LU factorization on heterogeneous systems: an energy-efficient approach towards high performance

LU factorization on heterogeneous systems: an energy-efficient approach towards high performance Dense lower–upper (LU) factorization (hereafter referred to as LU) is a critical kernel that is widely used to solve dense linear algebra problems. Hybrid LU algorithms have been well designed to exploit the full capacity of heterogeneous systems. However, existing heterogeneous implementations are typically CPU-centric, which rely highly on CPU cores and suffer from a large amount of data transfers via the PCIe bus, and thus reduce the overall energy efficiency of the entire computer system. In this paper, we provide a coprocessor-resident implementation of LU for a heterogeneous platform to improve energy efficiency by relieving the CPUs from performing heavy load computations and avoiding excessive data transfers via PCIe. To maintain the performance, we conduct optimizations to pipeline the CPU computation, coprocessor computation, MPI communication, and PCIe transfer between the CPUs and coprocessors. The experiments on the Tianhe-2 supercomputer show that our LU implementation can compete with the highly optimized Intel MKL implementation in performance and overcome the limitations of energy efficiency. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computing Springer Journals

LU factorization on heterogeneous systems: an energy-efficient approach towards high performance

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Publisher
Springer Vienna
Copyright
Copyright © 2017 by Springer-Verlag Wien
Subject
Computer Science; Computer Science, general; Information Systems Applications (incl.Internet); Computer Communication Networks; Software Engineering; Artificial Intelligence (incl. Robotics); Computer Appl. in Administrative Data Processing
ISSN
0010-485X
eISSN
1436-5057
D.O.I.
10.1007/s00607-016-0537-2
Publisher site
See Article on Publisher Site

Abstract

Dense lower–upper (LU) factorization (hereafter referred to as LU) is a critical kernel that is widely used to solve dense linear algebra problems. Hybrid LU algorithms have been well designed to exploit the full capacity of heterogeneous systems. However, existing heterogeneous implementations are typically CPU-centric, which rely highly on CPU cores and suffer from a large amount of data transfers via the PCIe bus, and thus reduce the overall energy efficiency of the entire computer system. In this paper, we provide a coprocessor-resident implementation of LU for a heterogeneous platform to improve energy efficiency by relieving the CPUs from performing heavy load computations and avoiding excessive data transfers via PCIe. To maintain the performance, we conduct optimizations to pipeline the CPU computation, coprocessor computation, MPI communication, and PCIe transfer between the CPUs and coprocessors. The experiments on the Tianhe-2 supercomputer show that our LU implementation can compete with the highly optimized Intel MKL implementation in performance and overcome the limitations of energy efficiency.

Journal

ComputingSpringer Journals

Published: Jan 2, 2017

References

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