Innovative Computing Laboratory

Overview

The Asynchronous Iterative Solvers for Extreme-Scale Computing (AsyncIS) project aims to explore more efficient numerical algorithms by decreasing their overhead. AsyncIS does this by replacing the outer Krylov subspace solver with an asynchronous optimized Schwarz method, thereby removing the global synchronization and bulk synchronous operations typically used in numerical codes.
AsyncIS, a DOE-funded collaboration between Georgia Tech, UTK, Temple University, and SNL, also focuses on the development and optimization of asynchronous preconditioners (i.e., preconditioners that are generated and/or applied in an asynchronous fashion). The novel preconditioning algorithms that provide fine-grained parallelism enable preconditioned Krylov solvers to run efficiently on large-scale distributed systems and many-core accelerators like GPUs.

Papers

Li, J., G. Bosilca, A. Bouteiller, and B. Nicolae, “Elastic deep learning through resilient collective operations,” SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, Denver, CO, ACM, November 2023.
Schuchart, J., P. Samfass, C. Niethammer, J. Gracia, and G. Bosilca, “Callback-based completion notification using MPI Continuations,” Parallel Computing, vol. 21238566, issue 0225, pp. 102793, May Jan.
Schuchart, J., C. Niethammer, J. Gracia, and G. Bosilca, “Quo Vadis MPI RMA? Towards a More Efficient Use of MPI One-Sided Communication,” EuroMPI'21, Garching, Munich Germany, 2021.  (835.27 KB)
Hori, A., T. Ogura, B. Gerofi, J. Yin, Y. Ishikawa, E. Jeannot, and G. Bosilca, A Report of the MPI International Survey (Poster) , Austin, TX, EuroMPI/USA '20: 27th European MPI Users' Group Meeting, September 2020.
Luo, X., W. Wu, G. Bosilca, Y. Pei, Q. Cao, T. Patinyasakdikul, D. Zhong, and J. Dongarra, “HAN: A Hierarchical AutotuNed Collective Communication Framework,” IEEE Cluster Conference, Kobe, Japan, Best Paper Award, IEEE Computer Society Press, September 2020.  (764.05 KB)
Zhong, D., Q. Cao, G. Bosilca, and J. Dongarra, “Using Advanced Vector Extensions AVX-512 for MPI Reduction,” EuroMPI/USA '20: 27th European MPI Users' Group Meeting, Austin, TX, September 2020.  (634.45 KB)
Zhong, D., P. Shamis, Q. Cao, G. Bosilca, and J. Dongarra, “Using Arm Scalable Vector Extension to Optimize Open MPI,” 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID 2020), Melbourne, Australia, IEEE/ACM, May 2020.  (359.95 KB)
Patinyasakdikul, T., D. Eberius, G. Bosilca, and N. Hjelm, “Give MPI Threading a Fair Chance: A Study of Multithreaded MPI Designs,” IEEE Cluster, Albuquerque, NM, IEEE, September 2019.  (220.84 KB)
Losada, N., G. Bosilca, A. Bouteiller, P. González, and M. J. Martín, “Local Rollback for Resilient MPI Applications with Application-Level Checkpointing and Message Logging,” Future Generation Computer Systems, vol. 91, pp. 450-464, February 2019.  (1.16 MB)
Bernholdt, D. E., S. Boehm, G. Bosilca, M G. Venkata, R. E. Grant, T. Naughton, H. P. Pritchard, M. Schulz, and G. R. Vallee, “A Survey of MPI Usage in the US Exascale Computing Project,” Concurrency Computation: Practice and Experience, September 2018.  (359.54 KB)
Castain, R., J. Hursey, A. Bouteiller, and D. Solt, “PMIx: Process Management for Exascale Environments,” Parallel Computing, vol. 79, pp. 9–29, January 2018.
Castain, R. H., D. Solt, J. Hursey, and A. Bouteiller, “PMIx: Process Management for Exascale Environments,” Proceedings of the 24th European MPI Users' Group Meeting, New York, NY, USA, ACM, pp. 14:1–14:10, 2017.

Presentations

Hori, A., T. Ogura, B. Gerofi, J. Yin, Y. Ishikawa, E. Jeannot, and G. Bosilca, A Report of the MPI International Survey (Poster) , Austin, TX, EuroMPI/USA '20: 27th European MPI Users' Group Meeting, September 2020.

ICL Team Members

George Bosilca
Research Associate Professor
Aurelien Bouteiller
Research Assistant Professor
Jack Dongarra
Research Professor Emeritus
Thomas Herault
Research Assistant Professor
Joseph Schuchart
Visiting Scholar

In Collaboration With

Georgia Tech University
Sandia National Laboratories
Sponsored By
The United States Department of Energy
Exascale Computing Project

AsyncIS is part of ICL's involvement in the Exascale Computing Project (ECP). The ECP was established with the goals of maximizing the benefits of high-performance computing (HPC) for the United States and accelerating the development of a capable exascale computing ecosystem. Exascale refers to computing systems at least 50 times faster than the nation’s most powerful supercomputers in use today.

The ECP is a collaborative effort of two U.S. Department of Energy organizations – the Office of Science (DOE-SC) and the National Nuclear Security Administration (NNSA).