benchmarking

问题 I know these terms are used in the context of performance achievement. These day I am working on that, and have tried to know about these from internet but didn't get any example which clearly present these concepts with the existence of these problems/concepts in real world development scenarios. Can somebody please thoroughly explain these terms, the example scenarios, and where these concepts and terms are likely used. Thanks. 回答1: "Boilerplate" has nothing to do with performance: it just

问题 We are benchmarking existing Java programs. They are threaded applications designed to benefit from multi-core CPUs. We would like to measure the effect of the number of cores on the running speed, but we are unwilling (and unable) to change the code of these applications. Of course, we could test the software on different machines, but this is expensive and complicated. We would rather have a software solution. Note: you can assume that the testing platform is either Windows, Linux or Mac.

问题 I was trying to figure out the fastest way to do matrix multiplication and tried 3 different ways: Pure python implementation: no surprises here. Numpy implementation using numpy.dot(a, b) Interfacing with C using ctypes module in Python. This is the C code that is transformed into a shared library: #include <stdio.h> #include <stdlib.h> void matmult(float* a, float* b, float* c, int n) { int i = 0; int j = 0; int k = 0; /*float* c = malloc(nay * sizeof(float));*/ for (i = 0; i < n; i++) {

问题 I read in a comment here on Stack Overflow that it is more memory efficient to do slice assignment when changing lists. For example, a[:] = [i + 6 for i in a] should be more memory efficient than a = [i + 6 for i in a] because the former replaces elements in the existing list, while the latter creates a new list and rebinds a to that new list, leaving the old a in memory until it can be garbage collected. Benchmarking the two for speed, the latter is slightly quicker: $ python -mtimeit -s 'a

问题 I would like to write a program that makes extensive use of BLAS and LAPACK linear algebra functionalities. Since performance is an issue I did some benchmarking and would like know, if the approach I took is legitimate. I have, so to speak, three contestants and want to test their performance with a simple matrix-matrix multiplication. The contestants are: Numpy, making use only of the functionality of dot . Python, calling the BLAS functionalities through a shared object. C++, calling the

问题 Closed . This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 9 months ago . Let's say I wanted to benchmark two programs: foo.py and bar.py. Are a couple thousand runs and the respective averages of time python foo.py and time python bar.py adequate enough for profiling and comparing their speed? Edit: Additionally, if the execution of each program

问题 Closed . This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 9 months ago . Let's say I wanted to benchmark two programs: foo.py and bar.py. Are a couple thousand runs and the respective averages of time python foo.py and time python bar.py adequate enough for profiling and comparing their speed? Edit: Additionally, if the execution of each program

问题 Closed . This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 9 months ago . Let's say I wanted to benchmark two programs: foo.py and bar.py. Are a couple thousand runs and the respective averages of time python foo.py and time python bar.py adequate enough for profiling and comparing their speed? Edit: Additionally, if the execution of each program

问题 Does anyone ever use stopwatch benchmarking, or should a performance tool always be used? Are there any good free tools available for Java? What tools do you use? To clarify my concerns, stopwatch benchmarking is subject to error due to operating system scheduling. On a given run of your program the OS might schedule another process (or several) in the middle of the function you're timing. In Java, things are even a little bit worse if you're trying to time a threaded application, as the JVM

问题 Is there a simple library to benchmark the time it takes to execute a portion of C code? What I want is something like: int main(){ benchmarkBegin(0); //Do work double elapsedMS = benchmarkEnd(0); benchmarkBegin(1) //Do some more work double elapsedMS2 = benchmarkEnd(1); double speedup = benchmarkSpeedup(elapsedMS, elapsedMS2); //Calculates relative speedup } It would also be great if the library let you do many runs, averaging them and calculating the variance in timing! 回答1: Basically, all