MPI-ACC: Accelerator-Aware MPI for Scientific Applications

Ashwin M. Aji, Lokendra S. Panwar, Feng Ji, Karthik Murthy, Milind Chabbi, Pavan Balaji, Keith R. Bisset, James Dinan, Wu Chun Feng, John Mellor-Crummey, Xiaosong Ma, Rajeev Thakur

Research output: Contribution to journalArticle

9 Citations (Scopus)


Data movement in high-performance computing systems accelerated by graphics processing units (GPUs) remains a challenging problem. Data communication in popular parallel programming models, such as the Message Passing Interface (MPI), is currently limited to the data stored in the CPU memory space. Auxiliary memory systems, such as GPU memory, are not integrated into such data movement standards, thus providing applications with no direct mechanism to perform end-to-end data movement. We introduce MPI-ACC, an integrated and extensible framework that allows end-to-end data movement in accelerator-based systems. MPI-ACC provides productivity and performance benefits by integrating support for auxiliary memory spaces into MPI. MPI-ACC supports data transfer among CUDA, OpenCL and CPU memory spaces and is extensible to other offload models as well. MPI-ACC's runtime system enables several key optimizations, including pipelining of data transfers, scalable memory management techniques, and balancing of communication based on accelerator and node architecture. MPI-ACC is designed to work concurrently with other GPU workloads with minimum contention. We describe how MPI-ACC can be used to design new communication-computation patterns in scientific applications from domains such as epidemiology simulation and seismology modeling, and we discuss the lessons learned. We present experimental results on a state-of-the-art cluster with hundreds of GPUs; and we compare the performance and productivity of MPI-ACC with MVAPICH, a popular CUDA-aware MPI solution. MPI-ACC encourages programmers to explore novel application-specific optimizations for improved overall cluster utilization.

Original languageEnglish
Article number7127020
Pages (from-to)1401-1414
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number5
Publication statusPublished - 1 May 2016
Externally publishedYes



  • concurrent programming
  • distributed architectures
  • Heterogeneous (hybrid) systems
  • parallel systems

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Computational Theory and Mathematics

Cite this

Aji, A. M., Panwar, L. S., Ji, F., Murthy, K., Chabbi, M., Balaji, P., Bisset, K. R., Dinan, J., Feng, W. C., Mellor-Crummey, J., Ma, X., & Thakur, R. (2016). MPI-ACC: Accelerator-Aware MPI for Scientific Applications. IEEE Transactions on Parallel and Distributed Systems, 27(5), 1401-1414. [7127020].