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New approach to MPI program execution time prediction

EasyChair Preprint no. 3978

8 pagesDate: July 30, 2020


The problem of MPI programs execution time prediction on a certain set of computer installations is considered. This problem emerges with orchestration and provisioning a virtual infrastructure in a cloud computing environment over a heterogeneous network of computer installations: supercomputers or clusters of servers (e.g. mini data centers). One of the key criteria for the effectiveness of the cloud computing environment is the time staying by the program inside the environment. This time consists of the waiting time in the queue and the execution time on the selected physical computer installation, to which the computational resource of the virtual infrastructure is dynamically mapped. One of the components of this problem is the estimation of the MPI programs execution time on a certain set of computer installations. This is necessary to determine a proper choice of order and place for program execution. The article proposes two new approaches to the program execution time prediction problem. The first one is based on computer installations grouping based on the Pearson correlation coefficient. The second one is based on vector representations of computer installations and MPI programs, so-called embeddings. The embedding technique is actively used in recommendation systems, such as for goods (Amazon), for articles (, for videos (YouTube, Netflix). The article shows how the embeddings technique helps to predict the execution time of a MPI program on a certain set of computer installations.

Keyphrases: embeddings, Ensemble, execution time prediction, matrix decomposition, MPI, Pearson correlation coefficient

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Andrey Chupakhin and Alexey Kolosov and Ruslan Smeliansky and Vitaly Antonenko and Gleb Ishelev},
  title = {New approach to MPI program execution time prediction},
  howpublished = {EasyChair Preprint no. 3978},

  year = {EasyChair, 2020}}
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