Predicting HPC parallel program performance based on LLVM compiler

Weizhe Zhang, Meng Hao, Marc Snir

Research output: Contribution to journalArticlepeer-review


Performance prediction of parallel program plays key roles in many areas, such as parallel system design, parallel program optimization, and parallel system procurement. Accurate and efficient performance prediction on large-scale parallel systems is a challenging problem. To solve this problem, we present an effective framework for performance prediction based on the LLVM compiler technique in this paper. We can predict the performance of a parallel program on a small amount of nodes of the target parallel system using this framework toned but not execute this parallel program on a corresponding full-scale parallel system. This framework predicts the performance of computation and communication components separately and combines the two predictions to achieve full program prediction. As for sequential computation, we first combine the static branch probability and loop trip count identification and propose a new instrumentation method to acquire the number of each instruction type. We then construct a test program to measure the average execution time of each instruction type. Finally, we utilize the pruning technique to convert a parallel program into a corresponding sequential program to predict the performance on only one node of the target parallel system. As for communication, we utilize the LogGP model to model point-to-point communication and the artificial neural network technique to model collective communication. We validate our approach by a set of experiments that predict the performance of NAS parallel benchmarks and CGPOP parallel application. Experimental results show that the proposed framework can accurately predict the execution time of parallel programs, and the average error rate of these programs is 10.86%.

Original languageEnglish (US)
Pages (from-to)1179-1192
Number of pages14
JournalCluster Computing
Issue number2
StatePublished - Jun 1 2017


  • Artificial neural network
  • LLVM
  • Loop trip count
  • Parallel program
  • Performance prediction

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications


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