Generating Optimized Sparse Matrix Vector Product over Finite Fields

Giorgi, Pascal; Vialla, Bastien

doi:10.1007/978-3-662-44199-2_102

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…This way, "bad" performance is easily identified. SELL-C-σ was quickly adopted by the community and is in use, in pure or adapted form, in many performance-oriented projects [5,6,28,60,80].…”

Section: Summary Of the Essex-i Software Structurementioning

confidence: 99%

ESSEX: Equipping Sparse Solvers For Exascale

Alappat¹,

Alvermann²,

Basermann³

et al. 2020

Software for Exascale Computing - SPPEXA 2016-2019

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The ESSEX project has investigated programming concepts, data structures, and numerical algorithms for scalable, efficient, and robust sparse eigenvalue solvers on future heterogeneous exascale systems. Starting without the burden of legacy code, a holistic performance engineering process could be deployed across the traditional software layers to identify efficient implementations and guide sustainable software development. At the basic building blocks level, a flexible MPI+X programming approach was implemented together with a new sparse data structure (SELL-C-σ) to support heterogeneous architectures by design. Furthermore, ESSEX focused on hardware-efficient kernels for all relevant architectures and efficient data structures for block vector formulations of the eigensolvers. The algorithm layer addressed standard, generalized, and nonlinear eigenvalue problems and provided some widely usable solver implementations including a block Jacobi-Davidson algorithm, contour-based integration schemes, and filter polynomial approaches. Adding to the highly efficient kernel implementations, algorithmic advances such as adaptive precision, optimized filtering coefficients, and preconditioning have further improved time to solution. These developments

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Section: Summary Of the Essex-i Software Structurementioning

confidence: 99%

ESSEX: Equipping Sparse Solvers For Exascale

Alappat¹,

Alvermann²,

Basermann³

et al. 2020

Software for Exascale Computing - SPPEXA 2016-2019

View full text Add to dashboard Cite

show abstract

“…They generate machine code based on the matrix structure. Giorgi and Vialla [2014] generate SpMV kernels based on characteristics of the input matrix. Venkat et al [2015] address indirect loop indexing and irregular data accesses in SpMV kernels and introduce new compiler transformations and automatically generated runtime inspectors.…”

Section: Related Workmentioning

confidence: 99%

Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication

Yilmaz

Aktemur

Garzarán

et al. 2016

ACM Trans. Archit. Code Optim.

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Runtime specialization is used for optimizing programs based on partial information available only at runtime. In this paper we apply autotuning on runtime specialization of Sparse Matrix-Vector Multiplication to predict a best specialization method among several. In 91% to 96% of the predictions, either the best or the second-best method is chosen. Predictions achieve average speedups that are very close to the speedups achievable when only the best methods are used. By using an efficient code generator and a carefully designed set of matrix features, we show the runtime costs can be amortized to bring performance benefits for many real-world cases.

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Generating Optimized Sparse Matrix Vector Product over Finite Fields

Cited by 2 publications

References 10 publications

ESSEX: Equipping Sparse Solvers For Exascale

ESSEX: Equipping Sparse Solvers For Exascale

Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication

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