Lattice QCD with domain decomposition on Intel® Xeon Phi™ co-processors

The gap between the cost of moving data and the cost of computing continues to grow, making it ever harder to design iterative solvers on extreme-scale architectures. This problem can be alleviated by alternative algorithms that reduce the amount of data movement. We investigate this in the context...

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Bibliographic Details
Published in:Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis pp. 69 - 80
Main Authors: Heybrock, Simon, Joó, Bálint, Kalamkar, Dhiraj D., Smelyanskiy, Mikhail, Vaidyanathan, Karthikeyan, Wettig, Tilo, Dubey, Pradeep
Format: Conference Proceeding
Language:English
Published: Piscataway, NJ, USA IEEE Press 16.11.2014
IEEE
Series:ACM Conferences
Subjects:
and very la
Applied computing > Physical sciences and engineering > Physics
Computing methodologies > Symbolic and algebraic manipulation > Symbolic and algebraic algorithms > Linear algebra algorithms
Domain decomposition
G.1.3 [Numerical Analysis]: Numerical Linear Algebra Sparse
Gold
Intel® Xeon Phi coprocessor
Jacobian matrices
Lattice QCD Categories and subject descriptors: D.3.4 [Programming Languages]: Processors Optimization
Lattices
Layout
Linear systems
Mathematics of computing > Mathematical analysis > Numerical analysis > Computations on matrices
Mathematics of computing > Mathematical software
Prefetching
Software and its engineering > Software notations and tools > Compilers
structured
Vectors
Xeon Phi
domain decomposition
Intel
coprocessor
lattice QCD
ISBN:1479955000, 9781479955008
ISSN:2167-4329
Online Access:Get full text
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Summary:The gap between the cost of moving data and the cost of computing continues to grow, making it ever harder to design iterative solvers on extreme-scale architectures. This problem can be alleviated by alternative algorithms that reduce the amount of data movement. We investigate this in the context of Lattice Quantum Chromodynamics and implement such an alternative solver algorithm, based on domain decomposition, on Intel® Xeon Phi™ co-processor (KNC) clusters. We demonstrate close-to-linear on-chip scaling to all 60 cores of the KNC. With a mix of single- and half-precision the domain-decomposition method sustains 400-500 Gflop/s per chip. Compared to an optimized KNC implementation of a standard solver [1], our full multi-node domain-decomposition solver strong-scales to more nodes and reduces the time-to-solution by a factor of 5.
ISBN:1479955000
9781479955008
ISSN:2167-4329
DOI:10.1109/SC.2014.11