Algorithmic performance studies on graphics processing units

Journal of Parallel and Distributed Computing

Volumn 68, Issue 10, 2008, Pages 1360-1369

  Schenk, Olaf ^a   Christen, Matthias ^a   Burkhart, Helmar ^a  

^a UNIVERSITY OF BASEL   (Switzerland)

Author keywords

Graphics processing units; Matrix decomposition; Nonlinear optimization; Parallel processing; Sparse direct solvers

Indexed keywords

APPLICATIONS; COLOR IMAGE PROCESSING; CONSTRAINED OPTIMIZATION; CONSTRAINT THEORY; DIGITAL ARITHMETIC; IMAGE CODING; INTEGRATION; OPTIMIZATION; PARTIAL DIFFERENTIAL EQUATIONS; RHENIUM COMPOUNDS;

GRAPHICS PROCESSING UNITS; MATRIX DECOMPOSITION; NONLINEAR OPTIMIZATION; PARALLEL PROCESSING; SPARSE DIRECT SOLVERS;

MATRIX ALGEBRA;

EID: 51449090534     PISSN: 07437315     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jpdc.2008.05.008     Document Type: Article

References (32)

1. AMD Stream Processor: http://ati.amd.com/products/streamprocessor/index.html
2. Amestoy P.R., Duff I.S., L'Excellent J.-Y., and Koster J. A fully asynchronous multifrontal solver using distributed dynamic scheduling. SIAM Journal of Matrix Analysis and Applications 23 1 (2001) 15-41
3. R.G. Belleman, J. Bedorf, S.P. Zwart, High performance direct gravitational N-body simulations on graphics processing units-II : An implementation in CUDA, New Astronomy, 2007 (in press)
4. Bolz J., Farmer I., Grinspun E., and Schröder P. Sparse matrix solvers on the GPU: Conjugate gradients and multigrid. ACM Transactions on Graphics (TOG) 22 3 (2003) 917-924
5. T. Davis, University of Florida Sparse Matrix Collection, University of Florida, Gainesville. http://www.cise.ufl.edu/davis/sparse/
6. Davis T. Direct Methods for Sparse Linear Systems (2006), SIAM
7. Dongarra J.J., DuCroz J., Duff I., and Hammarling S. A set of level 3 basic linear algebra subprograms. ACM Transactions on Mathematical Software 16 1 (1990) 1-28
8. N. Galoppo, N.K. Govindaraju, M. Henson, D. Manocha, LU-GPU: Efficient algorithms for solving dense linear systems on graphics hardware, in: ACM/IEEE SC 2005 Conference, SC'05, 2005, p. 3
9. Göddeke D., Strzodka R., and Turek S. Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations. International Journal of Parallel, Emergent and Distributed Systems 22 4 (2007) 221-256
10. Gould N.I.M., Hu Y., and Scott J.A. A numerical evaluation of sparse direct solvers for the solution of large sparse, symmetric linear systems of equations. ACM Transactions on Mathematical Software (TOMS) 33 2 (2007)
11. GPUBench: A benchmark suite designed to analyze the performance of programmable graphics processors: http://graphics.stanford.edu/projects/gpubench
12. Jin Hyuk Jung, Dianne P. O'Leary, Implementing an interior point method for linear programs on a CPU-GPU system, in: Electronic Transactions on Numerical Analysis, ETNA, 2008 (in press)
13. Intel Math Kernel Library 9.1 - Sparse Solvers: http://www.intel.com/cd/software/products/asmo-na/eng/266853.htm
14. Ito K., and Kunisch K. Augmented Lagrangian-SQP methods for nonlinear optimal control problems of tracking type. SIAM Journal of Optimization 6 (1996) 96-125
15. Andreas Kolb, Nicolas Cuntz, Dynamic particle coupling for gpu-based fluid simulation, in: Proceedings 18th Symposium on Simulation Techniques, 2005, pp. 722-727
16. Krüger J., and Westermann R. Linear algebra operators for GPU implementation of numerical algorithms. ACM Transactions on Graphics (TOG) 22 3 (2003) 908-916
17. J. Kurzak, J. Dongarra, Implementation of the mixed-precision high performance LINPACK Benchmark on the CELL Processor. Technical report UT-CS-06-580, LAPACK Working Note 177, University of Tennessee Computer Science, September 2006
18. Michael McCool, Data-parallel programming on the cell BE and the GPU using the RapidMind development platform, GSPx Multicore Applications Conference, Santa Clara, Oct. 31 to Nov. 2, 2006
19. Microsoft's DirectX - A collection of application programming interfaces for handling tasks related to multimedia, especially game programming and video, on Microsoft platforms: http://www.microsoft.com/windows/directx
20. Ng E.G., and Peyton B.W. Block sparse Cholesky algorithms on advanced uniprocessor computers. SIAM Journal on Scientific Computing 14 (1993) 1034-1056
21. NVIDIA CUDA Homepage: http://developer.nvidia.com/object/cuda.html
22. OpenGL-The Industry's Foundation for High Performance Graphics: http://www.opengl.org
23. Owens J.D., Luebke D., Govindaraju N., Harris M., Krüger J., Lefohn A.E., and Purcell T.J. A survey of general-purpose computation on graphics hardware. Computer Graphics Forum 34 March (2007) 80-113
24. Rothberg E. Performance of panel and block approaches to sparse Cholesky factorization on the iPSC/860 and Paragon multicomputers. SIAM Journal on Scientific Computing 17 3 (1996) 699-711
25. E. Rothberg, A. Gupta, Techniques for improving the performance of sparse matrix factorization on multiprocessor workstations, in: Supercomputing '90. ACM-IEEE, 1990
26. Schenk O., and Gärtner K. Solving unsymmetric sparse systems of linear equations with PARDISO. Journal of Future Generation Computer Systems 20 3 (2004) 475-487
27. Schenk O., and Gärtner K. On fast factorization pivoting methods for symmetric indefinite systems. Electronic Transactions on Numerical Analysis 23 1 (2006) 158-179
28. Shubhabrata Sengupta, Mark Harris, Yao Zhang, John D. Owens, Scan primitives for GPU computing, in: Proceedings Graphics Hardware 2007, August 2007, pp. 97-106
29. The cell project at IBM research: http://www.research.ibm.com/cell/
30. Wächter A., and Biegler L.T. On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming. Mathematical Programming 106 1 (2006) 25-57
31. Samuel W. Williams, Leonid Oliker, Richard Vuduc, Katherine Yelick, James Demmel, John Shalf, Optimization of sparse matrix-vector multiplication on emerging multicore platforms, in: Proceedings of Supercomputing '07, Nov. 2007
32. Yasuda K. Two-electron integral evaluation on the graphics processor unit. Journal of Computational Chemistry (2007)