Evaluation Criteria for Sparse Matrix Storage Formats

被引:106
作者
Langr, Daniel [1 ]
Tvrdik, Pavel [1 ]
机构
[1] Czech Tech Univ, Fac Informat Technol, Dept Comp Syst, Prague 16000, Czech Republic
关键词
Evaluation criterion; matrix-vector multiplication; memory footprint; nonzero matrix structure; sparse matrix; storage format; test matrices; VECTOR MULTIPLICATION; EFFICIENT; OPTIMIZATION; COMPRESSION; PRODUCT; MODEL;
D O I
10.1109/TPDS.2015.2401575
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
When authors present new storage formats for sparse matrices, they usually focus mainly on a single evaluation criterion, which is the performance of sparse matrix-vector multiplication (SpMV) in FLOPS. Though such an evaluation is essential, it does not allow to directly compare the presented format with its competitors. Moreover, in case that matrices are within an HPC application constructed in different formats, this criterion alone is not sufficient for the key decision whether or not to convert them into the presented format for the SpMV-based application phase. We establish ten evaluation criteria for sparse matrix storage formats, discuss their advantages and disadvantages, and provide general suggestions for format authors/evaluators to make their work more valuable for the HPC community.
引用
收藏
页码:428 / 440
页数:13
相关论文
共 80 条
[1]   An Effective Approach for Implementing Sparse Matrix-Vector Multiplication on Graphics Processing Units [J].
Abu-Sufah, Walid ;
Karim, Asma Abdel .
2012 IEEE 14TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS & 2012 IEEE 9TH INTERNATIONAL CONFERENCE ON EMBEDDED SOFTWARE AND SYSTEMS (HPCC-ICESS), 2012, :453-460
[2]  
[Anonymous], MSRTR201295
[3]  
[Anonymous], 2013, Intel Xeon Phi coprocessor architecture and tools: the guide for application developers
[4]  
[Anonymous], 2008, NVIDIA Technical Report NVR-2008-004
[5]  
[Anonymous], 1994, TEMPLATES SOLUTION L, DOI DOI 10.1137/1.9781611971538
[6]  
[Anonymous], 2011, ACTA TECH CSAV
[7]  
[Anonymous], 2005, Proceedings of the 16th Annual Workshop on Circuits, Systems and Signal Processing
[8]  
Asanovic K., 2006, The landscape of parallel computing research: A view from berkeley
[9]   An Efficient Two-Dimensional Blocking Strategy for Sparse Matrix-Vector Multiplication on GPUs [J].
Ashari, Arash ;
Sedaghati, Naser ;
Eisenlohr, John ;
Sadayappan, P. .
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON SUPERCOMPUTING, (ICS'14), 2014, :273-282
[10]   Fast Sparse Matrix-Vector Multiplication on GPUs for Graph Applications [J].
Ashari, Arash ;
Sedaghati, Naser ;
Eisenlohr, John ;
Parthasarathy, Srinivasan ;
Sadayappan, P. .
SC14: INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, 2014, :781-792