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  • Why does Apple use Objective-C for iPhone development? (App Store)

    - by Luca Matteis
    I'm interested to know your opinion on why Apple uses a language such as Objective-C for app development. Does Apple's app store allow apps written only in this language? Does apple even look at your source-code or does it just care of the binary output? I learned that most of their app rejection (in the app store) is based upon apps crashing (probably memory leaks in which Objective-c is not very efficient unless you use a GC). Why not let developers use a safer language, like a scripting language? I think these are important questions for a developer (I don't even use Apple's products) because it seems like Apple's app store is the MOST successful app sale place on the web.

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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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  • Web Experience Management: Segmentation & Targeting - Chalk Talk with John

    - by Michael Snow
    Today's post comes from our WebCenter friend, John Brunswick.  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Having trouble getting your arms around the differences between Web Content Management (WCM) and Web Experience Management (WEM)?  Told through story, the video below outlines the differences in an easy to understand manner. By following the journey of Mr. and Mrs. Smith on their adventure to find the best amusement park in two neighboring towns, we can clearly see what an impact context and relevancy play in our decision making within online channels.  Just as when we search to connect with the best products and services for our needs, the Smiths have their grandchildren coming to visit next week and finding the best park is essential to guarantee a great family vacation.  One town effectively Segments and Targets visitors to enhance their experience, reducing the effort needed to learn about their park. Have a look below to join the Smiths in their search.    Learn MORE about how you might measure up: Deliver Engaging Digital Experiences Drive Digital Marketing SuccessAccess Free Assessment Tool

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  • Apple de plus en plus critiqué sur l'AppStore, Mozilla et Opera montent au créneau

    Mise à jour du 12.03.2010 par Katleen Apple de plus en plus critiqué sur l'AppStore, Mozilla et Opera montent au créneau contre sa politique de validation La manière dont Apple gère la validation des applications publiables sur l'AppStore n'en finit plus de faire des mécontents. Déjà assez restrictive, cette politique s'est encore ressérée depuis que les applications à connotation sexuelle ont été interdites. Le soucis c'est que la manière de juger l'obscénité d'un contenu diffère selon les personnes, et chez Apple cette dernière est très stricte : un simple maillot de bain est considéré comme innaproprié. Quelles que soient les raisons de cette sévérité, ces contraintes exaspèr...

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  • L'ITC va examiner les mobiles d'HTC, pour répondre à la plainte déposée par Apple

    Mise à jour du 01.04.2010 par Katleen L'ITC va examiner les mobiles d'HTC, pour répondre à la plainte déposée par Apple L'ITC (U.S. International Trade Commission) va venir fourrer son nez dans l'affaire qui oppose Apple à HTC. La commission a en effet décidé de mener enquête en examinant les smartphones produits par le taiwannais. C'est l'entreprise de Steve Jobs qui a fait appel à l'ITC en portant plainte pour usage non-autorisé de ses brevets. Hier, un juge administratif de l'ITC a déclaré prendre possession du cas. Il a désormais 45 jours pour fixer une date de complément d'enquête. Apple demande purement et simplement que les mobiles d'HTC soie...

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  • Apple sort la bêta d'iOS 6.1 pour les développeurs, ainsi que la bêta de Xcode 4.6

    Apple sort la bêta d'iOS 6.1 pour les développeurs ainsi que la bêta de Xcode 4.6 À peine avoir propulsé la mise à jour mineure iOS 6.0.1 de son système d'exploitation mobile aux consommateurs, Apple met à la disposition des développeurs la prochaine évolution de sa plateforme. Ceux-ci peuvent dès aujourd'hui télécharger la mise à jour iOS 6.1, afin de découvrir les nouveautés de l'OS et préparer leurs applications avant la sortie grand public de la mouture. iOS 6.1 beta apporte quelques améliorations, surtout à son application native de cartographie Plans, très critiquée dans la version précédente. Il faut noter qu'Apple a mis avant un bouton permettant de soumettre un problème s...

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  • Apple actualise la gamme MacBook Pro

    Apple a renouvelé sa gamme de MacBook Pro. Ce qu'il faut retenir : Le MacBook Pro 13" gagne encore en autonomie, pouvant atteindre jusqu'à 10 heures. Il possède un processeur graphique NVidia 320M inconnu au catalogue NVidia. Il semblerait que ce soit un processeur graphique fait spécialement par NVidia à la demande d'Apple. Les MacBook Pro 15" et 17" accueille les nouveaux (peut on encore dire nouveaux ?) processeurs Intel Core i5 et i7. Du coup, ils embarquent également le processeur graphique Intel HD qui ne vaut rien par rapport au processeur Nvidia qui équipait la génération précédente. Mais elle est secondée par une carte NVidia GT330M, présentée comme étant 2 fois plus puissante que la NVidia 320M. C'est surtout le fait qu'il ne faille plus rien faire pour basculer d'une carte graphique à l'autre. Et ça, c'est vraiment bien. Apple propose également, en option, une dalle de 1680*1050 sur le 15". Ce qui est une excellente chose.

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  • Apple vient de faire le meilleur trimestre hors période de fin d'année

    Apple vient de publier son rapport pour le 2ième trimestre de l'année 2010. 2ième trimestre comptable, qui couvre en fait la période du 1er trimestre 2010 lorsqu'on suit le calendrier civil, soit janvier, février et mars. Et voici quelques morceaux choisis : Apple a vendu 2,94 millions de Macintosh® durant le trimestre, représentant une hausse de 33 % par rapport au même trimestre de l'année précédente. La compagnie a vendu 8,75 millions d'iPhones durant ce trimestre, représentant une hausse de 131 % par rapport au même trimestre de l'année précédente. Apple a vendu 10,89 millions d'iPods durant ce trimestre, représentant 1 % de moins par rapport au même trimestre de l'année précédente. Nous avons lancé notre nouveau révolutionnaire iPad et les...

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  • Apple Magic Trackpad multitouch configuration

    - by Sureshkannan Duraisamy
    Today I installed the Ubuntu 10.10 release on my Desktop PC. I was running Ubuntu 10.04 LTS with an Apple Magic Trackpad and everything was working fine. After today's fresh installation of Ubuntu 10.10, I don't see my Apple Magic Trackpad's multitouch working. Two-finger scrolling and three-finger third mouse button clicking are completely broken. Has anyone else experienced a similar issue? Has anyone had success with Ubuntu 10.10 and an Apple Magic TrackPad? Please help me to fix this issue. Your help is highly appreciated...

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  • Mozilla reproche à Apple et Google de vouloir s'approprier le HTML5, tandis que Microsoft est félici

    Mozilla reproche à Apple et Google de vouloir s'approprier le HTML5, tandis que Microsoft est félicité pour son soutien de la technologie Christopher Blizzard, évangéliste Open Source chez Mozilla, tire à boulets rouges sur Apple et Google. Il accuse les deux firmes d'essayer de s'approprier le format HTML5 de manière déloyale, alors qu'elles ne sont pas les seuls à travailler à son développement. Apple d'abord, qui à publié sur son site des démonstrations des capacités de l'HTML5 réservées aux utilisateurs de Safari (il faut passer par l'onglet "développeurs" pour les visionner depuis un autre navigateur). "Tous les navigateurs ne les supportent pas", indique en bas de page le groupe à la pomme, ce qui laissera...

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  • Nokia dépose une nouvelle plainte contre Apple, l'iPhone et l'iPad auraient violé ses brevets

    Mise à jour du 07.05.2010 par Katleen Nokia dépose une nouvelle plainte contre Apple, l'iPhone et l'iPad auraient violé ses brevets La conflit juridique entre Nokia et Apple monte encore d'un cran. Nokia vient de déposer une nouvelle plainte contre la firme de Steve Jobs, dans laquelle il l'accuse d'enfreindre cinq de ses brevets avec l'iPhone et l'iPad 3G. C'est la Federal Distric Court du district ouest du Wisconsin qui a enregistré la procédure. Nokia soutient qu'Apple enfreint des brevets en rapport à "des technologies pour des transmissions de données et de conversation améliorées, utilisant le positionnement des données dans les applications et des innovations dans la configuration des ant...

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  • Apple vaut désormais plus que Microsoft en bourse, et devient la troisième capitalisation boursière

    Apple vaut désormais plus que Microsoft en bourse, et devient la troisième capitalisation boursière mondiale La journée d'hier s'est clôturée avec une capitalisation totale de marché (le prix d'une action multiplié par le nombre d'actions) à hauteur de 222.12 milliards de dollars pour Apple, contre «*seulement*» 219.18 milliards pour Microsoft. A onze heures passées de cinquante deux minutes (heure de Paris), Les deux firmes n'étaient plus séparées que par un tout petit milliard de dollars. Les analystes voyaient leurs prédictions se réaliser. En effet, le dépassement de Microsoft par Apple avait été largement anticipé. Alors que la capitalisation de son stock market dépassait toujours celle de la firme à la pomme, l...

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  • Apple - Android : HTC contre-attaque en demandant comme Nokia l'interdiction totale d'importation et

    Mise à jour du 13/05/10 Apple ? Android : HTC contre-attaque En demandant comme Nokia l'interdiction totale d'importation et de vente des iPhone, iPad et iPod HTC met ses pas dans ceux de Nokia. Dans l'affaire qui l'oppose à Apple (lire ci-avant), la société a décidé de contre-attaquer en utilisant la méthode forte, tout comme le constructeur finlandais. HTC, principal utilisateur du système d'exploitation mobile de Google, répond à Apple en l'accusant à son tour de violation de brevets. L'affaire sera portée devant la décidément très occupée ITC (U.S. International Trade Commission). Mais HTC ne s'arrête...

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  • Apple serait prêt à modifier ses conditions de développement, pour éviter une plainte d'antitrust

    Mise à jour du 05.05.2010 par Katleen Apple serait prêt à modifier ses conditions de développement, pour éviter une plainte d'antitrust Quelques heures seulement après l'annonce officieuse d'une volonté des autorités américaines de se pencher sur le cas Apple, la firme en question pourrait assouplir sa très rigide politique de développement pour l'iPhone et l'iPad, afin de mettre un peu d'eau dans le vin. La version 4.0 de son SDK apportait en effet des changements très critiqués depuis : de nouvelles règles préconisant un usage exclusif d'APIs, de langages et de compilers approuvés par Apple. Cette mesure fut vite renommée la "No Adobe clause" par les bloggeurs, tandis que d...

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  • Apple annonce Safari 5 pour Mac et Windows

    Apple a, lors de la WWDC, annoncé la sortie de Safari 5 30% plus rapide que Safari 4, 3% plus rapide que Google Chrome, Safari 5 vous permet de choisir parmi les moteurs de recherche Google, Yahoo! ou Bing. Les outils intégrés pour les développeurs ont été améliorés, le support HTML5 encore amélioré. Mais la grosse nouveauté est qu'avec Safari 5, Apple annonce également la possibilité pour les développeurs de créer des extensions à Safari. En plus de l'iPhone developer program 99$/an et du Mac developer program, 99$/an , Apple a rajouté le Safari developer program, 0$/an. Citation:

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  • How can I check whether Exposé is being activated or not?

    - by yangumi
    Hi, I'm creating an application that emulates MacBook's multi-touch Trackpad. As you may know, on MacBook's trackpad if you swipe 4 fingers up, it triggers the Show Desktop. if you swipe 4 fingers down, it shows the Exposé. However, if the Show Desktop is being activated and you swipe 4 fingers down, it will come back to the normal mode. The same goes with the Exposé: if the Exposé is being activated and you swipe 4 fingers up, it will also come back to the normal mode. Here is the problem: I use the keyboard shortcut F3 to show the Exposé and F11 to show the Show Desktop. The problem is, when the Show Desktop is being activated, if I press F3, it will go straight to the Exposé. And when the Exposé is being activated, if I press F11 it will go straight to the Show Desktop. But I want it to behave like Trackpad, which I guess its code may look like this - FourFingersDidSwipeUp { if (isExposeBeingActivated() || isShowDesktopBeingActivated()) { pressKey("Esc"); } else { pressKey("F11"); } } But I don't know how to implement the "isExposeBeingActivated()" and "isShowDesktopBeingActivated()" methods. I've tried creating a window and check whether its size has changed (on assumption that if the Expose is being activated, its size should be smaller), but the system always returns the same size. I tried monitoring the background processes during the Expose, but nothing happened. Does anyknow have any suggestions on this? (I'm sorry if my English sounds weird.)

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  • Order Result in Sqlite

    - by saturngod
    In MySQL , my sql is like following SELECT * , IF( `Word` = 'sim', 1, IF( `Word` LIKE 'sim%', 2, IF( `Word` LIKE '%sim', 4, 3 ) ) ) AS `sort` FROM `dblist` WHERE `Word` LIKE '%sim%' ORDER BY `sort` , `Word` This sql is not working in SQlite. I want to do result order. SELECT * FROM dblist where word like 'sim' or word like 'sim%' or word like '%sim%' or word like '%sim' equal sim is a frist , sim% is second and %sim% is a thrid and then %sim is a last. Currently I can't sort like mysql in sqlite. How to change sql to order the result ?

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  • How to get my Apple Keyboard to work on Ubuntu?

    - by misbehavens
    I'm trying to use a USB keyboard with my Ubuntu laptop, but when I plug in the keyboard, it is not even detected. I am trying to use the Apple Slim Aluminum Keyboard. It would also be nice if the USB ports on the keyboard could work, but I can get by without that luxury. How can I get my Apple Slim Aluminum Keyboard to work with Ubuntu? Update: After upgrading to a newer version of Ubuntu (9.04 Jaunty), the keyboard was detected and types just fine. There are a few quirks like the clear button being used as the numlock key but that seems to be well documented on other sites.

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  • When did Apple stop using the name "Macintosh" in favor of "Mac", and does anyone know why?

    - by schnapple
    As of a few months ago I finally joined the ranks of Macintosh owners. Except "Macintosh" doesn't seem to exist anymore for some reason. I noticed everything was "Mac", i.e. Mac OS X, MacBook, Mac mini, Mac Pro, etc. I didn't pay a whole lot of attention, but I always thought everyone was using shorthand. I mean "MacBook" is the real name of the computer, as is "iMac", but I always thought when people said "Mac Pro" they were just shortening the real name, "Macintosh Pro". And yet now when you go to the Apple site, a search for "Macintosh" turns up several instances of the name being used on various things (system requirements for old versions of QuickTime, the occasional piece of software with "for the Macintosh" in the name) but nothing from the main Mac pages. Near as I can tell they're really no longer called "Macintoshes" they're just "Macs" When did this happen, and does anyone know why Apple ditched the term "Macintosh"?

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  • Rush…iPAD Pre-order announced officially

    - by samsudeen
    Apple’s latest product iPAD is now available for pre-order through online. You can place your pre-order through its online store (Apple) or reserve it at any of the Apple retail stores. iPAD may have received mixed reactions when announced last month. But Apple knows how to sell; it is believed that more than 50,000 pre orders are already placed till now placed till now. People have to wait for another 3 weeks to get the actual device as the launch date is 3rd of April in the US. The initial model released will be available only with Wi-Fi and the planned 3G model is expected to be released by end of April. So how much does it cost you to get this little marvel? The basic iPAD (16 GB Wi-Fi) will cost you $499. if you are serious apple fan and plan to buy an iPAD better place your order now. There already rumours that the initial demand may outstrip supply.The pre-order is limited only to US. Join us on Facebook to read all our stories right inside your Facebook news feed.

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  • Experience the iPad UI On Your PC

    - by Matthew Guay
    Want to test drive iPad without heading over to an Apple store?  Here’s a way you can experience some of the iPad UI straight from your browser! The iPad is the latest gadget from Apple to wow the tech world, and people even waited in line all night to be one of the first to get their hands on one.  Thanks to a simple JavaScript trick, however, you can get a feel for some of its new features without leaving your computer.  This won’t let you try out everything on the iPad, but it will let you see how the new lists and pop-over menus work just like they do in the new apps. Test drive the iPad’s UI from your browser Normally, the Apple iPhone developer library online looks like a standard webpage. But, on the iPad, it looks and feels like a full-blown native iPad app.  With a nifty JavaScript trick from boredzo.org you can use this same interface on your PC.  Since the iPad uses the Safari browser, we ran this test in Safari for Windows.  If you don’t already have it installed, you can download it from Apple (link below) and setup as normal. Now, open Safari and browse to Apple’s developer page at: http://www.developer.apple.com   Now, enter the following in the address bar, and press Enter. javascript:localStorage.setItem('debugSawtooth', 'true')   Finally, click this link to go to the iPhone OS documentation. http://developer.apple.com/iphone/library/iPad/ After a short delay, it should open in full iPad style! The left menu works just like the menus on the iPad, complete with transitions.  It feels entirely like a native application, instead of a webpage.  To scroll through text, click and pull up or down similar to the way you would use it on a touch screen. Some pages even include a pop-over menu like many of the new iPad apps use. Note that the page will be rendered for the size of your browser, and if you resize your window the page will not resize with it.  Simply press F5 to reload the page, and it will resize to fit the new window size.  If you resize your window to be tall and narrow, like the iPad in horizontal mode, the webpage will change and the left menu will disappear in lieu of a drop-down menu just like it would if you rotated the iPad. This works in Chrome as well, since it, like Safari, is based on Webkit.  However, it didn’t seem to work in our test on Firefox or other browsers. We’ve previously covered how you can experience some of the iPhone’s UI with the online iPhone user guide.  Check it out if you haven’t yet: View Mobile Websites in Windows with Safari 4 Developer Tools Conclusion Although this doesn’t let you really try out all of the iPad’s interface, it at least gives you a taste of how it works.  It’s exciting to see how much functionality can be packed into webapps today.  And don’t forget, How-to Geek is giving away an iPad to a random fan!  Head over to our Facebook page and fan How-to Geek if you haven’t already done so. Win an iPad on the How-To Geek Facebook Fan Page Similar Articles Productive Geek Tips Want an iPad? How-To Geek is Giving One Away!Why Wait? Amazing New Add-on Turns Your iPhone into an iPad! [Comic]The Complete List of iPad Tips, Tricks, and TutorialsShare Your Windows Vista Experience Index ScoreAnother Blog You Should Subscribe To TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 Awesome Lyrics Finder for Winamp & Windows Media Player Download Videos from Hulu Pixels invade Manhattan Convert PDF files to ePub to read on your iPad Hide Your Confidential Files Inside Images Get Wildlife Photography Tips at BBC’s PhotoMasterClasses

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  • Multi sim GSM modem or alternative

    - by Ando
    I'm trying to administer the SMS trafic of my businesss centrally through a web portal. In Europe (except UK) we don't have a numbers/SMS trafic provider like Twilio or Clickatell, nor any build in way to administer the SMS traffic for a number via http, so I will have to buy the long numbers and administer the SMS traffic myself. For this I was looking into a hardware solution for hosting all my SIM cards - I have like 400 sims cards (= numbers). I saw that GSM modems might fit in but they don't seem to scale up very well. Could you recommend me a GSM modem? If this is not the best way to approach this, what would my alternatives be? Thanks in advance

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  • Pasting extended ACL contents into telnet session to Cisco Router SIM

    - by Kyle Brandt
    I have a telnet session to a dynamips router sim. When I try to paste the contents of an actually working ACL retrieved from 'show run' into the access list, only part of gets pasted. The session is something like: enable conf t ip access-list extended Internet <PASTE of Rules> It stops right in the middle of a line: permit tcp any host 123.123.123.123 gt 1 ! should be gt 1023 Anyone know what is happening? The source is an extended access list.

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