计算机科学 ›› 2014, Vol. 41 ›› Issue (5): 27-32.doi: 10.11896/j.issn.1002-137X.2014.05.006
侯永生,赵荣彩,黄磊,韩林
HOU Yong-sheng,ZHAO Rong-cai,HUANG Lei and HAN Lin
摘要： 高性能微处理器中普遍采用SIMD向量扩展作为计算加速部件。在深入研究SIMD扩展部件数据依赖关系约束条件的基础上,提出一种基于依赖关系逆向图的Tarjan扩展算法,提高了SIMD并行性识别率,并结合传统向量化方法,实现了面向SIMD扩展部件的循环优化技术,消除了不可向量化语句对可向量化语句在数据重组中不必要的开销。实际程序测试结果显示,其在基于依赖关系的SIMD并行性判定方面优于ICC编译器,经过循环优化后,最终生成的SIMD代码其执行效率平均提高了12%。
| [1] Larsen S,Amarasinghe S．Exploiting superword level parallelism with multimedia instruction sets[C]∥Conference on Programming Language Design and Implementation．Vancouver,BCCanada,June 2000:145-156 [2] Intel．IA-32Intel Architecture Optimization Reference Manual．http://www.intel.com/products/processor/manuals/index.htm,2005 [3] Franchetti F,Kral S,Lorenz J,et al.Efficientutilization of SIMD extensions[J].Proc．IEEE,2005,3(2):409-425 [4] Al-Shayka O,Chen S,Mattavelli M．Introduction to the special issue on multimedia implementation[J]．IEEE Transactions on Circuits and System for Video Technology,2002,2(8):629-632 [5] Wu Peng,Eichenberger A E,Wang A．Efficient SIMD CodeGeneration for Runtime Alignment and Length Conversion[C]∥Proceedings of the International Symposium on Code Generation and Optimization．March 2005:153-164 [6] Wu Peng,Eichenberger A E,Wang A,et al.An integrated simdization framework using virtual vectors[C]∥Proceedings of the 19th Annual International Conference on Supercomputing．June 2005:169-178 [7] Kennedy K,Allen J R．Optimizing compilers for modern archi-tectures:a dependence-based approach[M].San Francisco CA,USA:Morgan Kaufmann Publishers Inc,2002 [8] Bacon D F,Graham S L,Sharp O J．Compiler Transformations for High-Performance Computing[J].ACM Computing Surveys,1994,26(4) [9] Tarjan R E.Depth-first search and linear graph algorithms[J]．SIAM Journal on Computing,1972,1(2):146-160 [10] Fournier J J A,Moore S W．A vector approach to cryptography implementation[M]∥Digital Rights Management.Technologyies,issues,challenges and systems.Springer Berlin Heidlberg,2005:277-297 [11] Franchetti F．A portable short vector version of FFTW [C]∥Proc．Fourth IMACS Symposium on Mathematical Modeling (MATHMOD 2003)．2003,2:1539-1548 [12] Slingerland N T,Smith A J．Measuring instruction sets for ge-neral purpose microprocessors:A survey [R]．CSD-00-1122．University of California at Berkeley Technical Report,2000 [13] Shin J,Hall M,Chame J．Superword-level parallelism in thepresence of control flow[C]∥International Symposium on Code Generation and Optimization.2005:165-175 [14] Feld D,Soddemann T,Jünger M,et al.Facilitate SIMD-Code-Generation in the Polyhedral Model by Hardware-aware Automatic Code-Transformation[C]∥Proceeding of the 3rd International Workshop on Polyhedral Compilation Techniques.2013:45-54 [15] Eichenberger A,Wu P,O’Brien K．Vectorization for simd architectures with alignment constraints[C]∥PLDI．2004 [16] Nuzman D,Rosen I,Zaks A．Auto-vectorization of interleaveddata for simd[C]∥PLDI．2006 [17] Kong M,Veras R,Stock K,et al.Polyhedral TransformationsMeet SIMD Code Generation[C]∥PLID．I2013 [18] Nuzman D,Zaks A．Outer-loop vectorization:revisited for short simd architectures[C]∥PACT．2008 [19] Chen C,Chame J,Hall M．Chill:A framework for composinghigh-level loop transformations[R]．Technical Report 08-897．USC Computer Science Technical Report．2008 [20] Trifunovic K,Nuzman D,Cohen A,et al.Polyhedral-modelguided loop-nest auto-vectorization[C]∥PACT．Sept.2009 [21] Vasilache N,Meister B,Baskaran M,et al．Joint scheduling and layout optimization to enable multi-level vectorization[C]∥Proc.of IMPACT’12．Jan．2012 |
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