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A Fully Structure-Driven Performance Analysis of Sparse Matrix-Vector Multiplication

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A Fully Structure-Driven Performance Analysis of Sparse Matrix-Vector Multiplication A Fully Structure-Driven Performance Analysis of Sparse Matrix-Vector Multiplication Prabhjot Sandhu Clark Verbrugge Laurie Hendren McGill University McGill University McGill University School of Computer Science School of Computer Science School of Computer Science Montréal, QC, Canada Montréal, QC, Canada Montréal, QC, Canada [email protected] [email protected] [email protected] ABSTRACT Engineering (ICPE ’20), April 20–24, 2020, Edmonton, AB, Canada. ACM, New Sparse matrix-vector multiplication (SpMV) is an important kernel York, NY, USA, 12 pages. https://doi.org/10.1145/3358960.3379131 in many scientific, machine-learning, and other compute-intensive applications. Performance characteristics, however, depend on a 1 INTRODUCTION complex combination of storage format, machine capabilities, and Recently, the performance capability of the web has been elevated choices in code-generation. A deep understanding of the relative with the addition of WebAssembly [15] to the world of JavaScript. impact of these properties is important in itself, and also to better It provides opportunities for the efficient execution of sophisticated understanding the performance potential of alternative execution and compute-intensive applications on the web, such as image edit- contexts such as web-based scientific computing, where the recent ing [7], computer-aided design [6], augmented reality [1, 2], text introduction of WebAssembly offers the potential for low-level, near- classification3 [ , 5, 21] and deep learning [25]. A number of these native performance within a web browser. applications [32, 35] involve sparse matrix computations which are In this work we characterize the performance of SpMV operations considered important for their performance. Sparse matrix-vector for different sparse storage formats based on the sparse matrix struc- multiplication (SpMV) is one such critical operation which computes ture and the machine architecture. We extract structural properties y =Ax, where matrix A is sparse and vector x is dense. It dominates from 2000 real-life sparse matrices to understand their impact on the the overall application run-time through its recurring nature which choice of storage format and also on the performance within those makes it a good candidate for optimization. Since the matrices in storage formats for both WebAssembly and native C. We extend this these applications are large and sparse with different sparsity char- with new matrix features based on a “reuse-distance” concept to acteristics, a single sparse storage format is not appropriate for all of identify performance bottlenecks, and evaluate the effect of interac- them. The choice of storage format, however, can have a significant tion between the matrix structure and hardware characteristics on impact on the SpMV performance based on the sparsity structure of SpMV performance. Our study provides valuable insights to scien- the matrix and the available code optimization opportunities. There- tific programmers and library developers to apply best practices and fore, selecting an optimal format to store the input matrix at run-time guide future optimization for SpMV in general, and in particular for is one way to optimize SpMV. Another way is to apply different data web-based contexts with abstracted hardware and storage models. structure optimizations and low-level code optimizations to a single format. It sometimes results in the derivation of new formats tailored CCS CONCEPTS for even more specific types of matrices [18, 22, 24, 27]. However, • Mathematics of computing → Computations on matrices; • Soft- both of these techniques require a thorough understanding of how ware and its engineering → Dynamic compilers. the performance of SpMV operation is affected by the structure of the matrix and the machine characteristics. KEYWORDS Previous work on the performance and storage format choice for SpMV has either focused on native languages like C or only Sparse Matrix-Vector Multiplication, SpMV, Matrix Structure, Reuse- been able to highlight general differences between native C and Distance, WebAssembly, C, Performance, Scientific Computing web languages, JavaScript and WebAssembly. In this paper we focus ACM Reference Format: on understanding the performance of the SpMV operation solely PrabhjotSandhu,ClarkVerbrugge,andLaurieHendren.2020.AFullyStructure- based on the structural properties of the matrices for both C and We- Driven Performance Analysis of Sparse Matrix-Vector Multiplication. In bAssembly. We performed rigorous experiments on a set of almost Proceedings of the 2020 ACM/SPEC International Conference on Performance 2000 real-life sparse matrices, storing them in the four most popular storage formats (COO, CSR, DIA and ELL). Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed Our investigation is structured in terms of three main research for profit or commercial advantage and that copies bear this notice and the full citation questions. Our first research question, RQ1, evaluates the impact on the first page. Copyrights for components of this work owned by others than ACM of the structure of the matrix on the choice of the storage format must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a for the SpMV operation for both WebAssembly and C. We employ fee. Request permissions from [email protected]. an x%-affinity criteria [28] to obtain the set of matrices best suited ICPE ’20, April 20–24, 2020, Edmonton, AB, Canada for each format category. We extract some useful sparse structure © 2020 Association for Computing Machinery. ACM ISBN 978-1-4503-6991-6/20/04...$15.00 features which contribute to build the affinity of our benchmark https://doi.org/10.1145/3358960.3379131 matrices towards the given formats. The results from RQ1 showed that these structure features indeed govern the choice of storage for- COO : DIA : 1 0 6 0 mat. However, one storage format can have a priority over the other row 0 0 1 1 2 3 3 - - - 5 0 2 0 7 data 1 2 3 4 format due to the availability of some interesting code optimizations A 0 0 3 0 col 0 2 1 3 2 0 3 6 7 - - in modern architecture. 5 0 0 4 Unanswered questions about the variation in SpMV performance val 1 6 2 7 3 5 4 offset -3 0 2 of the matrices with similar feature values led us to our second re- ELL : search question, RQ2, which explores more structure features to CSR : 1 2 3 5 explain the effect of the sparse matrix structure on the SpMV perfor- row_ptr 0 2 4 5 7 data 6 7 - 4 mance within each storage format for both WebAssembly and C. We col 0 2 1 3 2 0 3 modeled data locality for SpMV ELL and CSR execution using the 0 1 2 0 val 1 6 2 7 3 5 4 indices reuse-distance concept [12] and introduced some new features like 2 3 - 3 ELL Locality Index and CSR Locality Index to explain the performance bottlenecks for the given matrices based on their structure. Figure 1: A simple example for sparse matrix formats Features from RQ1 and RQ2 have their foundation in the hard- ware architecture. This drives our final research question, RQ3, aimed at measuring hardware performance properties to understand another compilation architecture. Current versions provide some the interaction between the sparse matrix structure and hardware low-level machine features, such as 128-bit SIMD support, with on- features. The results of this question validate our evaluations and going development aimed at extending this to longer SIMD types parameter choices from RQ1 and RQ2. and adding task parallelism through a thread model [4]. V8, the open-source JavaScript engine for Chrome browser, is reusing its 2 BACKGROUND AND RELATED WORK top-tier optimizing JITs from JavaScript for WebAssembly [15]. In terms of performance, Haas et. al [15] reported a performance Our work requires background knowledge of the core sparse matrix within 2x of native C for all benchmarks in the PolyBenchC suite, storage formats, as well as the basics of WebAssembly. After a brief with 7 of them within 10% of native C. Further experiments by Her- presentation of these concepts, we describe related work on SpMV rera et. al [17] using the Ostrich Numerical Benchmark set showed performance analysis. similar performance numbers across many devices, with one device 2.1 Sparse Matrix Formats achieving better overall performance than native C. Recently, Jangda et al. [19] analyzed the performance across the SPEC CPU bench- Sparse matrices are characterized by an abundance of 0-entries rel- mark suite and reported a mean slowdown factor less than 2x along ative to a small number of non-zero values. Storage formats aim with the causes of these performance gaps. to minimize space requirements, with different formats aimed at different kinds of matrix structure. Figure 1 uses a small example to 2.3 Related Work illustrate encodings in the four core formats we address in this study, with details discussed below. The feature-based aspect of our work of SpMV performance analysis on modern systems and different execution environments is inspired Coordinate Format (COO) is the simplest storage format. It con- from the research which dates back to nearly three decades ago. In row col val sists of three arrays, , and to store the row and column the early 1990s, Agarwal et al. [8] presented a feature extraction indices and corresponding values of each non-zero entry. scheme for SpMV with nearly dense submatrices, nearly dense di- Compressed Sparse Row Format (CSR) is the most widely used agonals and rows with a large number of non-zeros as its features to row_ptr format. It is the compressed version of COO format, using a examine sparsity structure in the pre-processing phase which could array with only one entry per row.
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