Exploring GPGPU workloads: Characterization methodology, analysis and microarchitecture evaluation implications

IEEE International Symposium on Workload Characterization, IISWC'10

Volumn , Issue , 2010, Pages

  Goswami, Nilanjan ^a   Shankar, Ramkumar ^a   Joshi, Madhura ^a   Li, Tao ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK SUITES; BRANCH DIVERGENCES; CLUSTERING ANALYSIS; DESIGN SPACES; DIMENSIONALITY REDUCTION; EVALUATION METRICS; FUNCTIONAL BLOCK; GPU ARCHITECTURES; HIGH-PERFORMANCE COMPUTING; K-MEANS; LARGE ARRAYS; MICRO ARCHITECTURES; MICRO-ARCHITECTURE DESIGN; NEAREST NEIGHBORS; RODINIA; SIMILARITY SCORES; WORKLOAD CHARACTERISTICS;

DESIGN; FLOCCULATION;

PROGRAM PROCESSORS;

EID: 78751477137     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IISWC.2010.5649549     Document Type: Conference Paper

References (51)

1. G. Yuan, A. Bakhoda, and T. Aamodt, Complexity Effective Memory Access Scheduling for Many-Core Accelerator Architectures, MICRO, 2009.
2. W. Fung, I. Sham, G. Yuan, and T. Aamodt, Dynamic Warp Formation and Scheduling for Efficient GPU Control Flow, MICRO, 2007.
3. T. Halfhill, Looking Beyond Graphics, Microprocessor Report, 2009.
4. D. Tarjan, J. Meng, and K. Skadron, Increasing Memory Miss Tolerance for SIMD Cores, SC, 2009.
5. NVIDIA Compute PTX: Parallel Thread Execution ISA, Version 1.4, 2009.
6. A. Bakhoda, G. Yuan, W. Fung, H. Wong, and T. Aamodt, Analyzing CUDA Workloads using a Detailed GPU Simulator, ISPASS, 2009.
7. G. Dunteman, Principal Components Analysis, SAGE Publications, 1989.
8. C. Romesburg, Cluster Analysis for Researchers, Lifetime Learning Publications, 1984.
9. A. Joshi, A. Phansalkar, L. Eeckhout, and L. John, Measuring Benchmark Similarity using Inherent Program Characteristics, IEEE Transactions on Computers, Vol.55, No.6, 2006.
10. C. Hughe, J. Poe, A. Qouneh, and T. Li, On the (Dis)similarity of Transactional Memory Workloads, IISWC, 2009.
11. L. Eeckhout, H. Vandierendonck, and K. De Bosschere, Designing Computer Architecture Research Workloads, Computer, Vol. 36, No. 2, 2003.
12. H. Vandierendonck and K. Bosschere, Many Benchmarks Stress the Same Bottlenecks, Workshop on Computer Architecture Evaluation Using Commercial Workloads, 2004.
13. J. Henning, SPEC CPU2000: Measuring CPU Performance in the New Millennium, IEEE Computer, pp. 28-35, July 2000.
14. C. Lee, M. Potkonjak, and W. Smith, MediaBench: A Tool for Evaluating and Synthesizing Multimedia and Communication Systems, MICRO, 1997.
15. M. Guthaus, J. Ringenberg, D. Ernst, T. Austin, T. Mudge, and R. Brown, MiBench: A Free, Commercially Representative Embedded Benchmark Suite, Workshop on Workload Characterization, 2001.
16. S. Woo, M. Ohara, E. Torrie, J. Singh, and A. Gupta, The SPLASH-2 Programs: Characterization and Methodological Considerations, ISCA, 1995.
17. C. Minh, J. Chung, C. Kozyrakis, and K. Olukotun, STAMP: Stanford Transactional Memory Applications for Multi-Processing, IISWC, 2008.
18. C. Bienia, S. Kumar, J. Singh, and K. Li, The PARSEC Benchmark Suite: Characterization and Architectural Implications, Princeton University Technical Report, 2008.
19. NVIDIA CUDA.™ Programming Guide Version 2.3.1, Nvidia Corporation, 2009.
20. J. Nickolls, I. Buck, M. Garland, and K. Skadron, Scalable Parallel Programming with CUDA, Queue 6, 2 (Mar. 2008), 40-53.
21. E. Lindholm, J. Nickolls, S. Oberman, and J. Montrym, NVIDIA Tesla: A Unified Graphics and Computing Architecture, Micro, vol.28, no.2, 2008.
22. The CUDA Compiler Driver NVCC, Nvidia Corporation, 2008.
23. Technical Overview, ATI Stream Computing, AMD Inc, 2009.
24. A. Kerr, G. Diamos, and S. Yalamanchilli, A Characterization and Analysis of PTX Kernels, IISWC 2009.
25. S. Che, M. Boyer, J. Meng, D. Tarjan, J. Sheaffer, S. Lee, and K. Skadron. Rodinia: A Benchmark Suite for Heterogeneous Computing, IISWC, 2009.
26. Parboil Benchmark suite. URL: http://impact.crhc.illinois.edu/parboil. php.
27. M. Hughes, Exploration and Play Re-visited: A Hierarchical Analysis, International Journal of Behavioral Development, 1979.
28. http://www.nvidia.com/object/cuda-sdks.html
29. P. Harish and P. J. Narayanan, Accelerating Large Graph Algorithms on the GPU Using CUDA, HiPC, 2007.
30. M. Giles, Jacobi Iteration for a Laplace Discretisation on a 3D Structured Grid, http://people.maths.ox.ac.uk/~gilesm/hpc/NVIDIA/laplace3d.pdf, April, 2008.
31. W. Huang, S. Ghosh, S. Velusamy, K. Sankaranarayanan, and K. Skadron, M. R. Stan, Hotspot: A Compact Thermal Modeling Methodology for Early-Stage VLSI Design, IEEE Transactions on VLSI Systems 14 (5) (2006).
32. M. Giles and S. Xiaoke, Notes on using the NVIDIA 8800 GTX graphics card. http://people.maths.ox.ac.uk/~gilesm/hpc/.
33. B. He, W. Fang, Q. Luo, N. Govindaraju, and T. Wang, Mars: a MapReduce Framework on Graphics Processors, PACT, 2008.
34. Billconan and Kavinguy, A Neural Network on GPU. http://www.codeproject. com/KB/graphics/GPUNN.aspx.
35. Pcchen. N-Queens Solver, http://forums.nvidia.com/index.php?showtopic= 76893, 2008.
36. Maxime. Ray tracing. http://www.nvidia.com/cuda.
37. S. Al-Kiswany, A. Gharaibeh, E. Santos-Neto, G. Yuan, and M. Ripeanu, StoreGPU: Exploiting Graphics Processing Units to accelerate Distributed Storage Systems, HPDC, 2008.
38. E.Sintorn and U. Assarsson, Fast Parallel GPU-sorting using a Hybrid Algorithm, Journal of Parallel and Distributed Computing, Volume 68, Issue 10, October 2008.
39. R. Narayanan, B. Ozisikyilmaz, J. Zambreno, J. Pisharath, G. Memik, and A. Choudhary, MineBench: A Benchmark Suite for Data Mining Workloads, IISWC, 2006.
40. M. Schatz, C. Trapnell, A. Delcher, and A. Varshney, High-throughput Sequence Alignment using Graphics Processing Units, BMC Bioinformatics, 8(1): 474, 2007.
41. T. Warburton, Mini Discontinuous Galerkin Solvers. http://www.caam.rice. edu/~timwar/RMMC/MIDG.html, 2008.
42. S. Manavski, CUDA compatible GPU as an Efficient Hardware Accelerator for AES Cryptography, ICSPC, 2007.
43. J. Michalakes and M. Vachharajani, GPU Acceleration of Numerical Weather Prediction, IPDPS, 2008.
44. S. Stone, J. Haldar, S. Tsao, W. Hwu, Z. Liang, and B. Sutton, Accelerating Advanced MRI Reconstructions on GPUs, Computing Frontiers, 2008.
45. StatSoft, Inc. STATISTICA, http://www.statsoft.com/.
46. K. Yeomans and P.Golder, The Guttman-Kaiser Criterion as a Predictor of the Number of Common Factors.", Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 31, No. 3, 1982.
47. R. H. Saavedra and A. J. Smith, Analysis of Benchmark Characteristics and Benchmark Performance Prediction, ACM Trans. Computer Systems, 1998.
48. K. Hoste and L. Eeckhout. Microarchitecture-independent Workload Characterization. IEEE Micro, 27(3):63.-72, 2007.
49. http://www.khronos.org/opencl/
50. L. Seiler, D. Carmean, E. Sprangle, T.Forsyth, M. Abrash, P. Dubey, S. Junkins, A. Lake, J. Sugerman, R. Cavin, R. Espasa, E. Grochowski, T. Juan, and P. Hanrahan, Larrabee: A Many-core X86 Architecture for Visual Computing, ACM Trans. Graph. 27-3, 2008.
51. A. Phansalkar, A. Joshi, and L. John, Analysis of Redundancy and Application Balance in the SPEC CPU2006 Benchmark Suite, ISCA, 2007.