TY - JOUR
T1 - Paradigmatic shifts for exascale supercomputing
AU - Davis, Neal E.
AU - Robey, Robert W.
AU - Ferenbaugh, Charles R.
AU - Nicholaeff, David
AU - Trujillo, Dennis P.
N1 - Funding Information:
This work was supported by Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Los Alamos National Security, LLC, for the National Nuclear Security Administration of the US Department of Energy under contract DE-AC52-06NA25396.
PY - 2012/11
Y1 - 2012/11
N2 - As the next generation of supercomputers reaches the exascale, the dominant design parameter governing performance will shift from hardware to software. Intelligent usage of memory access, vectorization, and intranode threading will become critical to the performance of scientific applications and numerical calculations on exascale supercomputers. Although challenges remain in effectively programming the heterogeneous devices likely to be utilized in future supercomputers, new languages and tools are providing a pathway for application developers to tackle this new frontier. These languages include open programming standards such as OpenCL and OpenACC, as well as widely-adopted languages such as CUDA; also of importance are high-quality libraries such as CUDPP and Thrust. This article surveys a purposely diverse set of proof-of-concept applications developed at Los Alamos National Laboratory. We find that the capability level of the accelerator computing hardware and languages has moved beyond the regular grid finite difference calculations and molecular dynamics codes. More advanced applications requiring dynamic memory allocation, such as cell-based adaptive mesh refinement, can now be addressedand with more effort even unstructured mesh codes can be moved to the GPU.
AB - As the next generation of supercomputers reaches the exascale, the dominant design parameter governing performance will shift from hardware to software. Intelligent usage of memory access, vectorization, and intranode threading will become critical to the performance of scientific applications and numerical calculations on exascale supercomputers. Although challenges remain in effectively programming the heterogeneous devices likely to be utilized in future supercomputers, new languages and tools are providing a pathway for application developers to tackle this new frontier. These languages include open programming standards such as OpenCL and OpenACC, as well as widely-adopted languages such as CUDA; also of importance are high-quality libraries such as CUDPP and Thrust. This article surveys a purposely diverse set of proof-of-concept applications developed at Los Alamos National Laboratory. We find that the capability level of the accelerator computing hardware and languages has moved beyond the regular grid finite difference calculations and molecular dynamics codes. More advanced applications requiring dynamic memory allocation, such as cell-based adaptive mesh refinement, can now be addressedand with more effort even unstructured mesh codes can be moved to the GPU.
KW - Cell-based adaptive mesh refinement
KW - Exascale computing
KW - GPGPU
KW - Heterogeneous architecture
KW - Molecular dynamics
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U2 - 10.1007/s11227-012-0789-3
DO - 10.1007/s11227-012-0789-3
M3 - Article
AN - SCOPUS:84867678300
VL - 62
SP - 1023
EP - 1044
JO - Journal of Supercomputing
JF - Journal of Supercomputing
SN - 0920-8542
IS - 2
ER -