2010/10/19 by Niket K. Choudhary, Rakesh Ginjupalli, Choudhary, Niket K. +5 · 1 citation
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Advanced Numerical Methods in Computational Mathematics #Computational Physics (physics.comp-ph) #Distributed #FOS: Computer and information sciences #FOS: Physical sciences #General Relativity and Quantum Cosmology (gr-qc) #Model Reduction and Neural Networks #Numerical methods for differential equations #Parallel #and Cluster Computing (cs.DC) #cs.DC #gr-qc #physics.comp-ph
paper · pdf · doi:10.48550/arxiv.1010.3816
6 pages, 1 table; Accepted for publication in Parallel and Distributed Computing and Systems (PDCS 2011)
openalex publication_date 2010/10/19 · arxiv created 2011/10/03 · arxiv updated 2011/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Currently there is considerable interest in making use of many-core processor architectures, such as Nvidia and AMD graphics processing units (GPUs) for scientific computing. In this work we explore the use of the Open Computing Language (OpenCL) for a typical Numerical Relativity application: a time-domain Teukolsky equation solver (a linear, hyperbolic, partial differential equation solver using finite-differencing). OpenCL is the only vendor-agnostic and multi-platform parallel computing framework that has been adopted by all major processor vendors. Therefore, it allows us to write portable source-code and run it on a wide variety of compute hardware and perform meaningful comparisons. The outcome of our experimentation suggests that it is relatively straightforward to obtain order-of-magnitude gains in overall application performance by making use of many-core GPUs over multi-core CPUs and this fact is largely independent of the specific hardware architecture and vendor. We also observe that a single high-end GPU can match the performance of a small-sized, message-passing based CPU cluster.