Global communication optimization for tensor contraction expressions under memory constraints

Cociorva, Daniel; Gao, Xiaoyang; Krishnan, Sandhya; Baumgartner, Gerald; Lam, Clk; Sadayappan, P.; Ramanujam, J.

doi:10.1109/ipdps.2003.1213121

Cited by 19 publications

(13 citation statements)

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“…We use a dynamic programming algorithm to search among all combinations of loop fusions and array distributions to find the one with minimal total communication cost, that also fits within the available memory. We omit details here and refer the reader to [10], [11].…”

Section: Data Locality Optimizationmentioning

confidence: 99%

Synthesis of High-Performance Parallel Programs for a Class of ab Initio Quantum Chemistry Models

et al. 2005

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This paper provides an overview of a program synthesis system for a class of quantum chemistry computations. These computations are expressible as a set of tensor contractions and arise in electronic structure modeling. The input to the system is a a high-level specification of the computation, from which the system can synthesize high-performance parallel code tailored to the characteristics of the target architecture. Several components of the synthesis system are described, focusing on performance optimization issues that they address.

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Section: Data Locality Optimizationmentioning

confidence: 99%

Synthesis of High-Performance Parallel Programs for a Class of ab Initio Quantum Chemistry Models

et al. 2005

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“…ification of a computation expressed as a set of tensor contraction expressions and transforms it into efficient parallel code. Several compile-time optimizations are incorporated into the TCE: algebraic transformations to minimize operation counts [31,32], loop fusion to reduce memory requirements [28,30,29], spacetime trade-off optimization [10], communication minimization [11], and data locality optimization [12,13] of memory-to-cache traffic.…”

Section: The Computational Contextmentioning

confidence: 99%

Efficient synthesis of out-of-core algorithms using a nonlinear optimization solver

Krishnan

Krishnamoorthy

Baumgartner

et al. 2006

Journal of Parallel and Distributed Computing

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“…We are developing an automatic synthesis system called the Tensor Contraction Engine (TCE) [24], to generate efficient parallel programs from high level expressions, for a class of computations expressible as tensor contractions [3,6,5,7,15,16]. Often the tensors (essentially multi-dimensional arrays) are too large to fit in memory and must be diskresident.…”

Section: Motivationmentioning

confidence: 99%

Layout transformation support for the disk resident arrays framework

et al. 2006

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The Global Arrays (GA) toolkit provides a sharedmemory programming model in which data locality is explicitly managed by the programmer. It inter-operates with MPI and supports a variety of language bindings. The Disk Resident Arrays (DRA) model extends the GA programming model to secondary storage. GA and DRA together provide a convenient programming model that encourages locality-aware programming by the user, while presenting a high-level abstraction. High performance depends on the appropriate distribution of the data in the disk-resident arrays. In this paper, we discuss the addition of layout transformation support to DRA. The implementation of an efficient parallel layout transformation algorithm is done on top of existing GA/DRA functions; thus GA/DRA is itself used in implementing the enhanced DRA functionality. Experimental performance data is provided that demonstrates the effectiveness of the new layout transformation functionality.

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Global communication optimization for tensor contraction expressions under memory constraints

Cited by 19 publications

References 13 publications

Synthesis of High-Performance Parallel Programs for a Class of ab Initio Quantum Chemistry Models

Synthesis of High-Performance Parallel Programs for a Class of ab Initio Quantum Chemistry Models

Efficient synthesis of out-of-core algorithms using a nonlinear optimization solver

Layout transformation support for the disk resident arrays framework

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