int y = tile.Y; spriteBatch.Draw(_tiles[i], new Rectangle(tile.X, tile.Y, TileWidth, TileHeight), Color.White); col++; if (col >= Columns) // Columns is the number of tiles in a single row { col = 0; row++; } } // Get selection overlay location (removed check if selection exists for simplicity sake) Point tile = IsoToScreen(_selectedTile.X, _selectedTile.Y, TileWidth / 2, TileHeight / 2, XOffset, YOffset); spriteBatch.Draw(_selectionTexture, new Rectangle(tile.X, tile.Y, TileWidth, TileHeight), Color.White); // End of draw code public Point IsoToScreen(int isoX, int isoY, int widthHalf, int heightHalf, int xOffset, int yOffset) { Point newPoint = new Point(); newPoint.X = widthHalf * (isoX + isoY) + xOffset; newPoint.Y = heightHalf * (-isoX + isoY) + yOffset; return newPoint; } This code draws the tiles correctly. Now I wanted to do picking to select the tiles. For this, I tried coming up with equations of my own (including reversing the drawing equation) and I tried multiple solutions I found on the internet and none of these solutions worked. Trying out lots of solutions, I came upon one that didn't work, but it seemed like an axis was just inverted. I fiddled around with the equations and somehow managed to get it to actually work (but have no idea why it works), but while it's close, it still doesn't work. I'm not really sure how to describe the behaviour, but it changes the selection at wrong places, while being fairly close (sometimes spot on, sometimes a tile off, I believe never more off than the adjacent tile). This is the code I have for getting which tile coordinates are selected: public Point? ScreenToIso(int screenX, int screenY, int tileHeight, int offsetX, int offsetY) { Point? newPoint = null; int nX = -1; int nY = -1; int tX = screenX - offsetX; int tY = screenY - offsetY; nX = -(tY - tX / 2) / tileHeight; nY = (tY + tX / 2) / tileHeight; newPoint = new Point(nX, nY); return newPoint; } I have no idea why this code is so close, especially considering it doesn't even use the tile width and all my attempts to write an equation myself or use a solution I googled failed. Also, I don't think this code accounts for the area outside the "tile" (the transparent part of the tile image), for which I intend to add a color map, but even if that's true, it's not the problem as the selection sometimes switches on approx 25% or 75% of width or height. I'm thinking I've stumbled upon a wrong path and need to backtrack, but at this point, I'm not sure what to do so I hope someone can shed some light on my error or point me to the right path. It may be worth mentioning that my goal is to not only pick the tile. Each main tile will be divided into 5x5 smaller tiles which won't be drawn seperately from the whole main tile, but they will need to be picked out. I think a color map of a main tile with different colors for different coordinates within the main tile should take care of that though, which would fall within using a color map for the main tile (for the transparent parts of the tile, meaning parts that possibly belong to other tiles).

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  • spliiting code in java-don't know what's wrong [closed]

    - by ???? ?????
    I'm writing a code to split a file into many files with a size specified in the code, and then it will join these parts later. The problem is with the joining code, it doesn't work and I can't figure what is wrong! This is my code: import java.io.*; import java.util.*; public class StupidSplit { static final int Chunk_Size = 10; static int size =0; public static void main(String[] args) throws IOException { String file = "b.txt"; int chunks = DivideFile(file); System.out.print((new File(file)).delete()); System.out.print(JoinFile(file, chunks)); } static boolean JoinFile(String fname, int nChunks) { /* * Joins the chunks together. Chunks have been divided using DivideFile * function so the last part of filename will ".partxxxx" Checks if all * parts are together by matching number of chunks found against * "nChunks", then joins the file otherwise throws an error. */ boolean successful = false; File currentDirectory = new File(System.getProperty("user.dir")); // File[] fileList = currentDirectory.listFiles(); /* populate only the files having extension like "partxxxx" */ List<File> lst = new ArrayList<File>(); // Arrays.sort(fileList); for (File file : fileList) { if (file.isFile()) { String fnm = file.getName(); int lastDot = fnm.lastIndexOf('.'); // add to list which match the name given by "fname" and have //"partxxxx" as extension" if (fnm.substring(0, lastDot).equalsIgnoreCase(fname) && (fnm.substring(lastDot + 1)).substring(0, 4).equals("part")) { lst.add(file); } } } /* * sort the list - it will be sorted by extension only because we have * ensured that list only contains those files that have "fname" and * "part" */ File[] files = (File[]) lst.toArray(new File[0]); Arrays.sort(files); System.out.println("size ="+files.length); System.out.println("hello"); /* Ensure that number of chunks match the length of array */ if (files.length == nChunks-1) { File ofile = new File(fname); FileOutputStream fos; FileInputStream fis; byte[] fileBytes; int bytesRead = 0; try { fos = new FileOutputStream(ofile,true); for (File file : files) { fis = new FileInputStream(file); fileBytes = new byte[(int) file.length()]; bytesRead = fis.read(fileBytes, 0, (int) file.length()); assert(bytesRead == fileBytes.length); assert(bytesRead == (int) file.length()); fos.write(fileBytes); fos.flush(); fileBytes = null; fis.close(); fis = null; } fos.close(); fos = null; } catch (FileNotFoundException fnfe) { System.out.println("Could not find file"); successful = false; return successful; } catch (IOException ioe) { System.out.println("Cannot write to disk"); successful = false; return successful; } /* ensure size of file matches the size given by server */ successful = (ofile.length() == StupidSplit.size) ? true : false; } else { successful = false; } return successful; } static int DivideFile(String fname) { File ifile = new File(fname); FileInputStream fis; String newName; FileOutputStream chunk; //int fileSize = (int) ifile.length(); double fileSize = (double) ifile.length(); //int nChunks = 0, read = 0, readLength = Chunk_Size; int nChunks = 0, read = 0, readLength = Chunk_Size; byte[] byteChunk; try { fis = new FileInputStream(ifile); StupidSplit.size = (int)ifile.length(); while (fileSize > 0) { if (fileSize <= Chunk_Size) { readLength = (int) fileSize; } byteChunk = new byte[readLength]; read = fis.read(byteChunk, 0, readLength); fileSize -= read; assert(read==byteChunk.length); nChunks++; //newName = fname + ".part" + Integer.toString(nChunks - 1); newName = String.format("%s.part%09d", fname, nChunks - 1); chunk = new FileOutputStream(new File(newName)); chunk.write(byteChunk); chunk.flush(); chunk.close(); byteChunk = null; chunk = null; } fis.close(); System.out.println(nChunks); // fis = null; } catch (FileNotFoundException fnfe) { System.out.println("Could not find the given file"); System.exit(-1); } catch (IOException ioe) { System.out .println("Error while creating file chunks. Exiting program"); System.exit(-1); }System.out.println(nChunks); return nChunks; } } }

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  • Using MongoDB with Ruby On Rails and the Mongomapper plugin

    - by Micke
    Hello, i am currently trying to learn Ruby On Rails as i am a long-time PHP developer so i am building my own community like page. I have came pritty far and have made the user models and suchs using MySQL. But then i heard of MongoDB and looked in to it a little bit more and i find it kinda nice. So i have set it up and i am using mongomapper for the connection between rails and MongoDB. And i am now using it for the News page on the site. I also have a profile page for every User which includes their own guestbook so other users can come to their profile and write a little message to them. My thought now is to change the User models from using MySQL to start using MongoDB. I can start by showing how the models for each User is set up. The user model: class User < ActiveRecord::Base has_one :guestbook, :class_name => "User::Guestbook" The Guestbook model model: class User::Guestbook < ActiveRecord::Base belongs_to :user has_many :posts, :class_name => "User::Guestbook::Posts", :foreign_key => "user_id" And then the Guestbook posts model: class User::Guestbook::Posts < ActiveRecord::Base belongs_to :guestbook, :class_name => "User::Guestbook" I have divided it like this for my own convenience but now when i am going to try to migrate to MongoDB i dont know how to make the tables. I would like to have one table for each user and in that table a "column" for all the guestbook entries since MongoDB can have a EmbeddedDocument. I would like to do this so i just have one Table for each user and not like now when i have three tables just to be able to have a guestbook. So my thought is to have it like this: The user model: class User include MongoMapper::Document one :guestbook, :class_name => "User::Guestbook" The Guestbook model model: class User::Guestbook include MongoMapper::EmbeddedDocument belongs_to :user many :posts, :class_name => "User::Guestbook::Posts", :foreign_key => "user_id" And then the Guestbook posts model: class User::Guestbook::Posts include MongoMapper::EmbeddedDocument belongs_to :guestbook, :class_name => "User::Guestbook" But then i can think of one problem.. That when i just want to fetch the user information like a nickname and a birthdate then it will have to fetch all the users guestbook posts. And if each user has like a thousand posts in the guestbook it will get really much to fetch for the system. Or am i wrong? Do you think i should do it any other way? Thanks in advance and sorry if i am hard to understand but i am not so educated in the english language :)

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  • point light illumination using Phong model

    - by Myx
    Hello: I wish to render a scene that contains one box and a point light source using the Phong illumination scheme. The following are the relevant code snippets for my calculation: R3Rgb Phong(R3Scene *scene, R3Ray *ray, R3Intersection *intersection) { R3Rgb radiance; if(intersection->hit == 0) { radiance = scene->background; return radiance; } ... // obtain ambient term ... // this is zero for my test // obtain emissive term ... // this is also zero for my test // for each light in the scene, obtain calculate the diffuse and specular terms R3Rgb intensity_diffuse(0,0,0,1); R3Rgb intensity_specular(0,0,0,1); for(unsigned int i = 0; i < scene->lights.size(); i++) { R3Light *light = scene->Light(i); R3Rgb light_color = LightIntensity(scene->Light(i), intersection->position); R3Vector light_vector = -LightDirection(scene->Light(i), intersection->position); // check if the light is "behind" the surface normal if(normal.Dot(light_vector)<=0) continue; // calculate diffuse reflection if(!Kd.IsBlack()) intensity_diffuse += Kd*normal.Dot(light_vector)*light_color; if(Ks.IsBlack()) continue; // calculate specular reflection ... // this I believe to be irrelevant for the particular test I'm doing } radiance = intensity_diffuse; return radiance; } R3Rgb LightIntensity(R3Light *light, R3Point position) { R3Rgb light_intensity; double distance; double denominator; if(light->type != R3_DIRECTIONAL_LIGHT) { distance = (position-light->position).Length(); denominator = light->constant_attenuation + (light->linear_attenuation*distance) + (light->quadratic_attenuation*distance*distance); } switch(light->type) { ... case R3_POINT_LIGHT: light_intensity = light->color/denominator; break; ... } return light_intensity; } R3Vector LightDirection(R3Light *light, R3Point position) { R3Vector light_direction; switch(light->type) { ... case R3_POINT_LIGHT: light_direction = position - light->position; break; ... } light_direction.Normalize(); return light_direction; } I believe that the error must be somewhere in either LightDirection(...) or LightIntensity(...) functions because when I run my code using a directional light source, I obtain the desired rendered image (thus this leads me to believe that the Phong illumination equation is correct). Also, in Phong(...), when I computed the intensity_diffuse and while debugging, I divided light_color by 10, I was obtaining a resulting image that looked more like what I need. Am I calculating the light_color correctly? Thanks.

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  • JFace ApplicationWindow: createContents isn't working

    - by jasonh
    I'm attempting to create a window that is divided into three parts. A non-resizable header and footer and then a content area that expands to fill the remaining area in the window. To get started, I created the following class: public class MyWindow extends ApplicationWindow { Color white; Font mainFont; Font headerFont; public MyWindow() { super(null); } protected Control createContents(Composite parent) { Display currentDisplay = Display.getCurrent(); white = new Color(currentDisplay, 255, 255, 255); mainFont = new Font(currentDisplay, "Tahoma", 8, 0); headerFont = new Font(currentDisplay, "Tahoma", 16, 0); // Main layout Composites and overall FillLayout Composite container = new Composite(parent, SWT.NO_RADIO_GROUP); Composite header = new Composite(container, SWT.NO_RADIO_GROUP); Composite mainContents = new Composite(container, SWT.NO_RADIO_GROUP);; Composite footer = new Composite(container, SWT.NO_RADIO_GROUP);; FillLayout containerLayout = new FillLayout(SWT.VERTICAL); container.setLayout(containerLayout); // Header Label headerLabel = new Label(header, SWT.LEFT); headerLabel.setText("Header"); headerLabel.setFont(headerFont); // Main contents Label contentsLabel = new Label(mainContents, SWT.CENTER); contentsLabel.setText("Main Content Here"); contentsLabel.setFont(mainFont); // Footer Label footerLabel = new Label(footer, SWT.CENTER); footerLabel.setText("Footer Here"); footerLabel.setFont(mainFont); return container; } public void dispose() { cleanUp(); } @Override protected void finalize() throws Throwable { cleanUp(); super.finalize(); } private void cleanUp() { if (headerFont != null) { headerFont.dispose(); } if (mainFont != null) { mainFont.dispose(); } if (white != null) { white.dispose(); } } } And this results in an empty window when I run it like this: public static void main(String[] args) { MyWindow myWindow = new MyWindow(); myWindow.setBlockOnOpen(true); myWindow.open(); Display.getCurrent().dispose(); } What am I doing wrong that I don't see three labels the way I'm trying to display them? The createContents code is definitely being called, I can step through it in Eclipse in debug mode.

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  • J: Self-reference in bubble sort tacit implementation

    - by Yasir Arsanukaev
    Hello people! Since I'm beginner in J I've decided to solve a simple task using this language, in particular implementing the bubblesort algorithm. I know it's not idiomatically to solve such kind of problem in functional languages, because it's naturally solved using array element transposition in imperative languages like C, rather than constructing modified list in declarative languages. However this is the code I've written: (((<./@(2&{.)), $:@((>./@(2&{.)),2&}.)) ^: (1<#)) ^: # Let's apply it to an array: (((<./@(2&{.)), $:@((>./@(2&{.)),2&}.)) ^: (1<#)) ^: # 5 3 8 7 2 2 3 5 7 8 The thing that confuses me is $: referring to the statement within the outermost parentheses. Help says that: $: denotes the longest verb that contains it. The other book (~ 300 KiB) says: 3+4 7 5*20 100 Symbols like + and * for plus and times in the above phrases are called verbs and represent functions. You may have more than one verb in a J phrase, in which case it is constructed like a sentence in simple English by reading from left to right, that is 4+6%2 means 4 added to whatever follows, namely 6 divided by 2. Let's rewrite my code snippet omitting outermost ()s: ((<./@(2&{.)), $:@((>./@(2&{.)),2&}.)) ^: (1<#) ^: # 5 3 8 7 2 2 3 5 7 8 Reuslts are the same. I couldn't explain myself why this works, why only ((<./@(2&{.)), $:@((>./@(2&{.)),2&}.)) ^: (1<#) is treated as the longest verb for $: but not the whole expression ((<./@(2&{.)), $:@((>./@(2&{.)),2&}.)) ^: (1<#) ^: # and not just (<./@(2&{.)), $:@((>./@(2&{.)),2&}.), because if ((<./@(2&{.)), $:@((>./@(2&{.)),2&}.)) ^: (1<#) is a verb, it should also form another verb after conjunction with #, i. e. one might treat the whole sentence (first snippet) as a verb. Probably there's some limit for the verb length limited by one conjunction. Look at the following code (from here): factorial =: (* factorial@<:) ^: (1&<) factorial 4 24 factorial within expression refers to the whole function, i. e. (* factorial@<:) ^: (1&<). Following this example I've used a function name instead of $:: bubblesort =: (((<./@(2&{.)), bubblesort@((>./@(2&{.)),2&}.)) ^: (1<#)) ^: # bubblesort 5 3 8 7 2 2 3 5 7 8 I expected bubblesort to refer to the whole function, but it doesn't seem true for me since the result is correct. Also I'd like to see other implementations if you have ones, even slightly refactored. Thanks.

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  • CodeGolf: Brothers

    - by John McClane
    Hi guys, I just finished participating in the 2009 ACM ICPC Programming Conest in the Latinamerican Finals. These questions were for Brazil, Bolivia, Chile, etc. My team and I could only finish two questions out of the eleven (not bad I think for the first try). Here's one we could finish. I'm curious to seeing any variations to the code. The question in full: ps: These questions can also be found on the official ICPC website available to everyone. In the land of ACM ruled a greeat king who became obsessed with order. The kingdom had a rectangular form, and the king divided the territory into a grid of small rectangular counties. Before dying the king distributed the counties among his sons. The king was unaware of the rivalries between his sons: The first heir hated the second but not the rest, the second hated the third but not the rest, and so on...Finally, the last heir hated the first heir, but not the other heirs. As soon as the king died, the strange rivaly among the King's sons sparked off a generalized war in the kingdom. Attacks only took place between pairs of adjacent counties (adjacent counties are those that share one vertical or horizontal border). A county X attacked an adjacent county Y whenever X hated Y. The attacked county was always conquered. All attacks where carried out simultanously and a set of simultanous attacks was called a battle. After a certain number of battles, the surviving sons made a truce and never battled again. For example if the king had three sons, named 0, 1 and 2, the figure below shows what happens in the first battle for a given initial land distribution: INPUT The input contains several test cases. The first line of a test case contains four integers, N, R, C and K. N - The number of heirs (2 <= N <= 100) R and C - The dimensions of the land. (2 <= R,C <= 100) K - Number of battles that are going to take place. (1 <= K <= 100) Heirs are identified by sequential integers starting from zero. Each of the next R lines contains C integers HeirIdentificationNumber (saying what heir owns this land) separated by single spaces. This is to layout the initial land. The last test case is a line separated by four zeroes separated by single spaces. (To exit the program so to speak) Output For each test case your program must print R lines with C integers each, separated by single spaces in the same format as the input, representing the land distribution after all battles. Sample Input: Sample Output: 3 4 4 3 2 2 2 0 0 1 2 0 2 1 0 1 1 0 2 0 2 2 2 0 0 1 2 0 0 2 0 0 0 1 2 2 Another example: Sample Input: Sample Output: 4 2 3 4 1 0 3 1 0 3 2 1 2 2 1 2

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