Solving lattice QCD systems of equations using mixed precision solvers on GPUs
By M. Clark, R. Babich, Kipton Barros, R. Brower, C. Rebbi. Computer Physics Communications, 2009
506 citations recorded by the source corpus.
Abstract Modern graphics hardware is designed for highly parallel numerical tasks and promises significant cost and performance benefits for many scientific applications. One such application is lattice quantum chromodynamics (lattice QCD), where the main computational challenge is to efficiently solve the discretized Dirac equation in the presence of an SU ( 3 ) gauge field. Using NVIDIA's CUDA platform we have implemented a Wilson–Dirac sparse matrix–vector product that performs at up to 40, 135 and 212 Gflops for double, single and half precision respectively on NVIDIA's GeForce GTX 280 GPU. We have developed a new mixed precision approach for Krylov solvers using reliable updates which allows for full double precision accuracy while using only single or half precision arithmetic for the bulk of the computation. The resulting BiCGstab and CG solvers run in excess of 100 Gflops and, in terms of iterations until convergence, perform better than the usual defect-correction approach for mixed precision.
Subfields: Computational Theory and Mathematics, Nuclear and High Energy Physics
View Paper: https://www.semanticscholar.org/paper/06721d4acf2544a77fdb4e8972d1e424f55cb00d
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In Nuclear and High Energy Physics
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Citations in the corpus
- Excited and exotic charmonium spectroscopy from lattice QCD (2012, methodology): “Chroma [46] and QUDA [47, 48] were used to perform this work on the Lonsdale cluster maintained by the Trinity Centre for High Performance Computing funded through grants from Science Foundation Ireland (SFI), at the SFI/HEA Irish Centre for High-End Computing (ICHEC), and at Jefferson Laboratory un”
- The GPU Computing Era (2010, methodology): “Table 3 lists some representative applications along with the runtime speedups obtained for the whole application using CPUþGPU coprocessing over CPU alone, as measured by application developers.(12-22) The speedups using GeForce 8800, Tesla T8, GeForce GTX 280, Tesla T10, and GeForce GTX 285 range ”