Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

MVAPICH2-AutoTune: An Automatic Collective Tuning Framework for the MVAPICH2 MPI Library

Srivastava, Siddhartha
http://rave.ohiolink.edu/etdc/view?acc_num=osu1619124494282218

Abstract Details

2021, Master of Science, Ohio State University, Computer Science and Engineering.
The Message Passing Interface (MPI) is a popular parallel programming interface for developing scientific applications. These applications rely a lot on MPI for performance. Collective operations like MPI_Allreduce, MPI_Alltoall, and others, provide an abstraction for group communication on High-Performance Computing (HPC) systems. MVAPICH2 is a popular open-source high-performance implementation of the MPI standard that provides advanced designs for these collectives through various algorithms. These collectives are highly optimized to provide the best performance on different existing and emerging architectures. To provide the best performance, the right algorithm must be chosen for a collective. Choosing the best algorithm depends on many factors like the architecture of the system, the scale at which the application is run, etc. This process of choosing the best algorithm is called tuning of the collective. But tuning of the collective takes a lot of time and using static tables may not lead to the best performance. To solve this issue, we have designed an “Autotuning Framework”. The proposed Autotuning Framework selects the best algorithm for a collective during runtime without having to rely on the previous static tuning of the MVAPICH2 library for the system. Experimental results have shown a performance increase of up to 3X while using the Autotuning Framework version of the MVAPICH2 library versus an untuned MVAPICH2 library for collectives.
Dhabaleswar K. Panda (Advisor)
Radu Teodorescu (Committee Member)
Hari Subramoni (Committee Member)
61 p.
Computer Engineering; Computer Science
MPI; Message Passing Interface; MVAPICH2; Tuning; Autotuning; Collectives; Allreduce; Reduce; Gather; Allgather; Bcast; Alltoall; Scatter; MVAPICH2-AutoTune; Tuning Framework; HPC; High-Performance Computing;

Recommended Citations

Citations

  • Srivastava, S. (2021). MVAPICH2-AutoTune: An Automatic Collective Tuning Framework for the MVAPICH2 MPI Library [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619124494282218

    APA Style (7th edition)

  • Srivastava, Siddhartha. MVAPICH2-AutoTune: An Automatic Collective Tuning Framework for the MVAPICH2 MPI Library. 2021. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1619124494282218.

    MLA Style (8th edition)

  • Srivastava, Siddhartha. "MVAPICH2-AutoTune: An Automatic Collective Tuning Framework for the MVAPICH2 MPI Library." Master's thesis, Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619124494282218

    Chicago Manual of Style (17th edition)

osu1619124494282218
142
© 2021, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.