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  • how to re-use a sprite in cocos2d-x

    - by zinking
    some times it takes time to create the sprite structures in the scene, I might need to setup structures inside this sprite to meet requirement, thus I would hope to reuse such structures with the game again and again. I tried that, remove the child from parent, detach it from parent , clean parent with the sprite. but when I try to add the sprite to another scene, it's just wont pass the assertion that the sprite already have parent did I miss some step ? add an example: I have a sprite A which involves of quite a few steps to construct, so I used it in scene A layer A, and then I want to use it in scene A layer B, scene B layer A1 etc..... generally speaking I don't want to reconstruct the sprte again.

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  • JNA and ZBar(library for bar code reader)

    - by user219120
    I'm creating Java Interface with JNA for ZBar(library for bar code reader). In JNA, structures in C are needed to declare. For example:: // In C typedef struct { char* id; char* name; int age; char* sectionId } EMPLOYEE; to // In Java with JNA public static class Employee extends Structure { // com.sun.jna.Structure String id; String name; int age; String sectionId; } But in ZBar, structures have no members. For example:: // zbar-0.10/include/zbar.h // line:1009-1011 struct zbar_image_scanner_s; /** opaque image scanner object. */ typedef struct zbar_image_scanner_s zbar_image_scanner_t; That doesn't declare size or members of the structures. How can I write interfaces for these structures in JNA?

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  • Fast exchange of data between unmanaged code and managed code

    - by vizcaynot
    Hello: Without using p/invoke, from a C++/CLI I have succeeded in integrating various methods of a DLL library from a third party built in C. One of these methods retrieves information from a database and stores it in different structures. The C++/CLI program I wrote reads those structures and stores them in a List<, which is then returned to the corresponding reading and use of an application programmed completely in C#. I understand that the double handling of data (first, filling in several structures and then, filling all of these structures into a list<) may generate an unnecessary overload, at which point I wish C++/CLI had the keyword "yield". Depending on the above scenario, do you have recommendations to avoid or reduce this overload? Thanks.

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  • Sort Strings by first letter [C]

    - by Blackbinary
    I have a program which places structures in a linked list based on the 'name' they have stored in them. To find their place in the list, i need to figure out if the name im inserting is earlier or later in the alphabet then those in the structures beside it. The names are inside the structures, which i have access to. I don't need a full comaparison if that is more work, even just the first letter is fine. Thanks for the help!

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  • locking on dictionary of structs not working between 2 threads?

    - by Rancur3p1c
    C#, .Net2.0, XP, Zen I have 2 threads accessing a shared dictionary of structures, each thread via an event. At the beginning of the event I lock the dictionary, remove some structures, and exit the lock+event. Yet somehow the 2nd thread|event is finding some of the removed structures. Conceptually I must be doing something wrong for this to be happening? I thought locking was supposed to make it thread safe?

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  • Breaking down CS courses for freshmen

    - by Avinash
    I'm a student putting together a slide geared towards freshmen level students who are trying to understand what the importance of various classes in the CS curriculum are. Would it be safe to say that this list is fairly accurate? Data structures: how to store stuff in programs Discrete math: how to think logically Bits & bytes: how to ‘speak’ the machine’s language Advanced data structures: how to store stuff in more ways Algorithms: how to compute things efficiently Operating systems: how to do manage different processes/threads Thanks!

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  • Upgrading 10.04LTS -> 10.10 using custom sources

    - by Boatzart
    I'm trying to upgrade to 10.10 from 10.04 LTS using a custom sources.list file that points to an unofficial mirror*. The mirror does have maverick, but I get the following output when upgrading: boatzart@somecomputer: > sudo do-release-upgrade Checking for a new ubuntu release Done Upgrade tool signature Done Upgrade tool Done downloading extracting 'maverick.tar.gz' authenticate 'maverick.tar.gz' against 'maverick.tar.gz.gpg' tar: Removing leading `/' from member names Reading cache Checking package manager Reading package lists... Done Building dependency tree Reading state information... Done Building data structures... Done Reading package lists... Done Building dependency tree Reading state information... Done Building data structures... Done Updating repository information WARNING: Failed to read mirror file No valid mirror found While scanning your repository information no mirror entry for the upgrade was found. This can happen if you run a internal mirror or if the mirror information is out of date. Do you want to rewrite your 'sources.list' file anyway? If you choose 'Yes' here it will update all 'lucid' to 'maverick' entries. If you select 'No' the upgrade will cancel. Continue [yN] y WARNING: Failed to read mirror file 96% [Working] Checking package manager Reading package lists... Done Building dependency tree Reading state information... Done Building data structures... Done Calculating the changes Calculating the changes Could not calculate the upgrade An unresolvable problem occurred while calculating the upgrade: The package 'update-manager-kde' is marked for removal but it is in the removal blacklist. This can be caused by: * Upgrading to a pre-release version of Ubuntu * Running the current pre-release version of Ubuntu * Unofficial software packages not provided by Ubuntu If none of this applies, then please report this bug against the 'update-manager' package and include the files in /var/log/dist-upgrade/ in the bug report. Restoring original system state Aborting Reading package lists... Done Building dependency tree Reading state information... Done Building data structures... Done Here is the relevant section from /var/log/dist-upgrade/main.log: 2010-11-18 14:05:52,117 DEBUG The package 'update-manager-kde' is marked for removal but it's in the removal blacklist 2010-11-18 14:05:52,136 ERROR Dist-upgrade failed: 'The package 'update-manager-kde' is marked for removal but it is in the removal blacklist.' 2010-11-18 14:05:52,136 DEBUG abort called *I'm located inside of USC, and for some crazy reason any sustained downloads to anywhere outside of the University are throttled down to 5kbps inside of my lab. Because of this I need to use the following sources.list: deb http://mirrors.usc.edu/pub/linux/distributions/ubuntu/ lucid main restricted universe multiverse deb http://mirrors.usc.edu/pub/linux/distributions/ubuntu/ lucid-updates main restricted universe multiverse deb http://mirrors.usc.edu/pub/linux/distributions/ubuntu/ lucid-backports main restricted universe multiverse deb http://mirrors.usc.edu/pub/linux/distributions/ubuntu/ lucid-security main restricted universe multiverse I've tried adding four more entries to the sources.list with s/lucid/maverick/ but that didn't help. Does anyone know how to fix this? Thanks!

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  • non-volatile virtual memory for C++ containers

    - by arieberman
    Is there a virtual memory management process that would allow a program to use the standard container structures and classes, but retain these structures and their data when the program is not running (or being used), for use by the program at a later time? This should be possible, but can it be done without changing the source code and its (container) declarations? Is there a standard way of doing this?

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  • .NET Developer Basics – Recursive Algorithms

    Recursion can be a powerful programming technique when used wisely. Some data structures such as tree structures lend themselves far more easily to manipulation by recursive techniques. As it is also a classic Computer Science problem, it is often used in technical interviews to probe a candidate's grounding in basic programming techniques. Whatever the reason, it is well worth brushing up one's understanding with Damon's introduction to Recursion.

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  • Use Thread-local Storage to Reduce Synchronization

    Synchronization is often an expensive operation that can limit the performance of a multithreaded program. Using thread-local data structures instead of data structures shared by the threads can reduce synchronization in certain cases, allowing a program to run faster.

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  • How John Got 15x Improvement Without Really Trying

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

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  • Looking for a lock-free RT-safe single-reader single-writer structure

    - by moala
    Hi, I'm looking for a lock-free design conforming to these requisites: a single writer writes into a structure and a single reader reads from this structure (this structure exists already and is safe for simultaneous read/write) but at some time, the structure needs to be changed by the writer, which then initialises, switches and writes into a new structure (of the same type but with new content) and at the next time the reader reads, it switches to this new structure (if the writer multiply switches to a new lock-free structure, the reader discards these structures, ignoring their data). The structures must be reused, i.e. no heap memory allocation/free is allowed during write/read/switch operation, for RT purposes. I have currently implemented a ringbuffer containing multiple instances of these structures; but this implementation suffers from the fact that when the writer has used all the structures present in the ringbuffer, there is no more place to change from structure... But the rest of the ringbuffer contains some data which don't have to be read by the reader but can't be re-used by the writer. As a consequence, the ringbuffer does not fit this purpose. Any idea (name or pseudo-implementation) of a lock-free design? Thanks for having considered this problem.

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  • What is the underlying reason for not being able to put arrays of pointers in unsafe structs in C#?

    - by cons
    If one could put an array of pointers to child structs inside unsafe structs in C# like one could in C, constructing complex data structures without the overhead of having one object per node would be a lot easier and less of a time sink, as well as syntactically cleaner and much more readable. Is there a deep architectural reason why fixed arrays inside unsafe structs are only allowed to be composed of "value types" and not pointers? I assume only having explicitly named pointers inside structs must be a deliberate decision to weaken the language, but I can't find any documentation about why this is so, or the reasoning for not allowing pointer arrays inside structs, since I would assume the garbage collector shouldn't care what is going on in structs marked as unsafe. Digital Mars' D handles structs and pointers elegantly in comparison, and I'm missing not being able to rapidly develop succinct data structures; by making references abstract in C# a lot of power seems to have been removed from the language, even though pointers are still there at least in a marketing sense. Maybe I'm wrong to expect languages to become more powerful at representing complex data structures efficiently over time.

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  • Annotation based data structure visualization - are there similar tools out there?

    - by Helper Method
    For a project at university I plan to build an annotation based tool to visualize/play around with data structures. Here's my idea: Students which want to try out their self-written data structures need to: mark the type of their data structures using some sort of marker annotation e.g. @List public class MyList { ... } so that I know how to represent the data structure need to provide an iterator so that I can retrieve the elements in the right order need to annotate methods for insertion and removal, e.g. @add public boolean insert(E e) { ... } so that I can "bind" that method to some button. Do similar applications exist? I googled a little bit around but didn't find anything like that.

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  • C: using a lot of structs can make a program slow?

    - by nunos
    I am coding a breakout clone. I had one version in which I only had one level deep of structures. This version runs at 70 fps. For more clarity in the code I decided the code should have more abstractions and created more structs. Most of the times I have two two three level deep of structures. This version runs at 30 fps. Since there are some other differences besides the structures, I ask you: Does using a lot of structs in C can slow down the code significantly? Thanks.

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  • Does this make any sense (Apple-documentation)?

    - by Paperflyer
    Here is a snippet of the official Apple Documentation of AudioBufferList (Core Audio Data Types Reference) AudioBufferList Holds a variable length array of AudioBuffer structures. struct AudioBufferList { UInt32 mNumberBuffers; AudioBuffer mBuffers[1]; }; typedef struct AudioBufferList AudioBufferList; Fields mNumberBuffers The number of AudioBuffer structures in the mBuffers array. mBuffers A variable length array of AudioBuffer structures. If mBuffers is defined as AudioBuffer[1] it is not of variable length and thus mNumberBuffers is implicitly defined as 1. Do I miss something here or is this just nonsense?

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  • SAPPHIRE 2012 : « 80 % de nos clients sont des PME », SAP revient sur l'évolution de ses solutions Cloud pour répondre à leurs besoins

    SAPPHIRE 2012 : « 80 % de nos clients sont des PME » SAP revient sur ses solutions Cloud et ses évolutions pour répondre à leurs besoins SAP n'a pas l'image d'un éditeur qui s'adresse aux petites entreprises. Et pourtant, 80% de ses clients sont des PME. Le chiffre est avancé par Chris Horak, vice-président en charge des solutions Cloud, dans un entretien à Developpez.com lors du SAPPHIRE NOW 2012. Il est vrai que la catégorie PME regroupe des structures diverses allant du petit au très gros. Une solution comme Business One, qui compte aujourd'hui 30.000 clients, vise cependant bien les plus petites structures. Adaptée pour les entreprises ayant entre...

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  • How does I/O work for large graph databases?

    - by tjb1982
    I should preface this by saying that I'm mostly a front end web developer, trained as a musician, but over the past few years I've been getting more and more into computer science. So one idea I have as a fun toy project to learn about data structures and C programming was to design and implement my own very simple database that would manage an adjacency list of posts. I don't want SQL (maybe I'll do my own query language? I'm just having fun). It should support ACID. It should be capable of storing 1TB let's say. So with that, I was trying to think of how a database even stores data, without regard to data structures necessarily. I'm working on linux, and I've read that in that world "everything is a file," including hardware (like /dev/*), so I think that that obviously has to apply to a database, too, and it clearly does--whether it's MySQL or PostgreSQL or Neo4j, the database itself is a collection of files you can see in the filesystem. That said, there would come a point in scale where loading the entire database into primary memory just wouldn't work, so it doesn't make sense to design it with that mindset (I assume). However, reading from secondary memory would be much slower and regardless some portion of the database has to be in primary memory in order for you to be able to do anything with it. I read this post: Why use a database instead of just saving your data to disk? And I found it difficult to understand how other databases, like SQLite or Neo4j, read and write from secondary memory and are still very fast (faster, it would seem, than simply writing files to the filesystem as the above question suggests). It seems the key is indexing. But even indexes need to be stored in secondary memory. They are inherently smaller than the database itself, but indexes in a very large database might be prohibitively large, too. So my question is how is I/O generally done with large databases like the one I described above that would be at least 1TB storing a big adjacency list? If indexing is more or less the answer, how exactly does indexing work--what data structures should be involved?

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  • Azure Blob storage defrag

    - by kaleidoscope
    The Blob Storage is really handy for storing temporary data structures during a scaled-out distributed processing. Yet, the lifespan of those data structures should not exceed the one of the underlying operation, otherwise clutter and dead data could potentially start filling up your Blob Storage Temporary data in cloud computing is very similar to memory collection in object oriented languages, when it's not done automatically by the framework, temp data tends to leak. In particular, in cloud computing,  it's pretty easy to end up with storage leaks due to: Collection omission. App crash. Service interruption. All those events cause garbage to accumulate into your Blob Storage. Then, it must be noted that for most cloud apps, I/O costs are usually predominant compared to pure storage costs. Enumerating through your whole Blob Storage to clean the garbage is likely to be an expensive solution. Lokesh, M

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  • Comprehensive system for documentation and handoff of developer project

    - by Uzumaki Naruto
    I work on a technology team that typically develops projects for a period of time, and then hands off to other groups for long-term maintenance and improvements. My team currently uses ad hoc methods of handing off documentations, such as diagrams, API references, etc. Is there a open source solution (or even proprietary one) that enables us to manage: Infrastructure/architecture/software diagrams API documentation Directory structures/file structures Overall documentation summaries in one place? E.g., instead of using multiple systems like Swagger, Wikis, etc. - is there a solution that can seamlessly combine all of these? And enable us to generate a package including all 4 key items with one click to hand off to other teams.

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  • Is nesting types considered bad practice?

    - by Rob Z
    As noted by the title, is nesting types (e.g. enumerated types or structures in a class) considered bad practice or not? When you run Code Analysis in Visual Studio it returns the following message which implies it is: Warning 34 CA1034 : Microsoft.Design : Do not nest type 'ClassName.StructueName'. Alternatively, change its accessibility so that it is not externally visible. However, when I follow the recommendation of the Code Analysis I find that there tend to be a lot of structures and enumerated types floating around in the application that might only apply to a single class or would only be used with that class. As such, would it be appropriate to nest the type sin that case, or is there a better way of doing it?

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  • Does anyone have a good example/sample of "goto" spaghetti code? [closed]

    - by ArtB
    I've read a lot about how GoTo was considered harmful and removed for other control structures that were more intuitive. Does anyone have a good example / sample of goto spaghetti code? Preferrably, the sample code should be difficult to follow, but realtively easy when rewritten into more conventional control structures. I know I could try to write you some of my own, but I've never really used goto and don't think I could due justice to the headaches its abuse can lead to. I want this for didactic purposes to train junior developers on what to avoid. Mainly, to point to illustrate how OOP is taking the same idea to next logical consequence. EDIT: by good example I mean code that is terrible to read and abuses it, rather than code that uses goto for reasonable optimization

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  • 3D RTS pathfinding

    - by xcrypt
    I understand the A* algorithm, but I have some trouble doing it in 3D to suit the needs of my RTS Basically, in the game I'm making, there will be agents with different sizes of OBB collision boxes. I can use steering behaviours for avoiding other agents, so I don't need complete dynamic pathfinding. However, there is a problem because different agents have different collision geometry, and structures can be placed in almost any place. This means that there might be a gap between two structures where some agents can go through and some can't. A solution I have found to this problem is to do a sweep of the collision geometry of the agent from start node of the edge the pf algorithm is currently testing, to the end node of that edge. But this is probably a bit overkill since every edge the algorithm tests would also have to create and test with a collision geometry sweep. What are some reasonable approaches to this problem? I should mention that I'd prefer not to use navmeshes, I prefer waypoints because my entire system is based on it atm.

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  • Missed question in technical phone interview and the follow up letter

    - by Jacob
    I may have just bombed a C++ technical phone interview. The interviewer asked mostly about data structures and I was able to go into detail about each of the data structures he asked about. Score one for me I'm thinking. Wrong. Then he asks to join me on a collaboration website where he can see what I am typing. This was the same process as interview #1 which went well, not perfect, but well. So the question was: How do you reverse a linked list? he gave a function prototype similar to Node *reverse(Node *head) I struggled with this for about 10-15 minutes until the hour was up. I was able to get the general idea across but was not able to reverse the link list. My question is that after remembering the answer post interview do I mention this in the thank you letter, if I even should write one?

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