High-Performance Sparse Matrix-Matrix Products on Intel KNL and Multicore Architectures

High-performance computing systems continue to increase in size in the quest for ever higher performance. The resulting increased electronic component count, coupled with the decrease in feature sizes of the silicon manufacturing processes used to build these components, may result in future exascale systems being more susceptible to soft errors caused by cosmic radiation than in current high-performance computing systems. Through the use of techniques such as hardware-based error-correcting codes and checkpoint-restart, many of these faults can be mitigated at the cost of increased hardware overhead, run-time, and energy consumption that can be as much as 10–20%. Some predictions expect these overheads to continue to grow over time. For extreme scale systems, these overheads will represent megawatts of power consumption and millions of dollars of additional hardware costs, which could potentially be avoided with more sophisticated fault-tolerance techniques. In this paper we present new software-based fault tolerance techniques that can be applied to one of the most important classes of software in high-performance computing: iterative sparse matrix solvers. Our new techniques enables us to exploit knowledge of the structure of sparse matrices in such a way as to improve the performance, energy efficiency, and fault tolerance of the overall solution.

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Parallel solution of irregular, sparse matrix problems using High Performance Fortran

High-Performance Computing and Networking - Lecture Notes in Computer Science ◽

10.1007/bfb0031608 ◽

1997 ◽

pp. 360-369 ◽

Cited By ~ 1

Author(s):

E. de Sturler ◽

D. Loher

Keyword(s):

High Performance ◽

Sparse Matrix ◽

Parallel Solution ◽

High Performance Fortran ◽

Matrix Problems

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Machine Learning to Design an Auto-tuning System for the Best Compressed Format Detection for Parallel Sparse Computations

Parallel Processing Letters ◽

10.1142/s0129626421500195 ◽

2021 ◽

Author(s):

Olfa Hamdi-Larbi ◽

Ichrak Mehrez ◽

Thomas Dufaud

Keyword(s):

Machine Learning ◽

Numerical Method ◽

High Performance ◽

Programming Model ◽

Learning Algorithm ◽

Sparse Matrix ◽

Sparse Matrices ◽

Matrix Compression ◽

Target Architecture ◽

Parallel Programming Model

Many applications in scientific computing process very large sparse matrices on parallel architectures. The presented work in this paper is a part of a project where our general aim is to develop an auto-tuner system for the selection of the best matrix compression format in the context of high-performance computing. The target smart system can automatically select the best compression format for a given sparse matrix, a numerical method processing this matrix, a parallel programming model and a target architecture. Hence, this paper describes the design and implementation of the proposed concept. We consider a case study consisting of a numerical method reduced to the sparse matrix vector product (SpMV), some compression formats, the data parallel as a programming model and, a distributed multi-core platform as a target architecture. This study allows extracting a set of important novel metrics and parameters which are relative to the considered programming model. Our metrics are used as input to a machine-learning algorithm to predict the best matrix compression format. An experimental study targeting a distributed multi-core platform and processing random and real-world matrices shows that our system can improve in average up to 7% the accuracy of the machine learning.

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The Combinatorial BLAS: design, implementation, and applications

The International Journal of High Performance Computing Applications ◽

10.1177/1094342011403516 ◽

2011 ◽

Vol 25 (4) ◽

pp. 496-509 ◽

Cited By ~ 187

Author(s):

Aydın Buluç ◽

John R Gilbert

Keyword(s):

Data Mining ◽

High Performance ◽

Web Search ◽

Sparse Matrix ◽

Ease Of Use ◽

Coarse Grained ◽

Matrix Methods ◽

The Right ◽

Traditional Approaches ◽

Combinatorial Graphs

This paper presents a scalable high-performance software library to be used for graph analysis and data mining. Large combinatorial graphs appear in many applications of high-performance computing, including computational biology, informatics, analytics, web search, dynamical systems, and sparse matrix methods. Graph computations are difficult to parallelize using traditional approaches due to their irregular nature and low operational intensity. Many graph computations, however, contain sufficient coarse-grained parallelism for thousands of processors, which can be uncovered by using the right primitives. We describe the parallel Combinatorial BLAS, which consists of a small but powerful set of linear algebra primitives specifically targeting graph and data mining applications. We provide an extensible library interface and some guiding principles for future development. The library is evaluated using two important graph algorithms, in terms of both performance and ease-of-use. The scalability and raw performance of the example applications, using the Combinatorial BLAS, are unprecedented on distributed memory clusters.

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