Abstract Machine Models and Proxy Architectures for Exascale Computing
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
To achieve exascale computing, fundamental hardware architectures must change. The most significant consequence of this assertion is the impact on the scientific and engineering 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 elucidate 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. In addition, through the use of proxy architectures, we can enable a more concrete exploration of how well new and evolving application codes map onto future architectures. This second version of the document addresses system scale considerations and provides a system-level abstract machine model with proxy architecture information.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- DOE Contract Number:
- AC04-94AL85000; AC02-05CH11231
- OSTI ID:
- 1561498
- Report Number(s):
- SAND-2016-6049; 642596
- Country of Publication:
- United States
- Language:
- English
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