Automatic Performance Prediction Method of Scientific Computing Program Based on llvm

Abstract

An LLVM-based automatic performance prediction method for scientific computing programs belongs to the technical field of program performance prediction. The purpose of the present invention is to realize the automatic analysis of scientific calculation program, improve the accuracy of static analysis, and at the same time, it can finally give the time of program prediction. Technical points: convert the predicted source program into the intermediate code bitcode in LLVM; analyze the intermediate code bitcode to obtain the call, cycle times, and static branch probability of the identification communication instruction MPI; perform mixed instrumentation on the intermediate code bitcode; mix the The intermediary code bitcode after stubbing is pruned for optimization; run the optimized intermediary code bitcode to obtain the llvmprof.out file; analyze the llvmprof.out file and combine the instruction time to predict the execution time. The method of the invention is suitable for performance prediction of scientific computing programs.

Description

technical field [0001] The invention discloses a scientific calculation program automatic performance prediction method, which belongs to the technical field of program performance prediction. Background technique [0002] High-performance computing is an important branch of computer science, and performance is a key characteristic of high-performance computing. Program execution time is the performance feature that users are most concerned about. The reason why users choose to use hundreds of processors to process programs is that they need to obtain program running results within a limited time. Therefore, predicting the execution time of parallel programs on a certain platform has been more and more researched, and this technique is called program profiling. [0003] Performance evaluation methods can be divided into dynamic analysis and static analysis. [0004] The so-called dynamic analysis method is to predict large-scale through small-scale, that is, to measure mul...

AI Technical Summary

Problems solved by technology

If it is to measure the 1024 degree of parallelism, and the selected sampling scale range is only a curve fitted by a small range such as [0-128], it is difficult to guarantee that it is reasonable under the 1024 scale

Even if the fitting function can overlap well in the previous range, there is no guarantee that it can fit well on a large scale

This limits the scope of application of the forecast

Moreover, the parallelism of some programs is required and cannot be changed continuously. In this way, the sampled data points cannot be evenly distributed.

[0008] The results are not reasonable enough: because the whole fitting process is analyzed purely mathematically, many internal dependencies of the program are ignored, so the need for program features is also ignored

[0011] Insufficient rationality: the branch probability is only the type of prior probability and branch instruction applied, and it cannot actually represent the target program

However, this does not meet our requirements

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