Microarchitectural performance characterization of irregular GPU kernels

IISWC 2014 - IEEE International Symposium on Workload Characterization

Volumn , Issue , 2014, Pages 130-139

  O'Neil, Molly A ^a   Burtscher, Martin ^a  

^a Texas State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCESS CONTROL; BANDWIDTH; CODES (SYMBOLS); DYNAMIC RANDOM ACCESS STORAGE; GRAPHICS PROCESSING UNIT; PROGRAM PROCESSORS;

ARCHITECTURE DESIGNS; CODE OPTIMIZATION; DATA DEPENDENT; EXECUTION CYCLES; MEMORY ACCESS PATTERNS; PERFORMANCE BOTTLENECKS; PERFORMANCE CHARACTERIZATION; PERFORMANCE DEGRADATION;

MEMORY ARCHITECTURE;

EID: 84946053358     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IISWC.2014.6983052     Document Type: Conference Paper

References (35)

1. Alfred Aho, Ravi Sethi, and Jeffrey D. Ulman. Compilers: principles, techniques, and tools. Addison Wesley, 1986.
2. Ali Bakhoda, George L. Yuan, Wilson W. L. Fung, Henry Wong, and Tor M. Aamodt. Analyzing CUDA Workloads Using a Detailed GPU Simulator. Proc. of the 2009 IEEE International Symposium on Performance Analysis of Systems and Software, pp. 163-174, 2009.
3. Josh Barnes and Piet Hut. A hierarchical O(N log N) force-calculation algorithm. Nature, 324(4):446-449, 1986.
4. Emily Blem, Matthew Sinclair, Karthikeyan Sankaralingam. Challenge Benchmarks That Must be Conquered to Sustain the GPU Revolution. Proc. of the 4th Annual Workshop on Emerging Applications and Many-Core Architecture, 2011.
5. Braunstein, M. Mezard, and R. Zecchina. Survey Propagation: An Algorithm for Satisfiability. Random Structures and Algorithms, 27:201-226, 2005.
6. Martin Burtscher and Keshav Pingali. An efficient CUDA implementation of the tree-based Barnes Hut n-body algorithm. GPU Computing Gems Emerald Edition, pp. 75-92. Morgan Kaufmann, 2011.
7. Martin Burtscher, Rupesh Nasre, and Keshav Pingali. A Quantitative Study of Irregular Programs on GPUs. Proc. of the 2012 IEEE International Symposium on Workload Characterization, pp. 141-151, 2012.
8. Shuai Che, Bradford M. Beckmann, Steven K. Reinhardt, and Kevin Skadron. Pannotia: Understanding Irregular GPGPU Graph Applications. Proc. of the 2013 IEEE International Symposium on Workload Characterization, 2013.
9. Shuai Che, Michael Boyer, Jiayuan Meng, David Tarjan, Jeremy W. Sheaffer, and Kevin Skadron. A performance study of general-purpose applications on graphics processors using CUDA. Journal of Parallel and Distributing Computing, 68:1370-1380, 2008.
10. Shuai Che, Jeremy W. Sheaffer, Michael Boyer, Lukasz G. Szafaryn, Liang Wang, Kevin Skadron. A Characterization of the Rodinia Benchmark Suite with Comparison to Contemporary CMP Workloads. Proc. of the 2010 IEEE International Symposium on Workload Characterization, pp. 1-11, 2010.
11. L. Paul Chew. Guaranteed-quality mesh generation for curved surfaces. Proc. of the Symposium on Computational Geometry, 1993.
12. Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest. Introduction to Algorithms, McGraw Hill, 2001.
13. CUB, http://nvlabs.github.io/cub/, 2014.
14. Fermi. http://www.nvidia.com/content/PDF/fermi white papers/NVIDIA Fermi Compute Architecture Whitepaper.pdf, 2010.
15. Nilanjan Goswami, Ramkumar Shankar, Madhura Joshi, and Tao Li. Exploring GPGPU Workloads: Characterization Methodology, Analysis and Microarchitecture Evaluation Implications. Proc. of the 2010 IEEE International Symposium on Workload Characterization, pp. 1-10, 2010.
16. GPGPU-Sim 3.x Manual. http://gpgpusim. org/manual/index.php5/GPGPU-Sim-3.x-Manual, 2012.
17. Kirsten Hildrum and Philip S. Yu. Focused Community Discovery. Proc. of the International Conference on Data Mining, 2005.
18. Song Huang, Shucai Xiao, and Wu chun Feng. On the energy efficiency of graphics processing units for scientific computing. Proc. of the 23rd IEEE International Symposium on Parallel and Distributed Processing, pp. 1-8, 2009.
19. Andrew Kerr, Gregory Diamos, and Sudhakar Yalamanchili. A Characterization and Analysis of PTX Kernels. Proc. of the 2009 IEEE International Symposium on Workload Characterization, pp. 3-12, 2009.
20. Milind Kulkarni, Martin Burtscher, Rajasekhar Inkulu, and Keshav Pingali. How Much Parallelism is There in Irregular Applications? Proc. of the 14th ACM SIGPLAN Symposium on Principles and Practices of Parallel Programming, pp. 3-14, 2009.
21. Milind Kulkarni, Keshav Pingali, Bruce Walter, Ganesh Ramanarayanan, Kavita Bala and L. Paul Chew. Optimistic Parallelism Requires Abstractions. Proc. of the ACM Conference on Programming Languages Design and Implementation, pp. 211-222, 2007.
22. Shin-Ying Lee and Carole-Jean Wu. Characterizing the Latency Hiding Ability of GPUs. Proc. of the 2014 IEEE International Symposium on Performance Analysis of Systems and Software, pp. 145-146, 2014.
23. LonestarGPU, http://iss.ices.utexas.edu/?p=projects/galois/lonestargpu
24. Ryan Meltzer, Chi Zeng, and Cris Cecka. Micro-benchmarking the C2070 (poster). GPU Technology Conference, 2013.
25. Mario Mendez-Lojo, Martin Burtscher, and Keshav Pingali. A GPU implementation of inclusion-based points-to analysis. Proc. of the 17th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 107-116, 2012.
26. Jiayuan Meng, David Tarjan, and Kevin Skadron. Dynamic warp subdivision for integrated branch and memory divergence tolerance. Proc. of the 37th International Symposium on Computer Architecture, pp. 235-246, 2010.
27. Duane G. Merrill, Michael Garland, and Andrew S. Grimshaw. Scalable GPU Graph Traversal. Proc. of the 17th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2012
28. Rupesh Nasre, Martin Burtscher, and Keshav Pingali. Morph Algorithms on GPUs. Proc. of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2013.
29. Rupesh Nasre, Martin Burtscher, and Keshav Pingali Atomic-free Irregular Computations on GPUs. 6th Workshop on General Purpose Processing on Graphics Processing Units, 2013.
30. Molly A. O'Neil and Martin Burtscher. Floating-point data compression at 75 Gb/s on a GPU. Proc. of the 4th Workshop on General Purpose Processing on Graphics Processing Units, pp. 7:1-7, 2011.
31. Molly A. O'Neil, Dan Tamir, and Martin Burtscher. A Parallel GPU Version of the Traveling Salesman Problem. Proc. of the 2011 International Conference on Parallel and Distributed Processing Techniques and Applications, pp. 348-353, 2011.
32. John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krger, Aaron Lefohn, and Timothy J. Purcell. A survey of general purpose computation on graphics hardware. Computer Graphics Forum, 26(1):80-113, 2007.
33. Victor Podlozhnyuk and Mark Harris. Monte Carlo Option Pricing. http://docs.nvidia.com/cuda/samples/4-Finance/MonteCarloMultiGPU/doc/MonteCarlo.pdf. 2012.
34. Henry Wong, Misel-Myrto Papadopoulou, Maryam Sadooghi-Alvandi, and Andreas Moshovos. Demystifying GPU Microarchitecture through Microbenchmarking. Proc. of the 2010 IEEE International Symposium on Performance Analysis of Systems and Software, pp. 235-246, 2010.
35. Haicheng Wu, Gregory Diamos, Si Li, and Sudhakar Yalamanchili. Characterization and Transformation of Unstructured Control Flow in GPU Applications. Proc. of the 1st International Workshop on Characterizing Applications for Heterogeneous Exascale Systems, 2011.