NDLI: Accelerating block checkerboard method on GPU for performance enhancement of 2D and 3D Quantum Monte Carlo simulations

Content Provider	IEEE Xplore Digital Library
Author	Chi-Cheng Chuang Yu-Sheng Chiu Quey-Liang Kao Zhi-Hung Chen Che-Rung Lee
Copyright Year	2012
Description	Author affiliation: Smart Network System Institute Institute for Information Industry Taipei, Taiwan (Chi-Cheng Chuang; Yu-Sheng Chiu) \|\| Department of Computer Science National Tsing Hua University Hsinchu, Taiwan (Quey-Liang Kao; Zhi-Hung Chen; Che-Rung Lee)
Abstract	Quantum Monte Carlo (QMC) simulations for the recent studies on complex materials were confronted by new computational challenges. Traditional approach to accelerate the simulations by parallel Monte Carlo chains faces serious scalability problems since the speedup is reaching the limitation predicted by Amdahl's law. Fine-grained parallelization of matrix kernels is essential to achieve better performance. In this paper, we investigate the performance optimization techniques on GPU for the most time consuming computational kernel in the Determinant Quantum Monte Carlo (DQMC) simulation: multiplication of matrix exponentials. The matrix, derived from the kinetic Hamiltonian, is highly sparse, and its exponential is approximated by the block checkerboard method, which can represent a matrix exponential as a product of a sequence of sparse matrices. The matrix exponentials from 2D and 3D toruses are focused, and various optimization techniques, such as data streaming and concurrent kernels, are proposed. Experiments show that the proposed optimization techniques can improve the SpMM (Sparse Matrix Multiplication) function, modified from the CUDA SKD SpMV function, up to 16 times and 117 times for 2D and 3D problems respectively.
Starting Page	717
Ending Page	722
File Size	470864
Page Count	6
File Format	PDF
ISBN	9781467345118
e-ISBN	9781467345101
e-ISBN	9781467345095
DOI	10.1109/CloudCom.2012.6427564
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-03
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Computational modeling Graphics processing units Kinetic theory Sparse matrices Kernel GPU Optimization Quantum Monte Carlo Simulation Matrix exponential
Content Type	Text
Resource Type	Article

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