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Exascale Combustion Co-Design Center (ExaCT)

LBNL is a key member of the Combustion Co-Design Center (ExaCT).  Our work represents a collaboration between applied mathematicians and computational scientists who have developed the Low Mach number combustion code (LMC), and computer scientists focused on performance optimization through auto-tuning and DSLs, performance modeling, and architectural simulation.

LBL Researchers

 

Proxy Apps

  • HPGMG-FV (A finite-volume multigrid solver that supports variable coefficient elliptic solves typical of those required by a low Mach number combustion algorithm)
  • AMR_Exp_Parabolic (explicit AMR with subcycling in time)

 

Documents

 

Publications

2016

Samuel Williams, Mark Adams, Brian Van Straalen, Performance Portability in Hybrid and Heterogeneous Multigrid Solvers, Copper Moutain, March 2016,

2015

Didem Unat, Cy Chan, Weiqun Zhang, Samuel Williams, John Bachan, John Bell, John Shalf, "ExaSAT: An Exascale Co-Design Tool for Performance Modeling", International Journal of High Performance Computing Applications (IJHPCA), May 2015, doi: 10.1177/1094342014568690

2014

Samuel Williams, HPGMG-FV, FastForward2 Proxy App Presentation, December 2014,

J.A. Ang, R.F. Barrett, R.E. Benner, D. Burke, C. Chan, D. Donofrio, S.D. Hammond, K.S. Hemmertand S.M. Kelly, H. Le, V.J. Leung, D.R. Resnick, A.F. Rodrigues, J. Shalf, D. Stark, andN.J. Wright D. Unat, "Abstract Machine Models and Proxy Architectures for Exascale Computing", Co--HPC2014 (to appear), New Orleans, LA, USA, IEEE Computer Society, November 17, 2014,

To achieve Exascale computing, fundamental hardware architectures must change. The most significant consequence of this assertion is the impact on the scientific applications that run on current High Performance Computing (HPC) systems, many of which codify years of scientific domain knowledge and refinements for contemporary computer systems. In order to adapt to Exascale architectures, developers must be able to reason about new hardware and determine what programming models and algorithms will provide the best blend of performance and energy efficiency into the future. While many details of the Exascale architectures are undefined, an abstract machine model is designed to allow application developers to focus on the aspects of the machine that are important or relevant to performance and code structure. These models are intended as communication aids between application developers and hardware architects during the co-design process. We use the term proxy architecture to describe a parameterized version of an abstract machine model, with the parameters added to ellucidate potential speeds and capacities of key hardware components. These more detailed architectural models are formulated to enable discussion between the developers of analytic models and simulators and computer hardware architects. They allow for application performance analysis and hardware optimization opportunities. In this report our goal is to provide the application development community with a set of models that can help software developers prepare for Exascale and through the use of proxy architectures, we can enable a more concrete exploration of how well application codes map onto the future architectures. 

Hongzhang Shan, Amir Kamil, Samuel Williams, Yili Zheng, Katherine Yelick, "Evaluation of PGAS Communication Paradigms with Geometric Multigrid", 8th International Conference on Partitioned Global Address Space Programming Models (PGAS), October 2014, doi: 10.1145/2676870.2676874

Didem Unat, George Michelogiannakis, John Shalf, The Role of Modeling in Locality Optimizations, Modeling and simulation workshop (MODSIM), August 2014,

J.A. Ang, R.F. Barrett, R.E. Benner, D. Burke, C. Chan, D. Donofrio, S.D. Hammond, K.S. Hemmert, S.M. Kelly, H. Le, V.J. Leung, D.R. Resnick, A.F. Rodrigues, J. Shalf, D. Stark, D. Unat, N.J. Wright, "Abstract Machine Models and Proxy Architectures for Exascale Computing", May 16, 2014,

Samuel Williams, Mike Lijewski, Ann Almgren, Brian Van Straalen, Erin Carson, Nicholas Knight, James Demmel, "s-step Krylov subspace methods as bottom solvers for geometric multigrid", Parallel and Distributed Processing Symposium, 2014 IEEE 28th International, January 2014, 1149--1158, doi: 10.1109/IPDPS.2014.119

2013

Didem Unat, Cy Chan, Weiqun Zhang, John Bell and John Shalf, Tiling as a Durable Abstraction for Parallelism and Data Locality, Workshop on Domain-Specific Languages and High-Level Frameworks for High Performance Computing, November 18, 2013,

Cy Chan, Didem Unat, Michael Lijewski, Weiqun Zhang, John Bell, John Shalf, "Software Design Space Exploration for Exascale Combustion Co-Design", International Supercomputing Conference (ISC), Leipzig, Germany, June 16, 2013,