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  • OS Development. Only Few Particular Questions

    - by Total Anime Immersion
    I am new to this site as a member but have consulted its answers quite a lot of times. Besides my questions regarding OS Development hasn't been answered in any forum. In OS Dev. we make a bootloader. The org point is 7C00H. Why so? Why not 0000h? What are the last two signatures in the bootloader used for? People on every forum have answered that it is important for the system to recognize it as a bootable media. But I want a specific answer. What do each of those signatures do. I have the basic concept of a kernel. Point is.. it relates to different files required in a system. It sort of binds up everything that is individually developed. Now the thing is that that I have floating ideas in my mind regarding different aspects like keyboard, mouse, etc.. how do I put them all together? Which should I start with first? If possible please provide a step by step procedure of the startups of the kernel. Suppose I have developed my language entirely in C and Assembly. Now questions is will exe files work on my system.. if it doesn't then I have to create my own files and publish them. Which is a bad idea.. next step would be for me to go for a compiler for a language which I have designed myself. Now the point is.. How do I implement the compiler into my OS? After all this my final question is that.. How do you go about multitasking and multithreading? and I don't want to use int 21h as its dos specific.. how do I go about making files, renaming them, etc. and all assembly books teach 16 but programming.. how do i go about doing 32 bit or 64 bit with the knowledge I have.. if the basics and instructions are the same.. I don't mind.. but how do i go about otherwise? Don't tell me to give up the idea because I WON'T. And don't tell me it's too complex because I have a sharp knowledge of working of a system, C, Java, Assembly, C++ and python, C#, visual basic.. and not just basics but full fledged api developments.. but I really want to go deep into the systems part.. so I want professional help.. And I have gone through many OS project files but I want help particularly from this site as there are people with knowledge depth who can guide me the right way. And please don't suggest any books above 20$ and they should be available on flipkart as amazon charges massively for shipping and I prefer free shipping from flipkart.

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  • how to install g77 on ubuntu 12.04

    - by ubuntu-beginner
    I want a workin g77 compiler on my Ubuntu 12.04 64 bit laptop. so did the following: 1. I change the sources.list by adding the following lines: deb http...hu.archive.ubuntu.com/ubuntu/ hardy universe deb-src ..//hu.archive.ubuntu.com/ubuntu/ hardy universe deb http:...hu.archive.ubuntu.com/ubuntu/ hardy-updates universe deb-src ..//hu.archive.ubuntu.com/ubuntu/ hardy-updates universe then I on a terminal i did the following: sudo apt-get update sudo apt-get install g77 Things looked very nice then. But when I tried to compile with g77 on my Fortran77 program. I got the following errors: /usr/bin/ld: cannot find crt1.o: No such file or directory /usr/bin/ld: cannot find crti.o: No such file or directory /usr/bin/ld: cannot find -lgcc_s collect2: ld returned 1 exit status Why doesn't the g77 work properly. Many people need g77 why cannot Ubuntu offer a workable g77 ? Please Help me ! Thanks from a ubuntu-beginner

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  • Converting ANTLR AST to Java bytecode using ASM

    - by Nick
    I am currently trying to write my own compiler, targeting the JVM. I have completed the parsing step using Java classes generated by ANTLR, and have an AST of the source code to work from (An ANTLR "CommonTree", specifically). I am using ASM to simplify the generating of the bytecode. Could anyone give a broad overview of how to convert this AST to bytecode? My current strategy is to explore down the tree, generating different code depending on the current node (using "Tree.getType()"). The problem is that I can only recognise tokens from my lexer this way, rather than more complex patterns from the parser. Is there something I am missing, or am I simply approaching this wrong? Thanks in advance :)

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  • Why does Facebook convert PHP code to C++?

    - by user72245
    I read that Facebook started out in PHP, and then to gain speed, they now compile PHP as C++ code. If that's the case why don't they: Just program in c++? Surely there must be SOME errors/bugs when hitting a magic compiler button that ports PHP to c++ code , right? If this impressive converter works so nicely, why stick to PHP at all? Why not use something like Ruby or Python? Note -- I picked these two at random, but mostly because nearly everyone says coding in those languages is a "joy". So why not develop in a super great language and then hit the magic c++ compile button?

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  • How compilers know about other classes and their properties?

    - by OnResolve
    I'm writing my first programming language that is object orientated and so far so good with create a single 'class'. But, let's say I want to have to classes, say ClassA and ClassB. Provided these two have nothing to do with each other then all is good. However, say ClassA creates a ClassB--this poses 2 related questions: -How would the compiler know when compiling ClassA that ClassB even exists, and, if it does, how does it know it's properties? My thoughts thus far had been: instead of compiling each class at a time (i.e scan, parse and generate code) each "file (not really file, per se, but a "class") do I need to scan + parse each first, then generate code for all?

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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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  • Eclipse Dynamic Web Project fails to load under WTP

    - by Cue
    When trying to run an Eclipse Dynamic Web Project under a Tomcat setup using WTP, it fails with the attached stacktrace. Checklist At the project properties, under "Java EE Module Dependencies" I have checked the "Maven Dependencies" At the wtp deploy directory, under lib indeed all dependencies are present (esp. struts-taglib-1.3.10.jar) On the other hand, if I package with maven and copy the war file under the webapps directory everything is working as normal. Specification Eclipse JEE Galileo SR2 (with WTP 3.1.1) Tomcat 6.0.26 Java(TM) SE Runtime Environment (build 1.6.0_17-b04-248-10M3025) Java HotSpot(TM) 64-Bit Server VM (build 14.3-b01-101, mixed mode) stacktrace org.apache.jasper.JasperException: Unable to read TLD "META-INF/tld/struts-html-el.tld" from JAR file "file:/Users/cue/Development/workspace/eclipse/.metadata/.plugins/org.eclipse.wst.server.core/tmp2/wtpwebapps/ticketing/WEB-INF/lib/struts-el-1.3.10.jar": org.apache.jasper.JasperException: Failed to load or instantiate TagExtraInfo class: org.apache.struts.taglib.html.MessagesTei at org.apache.jasper.compiler.DefaultErrorHandler.jspError(DefaultErrorHandler.java:51) at org.apache.jasper.compiler.ErrorDispatcher.dispatch(ErrorDispatcher.java:409) at org.apache.jasper.compiler.ErrorDispatcher.jspError(ErrorDispatcher.java:181) at org.apache.jasper.compiler.TagLibraryInfoImpl.<init>(TagLibraryInfoImpl.java:182) at org.apache.jasper.compiler.Parser.parseTaglibDirective(Parser.java:383) at org.apache.jasper.compiler.Parser.parseDirective(Parser.java:446) at org.apache.jasper.compiler.Parser.parseElements(Parser.java:1393) at org.apache.jasper.compiler.Parser.parse(Parser.java:130) at org.apache.jasper.compiler.ParserController.doParse(ParserController.java:255) at org.apache.jasper.compiler.ParserController.parse(ParserController.java:103) at org.apache.jasper.compiler.Compiler.generateJava(Compiler.java:185) at org.apache.jasper.compiler.Compiler.compile(Compiler.java:347) at org.apache.jasper.compiler.Compiler.compile(Compiler.java:327) at org.apache.jasper.compiler.Compiler.compile(Compiler.java:314) at org.apache.jasper.JspCompilationContext.compile(JspCompilationContext.java:589) at org.apache.jasper.servlet.JspServletWrapper.service(JspServletWrapper.java:317) at org.apache.jasper.servlet.JspServlet.serviceJspFile(JspServlet.java:313) at org.apache.jasper.servlet.JspServlet.service(JspServlet.java:260) at javax.servlet.http.HttpServlet.service(HttpServlet.java:717) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:290) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:206) at org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:233) at org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:191) at org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:127) at org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:102) at org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:109) at org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:298) at org.apache.coyote.http11.Http11Processor.process(Http11Processor.java:852) at org.apache.coyote.http11.Http11Protocol$Http11ConnectionHandler.process(Http11Protocol.java:588) at org.apache.tomcat.util.net.JIoEndpoint$Worker.run(JIoEndpoint.java:489) at java.lang.Thread.run(Thread.java:637)

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  • C headers: compiler specific vs library specific?

    - by leonbloy
    Is there some clear-cut distinction between standard C *.h header files that are provided by the C compiler, as oppossed to those which are provided by a standard C library? Is there some list, or some standard locations? Motivation: int this answer I got a while ago, regarding a missing unistd.h in the latest TinyC compiler, the author argued that unistd.h (contrarily to sys/unistd.h) should not be provided by the compiler but by your C library. I could not make much sense of that response (for one thing shouldn't that also apply to, say, stdio.h?) but I'm still wondering about it. Is that correct? Where is some authoritative reference for this? Looking in other compilers, I see that other "self contained" POSIX C compilers that are hosted in Windows (like the GCC toolchain that comes with MinGW, in several incarnations; or Digital Mars compiler), include all header files. And in a standard Linux distribution (say, Centos 5.10) I see that the gcc package provides a few header files (eg, stdbool.h, syslimits.h) in /usr/lib/gcc/i386-redhat-linux/4.1.1/include/, and the glibc-headers package provides the majority of the headers in /usr/include/ (including stdio.h, /usr/include/unistd.h and /usr/include/sys/unistd.h). So, in neither case I see support for the above claim.

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  • C# and Metadata File Errors

    - by j-t-s
    Hi All I've created my own little c# compiler using the tutorial on MSDN, and it's not working properly. I get a few errors, then I fix them, then I get new, different errors, then I fix them, etc etc. The latest error is really confusing me. --------------------------- --------------------------- Line number: 0, Error number: CS0006, 'Metadata file 'System.Linq.dll' could not be found; --------------------------- OK --------------------------- I do not know what this means. Can somebody please explain what's going on here? Here is my code. MY SAMPLE C# COMPILER CODE: using System; namespace JTM { public class CSCompiler { protected string ot, rt, ss, es; protected bool rg, cg; public string Compile(String se, String fe, String[] rdas, String[] fs, Boolean rn) { System.CodeDom.Compiler.CodeDomProvider CODEPROV = System.CodeDom.Compiler.CodeDomProvider.CreateProvider("CSharp"); ot = fe; System.CodeDom.Compiler.CompilerParameters PARAMS = new System.CodeDom.Compiler.CompilerParameters(); // Ensure the compiler generates an EXE file, not a DLL. PARAMS.GenerateExecutable = true; PARAMS.OutputAssembly = ot; foreach (String ay in rdas) { if (ay.Contains(".dll")) PARAMS.ReferencedAssemblies.Add(ay); else { string refd = ay; refd = refd + ".dll"; PARAMS.ReferencedAssemblies.Add(refd); } } System.CodeDom.Compiler.CompilerResults rs = CODEPROV.CompileAssemblyFromFile(PARAMS, fs); if (rs.Errors.Count > 0) { foreach (System.CodeDom.Compiler.CompilerError COMERR in rs.Errors) { es = es + "Line number: " + COMERR.Line + ", Error number: " + COMERR.ErrorNumber + ", '" + COMERR.ErrorText + ";" + Environment.NewLine + Environment.NewLine; } } else { // Compilation succeeded. es = "Compilation Succeeded."; if (rn) System.Diagnostics.Process.Start(ot); } return es; } } } ... And here is the app that passes the code to the above class: using System; using System.Collections.Generic; using System.ComponentModel; using System.Data; using System.Drawing; using System.Linq; using System.Text; using System.Windows.Forms; namespace WindowsFormsApplication1 { public partial class Form1 : Form { public Form1() { InitializeComponent(); } private void button1_Click(object sender, EventArgs e) { string[] f = { "Form1.cs", "Form1.Designer.cs", "Program.cs" }; string[] ra = { "System.dll", "System.Windows.Forms.dll", "System.Data.dll", "System.Drawing.dll", "System.Deployment.dll", "System.Xml.dll", "System.Linq.dll" }; JTS.CSCompiler CSC = new JTS.CSCompiler(); MessageBox.Show(CSC.Compile( textBox1.Text, @"Test Application.exe", ra, f, false)); } } } So, as you can see, all the using directives are there. I don't know what this error means. Any help at all is much appreciated. Thank you

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  • Java compiler error: Can't open input server /Library/InputManagers/Inquisitor

    - by unknown (yahoo)
    I am trying to compile HelloWorld in Java under Mac OS X 10.6 (Snow Leopard) and I get this compiler error: java[51692:903] Can't open input server /Library/InputManagers/Inquisitor It happens when I am using terminal command javac and when I am trying to do this in NetBeans. I was trying to open folder "Inquisitor", but I have no access to folder, even if I login as root user. What is going on?

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  • java compiler error: Can't open input server /Library/InputManagers/Inquisitor

    - by unknown (yahoo)
    Hi I am trying to compile helloWorld in java under snow leopard and I get this compiler error: java[51692:903] Can't open input server /Library/InputManagers/Inquisitor It happens when I am using terminal command javac and when I am trying do this in NetBeans. I was trying to open folder "Inquisitor" but I have no access to folder , even if I login as root user. Any clue what is going on? thanks.

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  • C compiler selection in cabal package

    - by ony
    Today I've tried C compiler (Clang) for C code I use in my haskell library and found that I can gain speed increase in comparsing with my system compiler (GCC 4.4.3) from 426.404 Gbit/s to 0.823 Tbit/s So I decided to add some flags to control the way that C source file is compiled (i.e. something like use-clang, use-intel etc.). Snippet of cabal package description file: C-Sources: c_lib/tiger.c Include-Dirs: c_lib Install-Includes: tiger.h if flag(debug) GHC-Options: -debug -Wall -fno-warn-orphans CPP-Options: -DDEBUG CC-Options: -DDEBUG -g else GHC-Options: -Wall -fno-warn-orphans Question is: which options in descritpion file need to be modified to change C compiler used to compile "c_lib/tiger.c"? I did found only CC-Options.

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  • Add a custom compiler to XCode 3.2

    - by racha
    I have a working gcc 4.3.3 toolchain for an ARM Cortex-m3 and would like to integrate it into XCode. Is there a way to set up XCode (3.2) to use this gcc toolchain instead of the built-in GCC 4.2? What I've tried so far: I've added a modified copy of the GCC 4.2.xcplugin and changed the name, version and executable path. It shows up in XCode but whenever I set the "C/C++ Compiler Version" to the custom compiler it fails with Invalid value '4.3.3' for GCC_VERSION It seems like the valid version numbers are hardcoded somewhere else because even when I remove the original GCC 4.2.xcplugin, the value 4.2 remains valid (but is not visible in the "C/C++ Compiler Version" drop down anymore).

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  • Unsuccessful error detection of improperly declared method in GCC 4.2 compiler

    - by sam
    I am using C++ compiler GCC 4.2 in XCode 3.2.2. I have noted that the compiler will successfully compile a method foo even though there are no ellipses. The header and method are properly declared as foo(), but when I do a find and replace either by file or by program-wide it will miss approximately 2-3% of the changes [foo to foo(). This would not be critical if the compiler did not give an erroneous successful build. I have not found that this to occur with: foo(any parameter). Does anyone have any solution? Thank you.

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  • Java bytecode compiler benchmarks

    - by Dave Jarvis
    Q.1. What free compiler produces the fastest executable Java bytecode? Q.2. What free virtual machine executes Java bytecode the fastest (on 64-bit multi-core CPUs)? Q.3. What other (currently active) compiler projects are missing from this list: http://www.ibm.com/developerworks/java/jdk/ http://gcc.gnu.org/java/ http://openjdk.java.net/groups/compiler/ http://java.sun.com/javase/downloads/ http://download.eclipse.org/eclipse/downloads/ Q.4. What performance improvements can compilers do that JITs cannot (or do not)? Q.5. Where are some recent benchmarks, comparisons, or shoot-outs (for Q1 or Q2)? Thank you!

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  • Java Compiler - Load Method

    - by Brian
    So I have been working on a java project where the goal is to create a virtual computer. So I am basically done but with one problem. I have created a compiler which translates a txt document with assembly code in it and my compiler has created a new-file with this code written as machine executable ints. But now I need to write a load method that reads these ints and runs the program but I am having difficulty doing this. Any help is much appreciated....also this is not homework if you are thinking this. The project was simply to make a compiler and now I am trying to complete it for my own interest. Thanks. Here is what I have so far for load: public void load(String filename) { FileInputStream fs = new FileInputStream(filename); DataInputStream dos = new DataInputStream(fs); dos.readInt();

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  • Compiler error when casting to function pointer

    - by detly
    I'm writing a bootloader for the PIC32MX, using HiTech's PICC32 compiler (similar to C90). At some point I need to jump to the real main routine, so somewhere in the bootloader I have void (*user_main) (void); user_main = (void (*) (void)) 0x9D003000; user_main(); (Note that in the actual code, the function signature is typedef'd and the address is a macro.) I would rather calculate that (virtual) address from the physical address, and have something like: void (*user_main) (void); user_main = (void (*) (void)) (0x1D003000 | 0x80000000); user_main(); ...but when I try that I get a compiler error: Error #474: ; 0: no psect specified for function variable/argument allocation Have I tripped over some vagarity of C syntax here? This error doesn't reference any particular line, but if I comment out the user_main() call, it goes away. (This might be the compiler removing a redundant code branch, but the HiTech PICC32 isn't particularly smart in Lite mode, so maybe not.)

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  • compiler warning on (ambiguous) method resolution with named parameters

    - by FireSnake
    One question regarding whether the following code should yield a compiler warning or not (it doesn't). It declares two methods of the same name/return type, one has an additional named/optional parameter with default value. NOTE: technically the resolution isn't ambiguous, because the rules clearly state that the first method will get called. See here, Overload resolution, third bullet point. This behavior is also intuitive to me, no question. public void Foo(int arg) { ... } public void Foo(int arg, bool bar = true) { ...} Foo(42); // shouldn't this give a compiler warning? I think a compiler warning would be kind of intuitive here. Though the code technically is clean (whether it is a sound design is a different question:)).

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  • Compiler #defines for g++ and cl

    - by DHamrick
    I am writing a program that is cross platform. There are a few spots where I have to specify an operating system dependent call. #ifdef WINDOWS ..do windows only stuff #endif #ifdef LINUX ..do linux only stuff #endif Are there any preprocesser directives that get defined by the compiler so I don't have to explicitly define them when I use the command line compiler. ie. cl -DWINDOWS program.cpp or g++ -DLINUX program.cpp I realize I could easily write a makefile or have a shell/batch script that will do this automatically. But I would prefer to use the same ones as the compiler (if they exist) by default.

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  • Choosing the right and learning assembler for compiler-writing

    - by X A
    I'm writing a compiler and I have gone through all the steps (tokenizing, parsing, syntax tree structures, etc.) that they show you in all the compiler books. (Please don't comment with the link to the "Resources for writing a compiler" question!). I have chosen to use NASM together with alink as my backend. Now my problem is: I just can't find any good resources for learning NASM and assembly in general. The wikibook (german) on x86 assembly is horrible. They don't even explain the code they write there, I currently can't even get simple things like adding 1 to 2 and outputting the result working. Where can I learn NASM x86 assembly?

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