A Framework for Performance Optimization of TensorContraction Expressions

A Framework for Performance Optimization of TensorContraction Expressions

Bibliographic Details
Main Author: Lai, Pai-Wei
Language:English
Published: The Ohio State University / OhioLINK 2014
Subjects:
Computer Science
tensor contraction
operation minimization
dynamic load-balancing
caching
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=osu1408968185
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-osu14089681852021-08-03T06:27:12Z A Framework for Performance Optimization of TensorContraction Expressions Lai, Pai-Wei Computer Science tensor contraction operation minimization dynamic load-balancing caching Attaining high performance and productivity in the evaluation of scientific applications is a challenging task for computer scientists, and is often critical in the advancement of many scientific disciplines. In this dissertation, we focus on the development of high performance, scalable parallel programs for a class of scientific computations in quantum chemistry --- tensor contraction expressions.Tensor contraction expressions are generalized forms of multi-dimensional matrix-matrix operations, which form the fundamental computational constructs in electronic structure modeling. Tensors in these computations exhibit various types of symmetry and sparsity. Contractions on such tensors are highly irregular with significant computation and communication cost, if data locality is not considered in the implementation. Prior efforts have focused on implementing tensor contractions using block-sparse representation. Many parallel programs of tensor contractions have been successfully implemented, however, their performances are unsatisfactory on emerging computer systems.In this work, we investigate into several performance bottlenecks of previous approaches, and present responding techniques to optimize operations, parallelism, workload balance, and data locality. We exploit symmetric properties of tensors to minimize the operation count of tensor contraction expressions through algebraic transformation. Rules are formulated to discover symmetric properties of intermediate tensors; cost models and algorithms are developed to reduce operation counts.Our approaches result in significant operation count reduction, compared to many other state of the art computational chemistry softwares, using examples from real-world tensor contraction expressions from the coupled cluster methods.In order to achieve high performance and scalability, multiple programming models are often used in a single application. We design a domain-specific framework which utilizes the partitioned global address space programming model for data management and inter-node communication. We employ the task parallel execution model for dynamic load balancing. Tensor contraction expressions are decomposed into a collection of computational tasks operating on tensor tiles. We eliminate most of the synchronization steps by executing independent tensor contractions concurrently, and present mechanisms to improve their data locality. Our framework shows improved performance and scalability for tensor contraction expressions from representative coupled cluster methods. 2014 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1408968185 http://rave.ohiolink.edu/etdc/view?acc_num=osu1408968185 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Computer Science
tensor contraction
operation minimization
dynamic load-balancing
caching
spellingShingle Computer Science
tensor contraction
operation minimization
dynamic load-balancing
caching
Lai, Pai-Wei
A Framework for Performance Optimization of TensorContraction Expressions
author Lai, Pai-Wei
author_facet Lai, Pai-Wei
author_sort Lai, Pai-Wei
title A Framework for Performance Optimization of TensorContraction Expressions
title_short A Framework for Performance Optimization of TensorContraction Expressions
title_full A Framework for Performance Optimization of TensorContraction Expressions
title_fullStr A Framework for Performance Optimization of TensorContraction Expressions
title_full_unstemmed A Framework for Performance Optimization of TensorContraction Expressions
title_sort framework for performance optimization of tensorcontraction expressions
publisher The Ohio State University / OhioLINK
publishDate 2014
url http://rave.ohiolink.edu/etdc/view?acc_num=osu1408968185
work_keys_str_mv AT laipaiwei aframeworkforperformanceoptimizationoftensorcontractionexpressions
AT laipaiwei frameworkforperformanceoptimizationoftensorcontractionexpressions
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