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Hardware-aware tiling optimization for multi-core systems

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Języki publikacji
EN
Abstrakty
EN
This paper presents a proposal for a new tool that improves tiling efficiency for a given hardware architecture. This article also describes the correlation between the changing hardware architecture and methods of software optimization. The first chapter includes a short description of the change in hardware architecture that has occurred over the past ten years. The second chapter provides an overview of the tools that will be used in further research. The subsequent sections contain a description of the proposed hardware-aware tool for optimal tiling.
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Strony
145--162
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
autor
  • Lodz University of Technology, Department of Microelectronics and Computer Science
  • Lodz University of Technology, Department of Microelectronics and Computer Science
Bibliografia
  • [1] Bastoul C.: Code Generation in the Polyhedral Model Is Easier Than You Think. In: Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques , PACT ’04, pp. 7–16, IEEE Computer Society, Washington, DC, USA, 2004. http://dx . doi . org/10 . 1109/PACT . 2004 . 11 .
  • [2] Carvalho C.: The Gap between Processor and Memory Speed. In: Proceedings of the Internal Conference on Computer Architecture , pp. 27–34, 2002.
  • [3] Deest G., Estibals N., Yuki T., Derrien S., Rajopadhye S.: Towards Scalable and Efficient FPGA Stencil Accelerators , article presented during 6th International Workshop on Polyhedral Compilation Techniques 2016. http://impact . gforge . inria . fr/impact2016/papers/impact2016-deest . pdf .
  • [4] Dongarra J., Jagode H., Mucci P., Vaccaro P., YarKhan A.: PAPI Library. Project description available on webpage http://icl . cs . utk . edu/papi/index . html .
  • [5] Frigo M., Leiserson C.E., Prokop H., Ramachandran S.: Cache-Oblivious Algorithms. In: Proceedings of the 40th Annual Symposium on Foundations of Computer Science , FOCS ’99, pp. 285–. IEEE Computer Society, Washington, DC, USA, 1999. http://dl . acm . org/citation . cfm?id=795665 . 796479 .
  • [6] Grosser T.: Speedup of Polly tiling optimization in comparison to gcc -O3. Figure available on webpage http://polly . llvm . org/performance . html .
  • [7] Grosser T., Cohen A., Holewinski J., Sadayappan P., Verdoolaege S.: Hybrid Hexagonal/Classical Tiling for GPUs. In: Proceedings of Annual IEEE/ACM International Symposium on Code Generation and Optimization , CGO ’14, pp. 66–75. ACM, New York, NY, USA, 2014. http://doi . acm . org/10 . 1145/ 2544137 . 2544160 .
  • [8] Grosser T., Gr ̈oßlinger A., Lengauer C.: Polly – Performing Polyhedral Optimizations on a Low-Level Intermediate Representation. In: Parallel Processing Letters , vol. 22(4), 2012. http://dx . doi . org/10 . 1142/S0129626412500107 .
  • [9] Grosser T., Zheng H., Aloor R., Simburger A., Großlinger A., Pouchet L.N.: Polly – Polyhedral optimization in LLVM. In: First International Workshop on Polyhedral Compilation Techniques (IMPACT’11) . Chamonix, France, 2011.
  • [10] Hennessy J.L., Patterson D.A.: Computer Architecture, Fifth Edition: A Quantative Approach , Morgan Kaufmann Publishers, San Francisco, CA, USA, 5th ed., 2011.
  • [11] Intel Xeon Phi TM Coprocessor System Software Developers Guide, document available on webpage http://www . intel . com/content/dam/www/public/us/ en/documents/product-briefs/xeon-phi-coprocessor-system-software- developers-guide . pdf .
  • [12] Kong M., Pop A., Pouchet L.N., Govindarajan R., Cohen A., Sadayappan P.: Compiler/Runtime Framework for Dynamic Dataflow Parallelization of Tiled Programs. In: ACM Trans. Archit. Code Optim. , vol. 11(4), pp. 1–30, 2015. http://doi . acm . org/10 . 1145/2687652 .
  • [13] Lattner C.: LLVM. Figure available on webpage http://www . aosabook . org/en/ llvm . html .
  • [14] Lattner C., Adve V.: LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation. In: Proceedings of the International Symposium on Code Generation and Optimization: Feedback-directed and Runtime Optimization , CGO ’04, pp. 75–86, IEEE Computer Society, Washington, DC, USA, 2004. http://dl . acm . org/citation . cfm?id=977395 . 977673 .
  • [15] Malik A.M.: Optimal Tile Size Selection Problem Using Machine Learning. In: Proceedings of the 2012 11th International Conference on Machine Learning and Applications – Volume 02 , ICMLA ’12, pp. 275–280, IEEE Computer Society, Washington, DC, USA, 2012. http://dx . doi . org/10 . 1109/ICMLA . 2012 . 214 .
  • [16] Puchet L.N.: PolyBench/C the Polyhedral Benchmark suite. Benchmark available on webpage http://web . cse . ohio-state . edu/ ~ pouchet/software/ polybench/ .
  • [17] Rahman M., Pouchet L.N., Sadayappan P.: Neural Network Assisted Tile Size Selection. In: International Workshop on Automatic Performance Tuning (IWAPT’2010) . Springer-Verlag, Berkeley, CA, 2010.
  • [18] Rupp K., Horovitz M., Labonte F., Shacham O., Olukotun K., Hammond L., Batten C.: 40 Years of Microprocessor Trend Data. Figure available on webpage http://www . karlrupp . net/wp-content/uploads/2015 . 06/40-years- processor-trend . png .
  • [19] Shirako J., Sharma K., Fauzia N., Pouchet L.N., Ramanujam J., Sadayappan P., Sarkar V.: Analytical Bounds for Optimal Tile Size Selection. In: Proceedings of the 21st International Conference on Compiler Construction , CC’12, pp. 101–121. Springer-Verlag, Berlin, Heidelberg, 2012. http://dx . doi . org/10 . 1007/978-3- 642-28652-0 6 .
  • [20] Verdoolaege S.: isl: An Integer Set Library for the Polyhedral Model. In: Fukuda K., Hoeven J., Joswig M., Takayama N. (eds.), ICMS’10 Proceedings of the Third International Congress on Mathematical Software , pp. 299–302, Springer- -Verlag, 2010.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-aa2bf0c5-d051-4995-8daa-eedb4099a962
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