Machine Learning and Understanding for Intelligent Extreme Scale Scientific Computing and Discovery. DOE Workshop Report, January 7–9, 2015, Rockville, MD
- Univ. of Tennessee, Knoxville, TN (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Univ. of Chicago, Chicago, IL (United States)
- North Carolina State Univ., Raleigh, NC (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Large-scale parallel simulations and data analysis drive scientific discovery across many disciplines. To drive larger and more detailed simulations, and deal with larger data volumes, exascale machines capable of 1018 operations per second are expected within the next five to ten years. However, the complexity of developing and adapting modern simulation codes for these architectures is increasing rapidly. Workloads on exascale machines will be billion-way parallel. Exploiting the full capability of the machine will require carefully assigning application tasks to cores, accelerators, deep memories, and other heterogeneous compute resources, while simultaneously optimizing for time to solution, data movement, power, and resilience. Optimizations that improve performance on one machine may slow down another. Worse, applications themselves are dauntingly complex. Production simulations comprise millions of lines of code and use sophisticated, adaptive algorithms whose performance is input-dependent. Complex workflows can couple multi-physics simulation with data preprocessing and post-processing modules.
- Research Organization:
- USDOE Office of Science (SC), Washington, D.C. (United States). Advanced Scientific Computing Research (ASCR)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI ID:
- 1471083
- Country of Publication:
- United States
- Language:
- English
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