High performance conjugate gradient solver on multi-GPU clusters using hypergraph partitioning

Computer Science - Research and Development

Volumn 25, Issue 1-2, 2010, Pages 83-91

  Cevahir, Ali ^a   Nukada, Akira ^a   Matsuoka, Satoshi ^a,b  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^b NATIONAL INSTITUTE OF INFORMATICS   (Japan)

Author keywords

Conjugate Gradients; GPU cluster; GPU computing; Hypergraph partitioning

Indexed keywords

CONJUGATE GRADIENTS; CONJUGATE-GRADIENT SOLVERS; GPU CLUSTERS; GPU COMPUTING; HIERARCHICAL PARTITIONING; HIGH PERFORMANCE SCIENTIFIC COMPUTING; HYPERGRAPH PARTITIONING; SPARSE MATRIX-VECTOR MULTIPLICATION;

CLUSTER COMPUTING; COMMUNICATION; CONJUGATE GRADIENT METHOD; SCALABILITY;

PROGRAM PROCESSORS;

EID: 77952956190     PISSN: 18652034     EISSN: 18652042     Source Type: Journal    
DOI: 10.1007/s00450-010-0112-6     Document Type: Conference Paper

References (20)

1. Baskaran MM, Bordawekar R (2008) Optimizing sparse matrix-vector multiplication on GPUs. IBM Research Report, RC24704
2. Bell N, Garland M (2009) Implementing sparse matrix-vector multiplication on throughput-oriented processors. In: Proc SC '09: ACM/IEEE Conference on Supercomputing, Portland, OR, USA
3. Buatois L, Caumon G, Lévy B (2007) Concurrent number cruncher: an efficient linear solver on the GPU. In: Proc HPCC 2007. LNCS vol 4782, pp 358-371
4. UV Catalyurek C Aykanat 1999 Hypergraph-partitioning-based decomposition for parallel sparse-matrix vector multiplication IEEE Trans Parallel Distrib Syst 10 7 673 693 10.1109/71.780863 (Pubitemid 30500688)
5. Catalyurek UV, Aykanat C (1999) A multilevel hypergraph partitioning tool, V. 3.0. Tech Rep, Dept of Comp Eng, Bilkent University
6. Catalyurek UV, Ucar B, Aykanat C On two-dimensional sparse matrix partitioning: models, methods, and a recipe. Tech Rep, OSUBMI-TR-2008-2008-n04
7. Cevahir A, Nukada A, Matsuoka S (2009) Fast conjugate gradients with multiple GPUs. Lecture notes in computer science, vol 5544. Springer, Berlin, pp 898-903
8. Che S, Li J, Sheaffer JW, Skadron K, Lach J (2008) Accelerating compute intensive applications with GPUs and FPGAs. In: Proc IEEE symposium on application specific processors (SASP)
9. Fan Z, Qiu F, Kaufman A, Stover SY (2004) GPU cluster for high performance computing. In: Proc SC'04: ACM/IEEE conference on supercomputing
10. D Göddeke R Strzodka J Mohd-Yusof P McCormick SHM Buijssen M Grajewski S Turek 2007 Exploring weak scalability for FEM calculations on a GPU-enhanced cluster Parallel Comput 33 10-11 685 699
11. GraphStream Inc.: GraphStream Scalable Computing Platforms. http://www.graphstream.com. Accessed 2009
12. Harris M (2007) Optimizing parallel reduction in CUDA. NVIDIA Developer Technology
13. Hartley TDR, Catalyurek UV, Ruiz A, Ujaldon M, Igual F, Mayo R (2008) Biomedical image analysis on a cooperative cluster of GPUs and multicores. In: Proc 22nd ACM international conference on supercomputing, pp 15-25
14. Lengauer T (1990) Combinatorial algorithms for integrated circuit layout. Wiley, Chichester
15. Matsuoka S (2008) The road to TSUBAME and beyond. Petascale computing: algorithms and applications. Computational science series. Chapman & Hall/CRC, London, pp 289-310
16. Matsuoka S, Aoki T, Endo T, Nukada A, Kato T, Hasegawa A GPU-accelerated computing-from hype to mainstream, the rebirth of vector computing. J Phys Conf Ser 180 (2009)
17. NVIDIA Corporation (2007) NVIDIA CUDA compute unified device architecture programming guide
18. Saad Y (1990) SPARSKIT: A basic tool kit for sparse matrix computation. Tech Rep CSRD TR 1029, University of Illinois, Urbana, IL
19. Teresco JD, Faik J, Flaherty JR (2004) Hierarchical partitioning and dynamic load balancing for scientific computation. In: Proc PARA'04, pp 911-920
20. University of Florida Sparse Matrix Collection. http://www.cise.ufl.edu/ research/sparse/matrices/