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• Convert python script to binary executable

  - by Werner

  Hi, I wrote a number crunching python code. The calculations involved can take hours. Is it possible somehow to compile it to binary? Thanks

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• Best javascript i18n techniques / AJAX - dates, times, numbers, currency

  - by John Vasileff

  For server side generated content, i18n support is usually pretty easy, for example, Java provides extensive i18n support. But, these rich server side libraries are not available within the browser, which causes a problem when trying to format data client side (AJAX style.) What javascript libraries and techniques are recommended for performing client side formatting and time-zone calculations? Also - beyond simple client side formatting, how can consistency be achieved when performing both server side and client side formatting?

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• Creating Playlist for playing video

  - by Timmi

  Hi, I am developing a play list for playing video. Which event get fired when an item is added to listview. I need to do some calculations with the files added to the playlist.How can we make a playlist. Please help me with code examples Thanks,

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• Sending floating point values between processes with pipes in C

  - by Alex

  Is there a standard way of sending floating point values from a child process to a parent process in C. I have a some calculations where I want to fork a process, then have the child do some busy work, the parent do something else, and then the child send its values (which are doubles) back to the parent (presumably through a pipe). Clearly the parent could parse the stream, but I'm just wondering if there's a cleaner way?

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• How to host a RESTful C# webservice and test it.

  - by Debby

  Hi, I need to create a RESTful webservice in C#. This is what I have right now: namespace WebService { [ServiceContract] public interface IService { [OperationContract(Name="Add")] [WebGet(UriTemplate = "/")] int Add(); } public class Service:IService { public int Add() { // do some calculations and return result return res; } } } Now, my question is How do i host this service at a location say (http://localhost/TestService) and how can i test the service in console application client?

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• PHP Forms checkbox calculation

  - by Sef

  Hello, I am trying to perform some calculations with a form but every time i try to work with checkboxes it goes wrong. The checkboxes are beign set on value 1 in the form itselff and are being checked if there checked or not. $verdieping = isset($_POST["verdieping"]) ? $_POST["verdieping"] : 0; $telefoon = isset($_POST["telefoon"]) ? $_POST["telefoon"] : 0; $netwerk = isset($_POST["netwerk"]) ? $_POST["netwerk"] : 0; When i try to do calculations every works expect for the options with the checkboxes. When both checkboxes (telefoon & netwerk) are selected the value should be 30. If only one is selected the value should be 20. But no mather what i have tried to write down it always give problem, and it always uses 20, never the value 30. How do i solve this problem? Or suppose i am writing the syntax all wrong to lay conditions to a calculation? Any input appreciated. $standnaam = $_SESSION["standnaam"]; $oppervlakte = $_SESSION["oppervlakte"]; $verdieping = $_SESSION["verdieping"]; $telefoon = $_SESSION["telefoon"]; $netwerk = $_SESSION["netwerk"]; if ($oppervlakte <= 10) $tarief = 100; if ($oppervlakte > 10 && $oppervlakte <= 20) $tarief = 90; if ($oppervlakte > 20) $tarief = 80; if($verdieping == 1) { $prijsVerdieping = $oppervlakte * 120; } else { $prijsVerdieping = 0; } if(($telefoon == 1) && ($netwerk == 1)) { $prijsCom = 30; // never get this value, it always uses 20 } if(($telefoon == 1) || ($netwerk == 1)) { $prijsCom = 20; } $prijsOpp = $tarief * $oppervlakte; // works $totalePrijs = $prijsOpp + $prijsVerdieping + $prijsCom; //prijsCom value is always wrong Regards.

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• ASP page get current focused control

  - by Breander

  So what I have is a bunch of dynamically created textboxs that when the user enters some data and either tabs out or clicks out some calculations are done. After the page posts back control focus is lost. What I need is to be able to set focus back to the control that was tabbed to or clicked into not the control that data was entered into.

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• Efficient way to store a graph for calculation in Hadoop

  - by user337499

  I am currently trying to perform calculations like clustering coefficient on huge graphs with the help of Hadoop. Therefore I need an efficient way to store the graph in a way that I can easily access nodes, their neighbors and the neighbors' neighbors. The graph is quite sparse and stored in a huge tab separated file where the first field is the node from which an edge goes to the second node in field two. Thanks in advance!

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• PHP: Check if it has been one week since timestamp

  - by Rudi

  Hi guys, Let's assume: $time = '2010-05-17 02:49:30' // (retrieved from MySQL TIMESTAMP field) How do I do the following in PHP: 1) Check if it has been more than one week since this time has passed? 2) Assuming "false" on (1), find out how much more time until the one week mark, rounded to days and hours remaining. I know this is pretty straightforward, but it uses a very specific syntax. Having never played with time calculations before, I'd appreciate some guidance. Thanks!

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• Why does this eval not work in Ruby

  - by Anil

  Can you explain this? I want to eval values and calculations from two different sources. One source gives me the following info(programmatically): 'a = 2' The second source gives me this expression to evaluate: 'a + 3' This works: a = 2 eval 'a + 3' This also works: eval 'a = 2; a + 3' But what I really need is this, and it doesn't work: eval 'a = 2' eval 'a + 3' I would like to understand the difference, and how can I make the last option work. Thanks for your help.

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• Is a string formatter that pulls variables from its calling scope bad practice?

  - by Eric

  I have some code that does an awful lot of string formatting, Often, I end up with code along the lines of: "...".format(x=x, y=y, z=z, foo=foo, ...) Where I'm trying to interpolate a large number of variables into a large string. Is there a good reason not to write a function like this that uses the inspect module to find variables to interpolate? import inspect def interpolate(s): return s.format(**inspect.currentframe().f_back.f_locals) def generateTheString(x): y = foo(x) z = x + y # more calculations go here return interpolate("{x}, {y}, {z}")

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• Adding characters to string (input field)

  - by Zaps

  Hi, I have a text box where the value the result of a calculation carried out in jQuery. What I would like to do, using jQuery, is to display brackets around the number in the text box if the number is negative. The number may be used again later so I would then have to remove the brackets so further calculations could be carried out. Any ideas as to how I could implement this? Thanks Zaps

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• Are there any "gotchas" to watch for in using a Class (object) within itself?

  - by Clay Nichols

  I've got a Registry class and there are a few Registry values that I want to access from within that Registry class. (There is a bit of a calculation with these values so I thought I'd just put all that code right in the Registry Class itself). So we might have something within our RegistryRoutine.cls like: Function GetMyValue() as integer Dim R as new RegistryRoutine <calculations> GetMyValue=R.GetRegisetryValue (HKEY, key, value, etc.) End Function

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• Personal Development : Time, Planning , Repairs & Maintenance

  - by Rajesh Pillai

  Personal Development : Time, Planning, Repairs & Maintenance These are just my thoughts, but some you may find something interesting in it. Please think over it. We may know many things, but still we always keeps procrastinating it. I have written this as I have heard many people coming back and saying they don’t have time to do things they like. These are my thoughts buy may be useful to someone else too. Certain things in life needs periodic repairs and maintenance. To cite some examples , your CAR, your HOUSE, your personal laptop/desktop, your health etc. Likewise there are certain other things in professional life that requires repair/ maintenance /or some kind of polishing, so that you always stay on top of it. But they are not always obvious. Some of them are - Improving your communication skills - Increasing your vocabulary - Upgrading your technical skills - Pursuing your hobby - Increasing your knowledge/awareness etc… etc… And then there are certain things that we are always short of…. one is TIME. We all know TIME is one of the most precious things in life and yet we all are very miserable at managing it. Remember you can only manage it and not control it. You can only control which you own or which you create. In theory time is infinite. So, there should be abundant of it. But remember one thing, you know this, it’s not reversible. Once it has elapsed you cannot live it again. Think over it. So, how do find that golden 25th hour every day. To find the 25th hour you need to reflect back on your current daily activities. Analyze them and see where you are spending most of your time and is it really important. Even the 8 hours that you spent in the office, is it spent fruitfully. At the end of the day is the 8 precious hour that you spent was worth it. Just reflect back on your activities. Did you learn something? If yes did you make a point to NOTE IT. If you didn’t NOTED it then was the time you spent really worth it. Just ponder over it. Some calculations of your daily activities where most of the time is spent. Let’s start (in no particular order though) - Sleep (6.5 hours) [Remember you only require 6 good hours of sleep every day]. Some may thing it is 8, but it’s a myth.   o To achive 6 hours of sleep and be in good health you can practice 15 minutes of daily meditation. So effectively you can    round it to 6.5 hours. - Morning chores(2 hours) : Some may need to prepare breakfast and all other things. - Office commuting (avg. to and fro 3 hours) - Office Work (avg 9.5 hours) Total Hours: 21 hours effective time which is spent irrespective of what you do. There may be some variations here and there. Still you have 3 hours EXTRA. Where do these 3 hours go? If you can find it, then you may get that golden 25th hour out of these 3 hours. Let’s discount 2 hours for contingencies, still you have 1 hour with you. If you can’t find it then you are living a direction less life. As you can see, the 25th Hour lies within the 24 hours of the day. It’s upto each one of us to find and make use of it. Now what can you do with that 25th hour i.e. 1 hour extra of your life. Imagine the possibility. Again some calculations 1 hour daily * 30 days = 30 hours every month 30 hours pm * 12 month = 360 hours every year. 360 hours every year seems very promising. Let’s add some contingencies, say, let’s be optimistic and say 50 % contingency. Still you have 180 hours every year. That leaves with 30 minutes every day of extra time. That’s hell a lot of time, if you could manage it. These may sound like a high talk [yes, it is, unless you apply these simple rules and rationalize your everyday living and stop procrastinating]. NOTE: I haven’t taken weekend, holidays and leaves into account. So, that leaves us with a lot of buffer time. You can meet family friends, relatives, other tasks, and yet have these 180 pure hours of joy every year. Do whatever you want to do with it. So, how important is this 180 hours per year to you? Just think over it. You may use it the way you like - 50 hours [pursue your hobby like drawing, crafting, learn dance, learn juggling, learn swimming, travelling hmm.. anything you like doing and you didn’t had time to do it.] - 30 hours you can learn a new programming language or technology (i.e. you can get comfortable with it) - 50 hours [improve existing skills] - 20 hours [improve you communication skill]. Do some light reading. - 30 hours [YOU DECIDE WHAT TO DO]? So, if you had done this for one year you would have learnt a new programming language, upgraded existing skills, improved you communication etc.. If you had done this for two years.. imagine the level of personal development or growth which you may have attained….. If you had done this for three years….. NOW I think I don’t need to mention this… So, you still have TIME, as they say TIME is infinite. So, make judicious use of this precious thing. And never ever comeback saying “I don’t have time”. So, if you are RICH in TIME, everything else will be automatically taken care of, as those things may just be a byproduct of how you spend your time… So, happy TIMING your TIME everyday.

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• How John Got 15x Improvement Without Really Trying

  - by rchrd

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

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• Why is lassoing ink in OneNote so slow in general? Doesn't anyone care?

  - by GuoLiang Oon

  Now I understand that this is especially an (known) issue in the OneNote 2013 preview and that it will probably be fixed in the final release. But lassoing in OneNote 2010 was no sprightly affair either. I'm just perplexed really, why on earth is there such an issue? Is lassoing intrinsically computationally expensive? OneNote would be soooooooooooo much more useful if there's no lasso lag. And doing a laggy lasso on tablet pcs with weak processors is just so much worse. Or do most folks just don't use the lasso feature much? I use it primarily to shrink intermediate calculations for future retrieval.

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• Excel Conditional Formatting Multiple Data Bars and Data Icons in one cell

  - by wbeard52

  I am using Excel 2007 on a windows machine. I am attempting to place one data bar and one data icon into a cell under the conditional formatting. The issue is that I don't really want to have data icons or data bars for cells that have dates in the future and I only want to have data icons for dates in the at least one month in the past. This is what I have: This is what I want: I am using the EOMONTH function to determine the last day of the month for the conditional formatting calculations. For the data bar the formula is =EOMONTH(Now(), 4) and =EOMONTH(Now(), -1). The data icons formulas are =EOMONTH(Now(), -1) and =EOMONTH(Now(), -2) Is there a way in Excel 2007 to get rid of the data icons for all the dates in the future and lose the data bars when the date has past. Thanks

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• Predictive vs Least Connection Load Balancing Techniques

  - by Mani

  I have a windows based desktop application that communicates via TCP to the application servers. (windows 2003). No sticky sessions between client calls. We have exactly 2 servers to load balance and we are thinking to use a F5 hardware NLB. The application is a heavy load types, doing not much bussiness logic in the services but retrieving quite a big amount of data at most of the times. May be on an average 5000 to 10000 records at all times. Used mainly for storing and retirieving data and no special processing of data or calculations running on the server side. I am favouring 'predictive' considering my services take a while at times to return data and hence tracking the feedback would yield some better routing as in predictive. I am not sure if the given data is sufficient enough to suggest some ideas but considering these, what would be some suggestions\things to consider\best between Predictive and Least Connections ? Thanks.

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• How can I exceed the 60% Memory Limit of IIS7

  - by evilknot

  Pardon if this is more stackoverflow vs. serverfault. It seems to be on the border. We have an application that caches a large amount of product data for an e-commerce application using ASP.NET caching. This is a dictionary object with 65K elements, and our calculations put the object's size at ~10GB. Problem: The amount of memory the object consumes seems to be far in excess of our 10GB calculation. BIGGEST CONCERN: We can't seem to use over 60% of the 32GB in the server. What we've tried so far: In machine.config/system.web (sf doesn't allow the tags, pardon the formatting): processModel autoConfig="true" memoryLimit="80" In web.config/system.web/caching/cache (sf doesn't allow the tags, pardon the formatting): privateBytesLimit = "20000000000" (and 0, the default of course) percentagePhysicalMemoryUsedLimit = "90" Environment: Windows 2008R2 x64 32GB RAM IIS7 Nothing seems to allow us to exceed the 60% value. See attached screenshot of taskman.

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• excel 2010 format and input issue

  - by Craig Gunn

  I have completed a very complex Excel spreadsheet with a lot of equations, except ... I forgot to include September I have Jan through Dec, all the months, except the calculations for September. Of course all the equations are currently perfect for the data that's here. How do I add a whole new column without ruining the previous equations? PS: tomorrow is my holidays and I have to go to work to finish this table, so bad. would really appreciate some kind expertise :) cheers craig.

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• Excel 2007: how to work out percentages of groups (top 10% of...)

  - by Mike

  I've recently read the following paragraph, and wondered: how you would organise the data (possibly Column A = country, Column B = salary, Column C = tax paid) but what formulas/calculations are used to work out these types of % figures: In country Y the top 0.5% of taxpayers pay 17% of total income tax. In country X the top 0.1% of taxpayers pay 8% of total income tax and in country Z, the top 1% pay about 40% of total federal income tax. I've gone through the help files and searched within Excel websites but I'm struggling to find an answer. %'s interest and trouble me... Any pointers or examples very welcome. Thanks Mike

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• Microsoft Excel 2007 constantly calculating sheets

  - by acseven

  I believe this happening for two weeks now: Excel 2007 (on Windows XP) is acting funny on my computer; any medium sized sheet with some formulas in it takes a significant amount of time recalculating. I can see this because the "calculating: 2 processors xx%" message was almost unseen before and now it appears on most operations like calculating a formula (on one cell), saving, previewing, etc. If the sheet is complex (lots of formulas) I have to disable automatic calculations because excel renders as unusable - it hangs for a really long time, measureable in minutes. Any idea on what may be causing this? ps: this is a Core2 Duo computer with 2 Gb of RAM

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• Making Sure Which Partition to Choose with Linux Dual Boot?

  - by user128360

  In the Linux Mint 12 LXDE the partitions are listed as nsd 1, 2, 3, 4, though I have a Windows 8 CP installation on one of the two partitions on the single hard drive. The space usage is differing in both system calculations, though still relateable. Where the partition is at around 20 GB usage in Windows 8 it will be at around 24 GB in the Linux installation menu. I am just wondering is there a certain way to choose the right partition? Also in the drop down menu regarding the boot loader, there are multiple options, which one would be the one to be chosen in this case? What about the system-reserve partition of Windows 7 (the one I am trying to overwrite)? What is happening with that?

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• Calculate minimum ext3 partition size for certain amount of data

  - by Daniel Beck

  These following ext3 partitions contain identical data. As we can see, the larger the partition size, the more space is required for the same files: Filesystem 1K-blocks Used Available Use% Mounted on /dev/loop11 3965777 561064 3199964 15% [...] /dev/loop19 573029 543843 29186 95% [...] Filesystem Size Used Avail Use% Mounted on /dev/loop11 3.8G 548M 3.1G 15% [...] /dev/loop19 560M 532M 29M 95% [...] Filesystem Inodes IUsed IFree IUse% Mounted on /dev/loop11 1024000 1656 1022344 1% [...] /dev/loop19 1024000 1656 1022344 1% [...] I start with a partition of fixed size that possibly wasted a lot of space and I want to create a partition that is able to hold that data but with (almost) minimal size. How can I reliably calculate that minimal partition size needed for storing a certain amount of data? The amount of data changes over time, and I need to automate these calculations.

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• 912 stream processor available in OpenCL

  - by tugrul büyükisik

  I am thinking of assembling this system: AMD CPU (A8-3870 APU which has Radeon HD 6550D inside: 400 stream processors:xxx GFLOPS) nearly 110$ AMD Graphics card: HD 7750 (512 stream processors:819 GFLOPS peak performance) nearly 170$ Appropriate ram (1600MHz bus) Mainboard What GFLOPS level can I reach as a stable mode with using OpenCL and similar programs? Can I use all 912 stream processors at the same time? I am not trying to do a VS question. I need to know what could be better for scientific computing (%75 of the time) and gaming (%25 of the time) because I have a low budget. With "scientific calculations" I mean fluid dynamics/solid state physics simulating; with games I mean those that need openCL and PhysX.

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