You will have to take the knowledge you learn from experience (currently at beginner level), use that to determine where/how to address issues of improving opportunities for vectorization and where/how to parallelize the code. Applications in general with have a mix "intensiveness". ![]() There is no such thing as a typical application. Scalar intensive (that are not memory intensive) tend to scale by the number of hardware threads (though to a lesser extent for applications with larger cache utilization). Floating point intensive applications (that are not memory intensive) tend to scale by the number of cores (vector units). Memory bandwidth limited applications tend to scale by the number of memory channels as opposed to number of hardware threads. More importantly it has 2 memory channels. Your test program is essentially memory (write) bandwidth limited. and then there are naïve expectations: no overhead for region entry, no overhead for region distribution, no overhead/interference for memory bus and cache resources.Īll of the comments posted here are intended to help you through your learning experience. CPU_TIME vs elapse time, compiler optimization eliding code generating unused results, compiler optimization removing unnecessary iterations of loops, compiler optimizations producing results calculable at compile time. There are some "gotcha's" you can fall into when first exploring parallelization. Tim's comments should be read and considered. simulation advancing through time), then the method above would be correct. If you have one "thing", iterating 40000 times (e.g. If you have 40000 "things" (objects, jobs, entities), each separate calculations, then Andrew's suggestion is correct. The reason I say "may need modification" is this is would depend on what your actual code is doing as opposed to what the sketch code you provided is doing: The peeled loop might cause one thread to take extra time.Īndrew's code may need modification to meet your needs. You would want to check whether adding flags such as -QxHost -align arra圓2byte have an effect. The remark about a peeled loop being generated in your parallel region may have a bearing on the extra time taken there. The remark is useful to assure you that the compiler didn't decide to skip the code, even though no locally vectorized loop is generated. In the context of your example, the effect is practically indistinguishable from local vectorized code expansion. The compiler has made memset library function calls to zero out arrays. Evidently, they aren't vectorizable, and may be protected against in-lining, knowing that it would be counter-productive. The function calls referred to in opt-report must be those library calls inserted by expansion of the omp directives. As such an optimization may occur in non-parallel code but be suppressed by a parallel directive, it is of particular concern for the kind of conclusion you are trying to draw. ![]() ![]() In your cases, if the compiler were to examine the outer loops with a view toward vectorization collapse (making 1 loop out of 2) it would probably lead to skipping the outer loop iterations whose results are over-written immediately, exposing a fallacy in this method for assessing performance. In your example, you don't have any inlineable code, so the inline limits aren't remotely approached. I will greatly appreciate your contribution in this problem. It seems challenging to find out the reason! The parallel code is much slower than the sequential code. The cpu time (s) cost by the parallel code is 3.85937500000000 The cpu time (s) by the sequential code is 1.98437500000000 Write(*,*)'The cpu time (s) by the parallel code is',time2-time1 Write(*,*)'The cpu time (s) by the sequential code is',time2-time1 I tried to use OpenMP to parallelize an inner loop. The code is as the following (also see the attachment)
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |