Putting Polyhedral Loop Transformations to Work

We seek to extend the scope and efficiency of iterative compilation techniques by searching not only for program transformation parameters but for the most appropriate transformations themselves. For that purpose, we need a generic way to express program transformations and compositions of transformations. In this article, we introduce a framework for the polyhedral representation of a wide range of transformations in a unified way. We also show that it is possible to generate efficient code after the application of polyhedral program transformations. Finally, we demonstrate an implementation of the polyhedral representation and code generation techniques in the Open64/ORC compiler.

[1]  Keshav Pingali,et al.  A Singular Loop Transformation Framework Based on Non-Singular Matrices , 1992, LCPC.

[2]  Martin Griebl,et al.  Code generation in the polytope model , 1998, Proceedings. 1998 International Conference on Parallel Architectures and Compilation Techniques (Cat. No.98EX192).

[3]  Pierre Jouvelot,et al.  Semantical interprocedural parallelization: an overview of the PIPS project , 1991 .

[4]  Paul Feautrier,et al.  Improving Data Locality by Chunking , 2003, CC.

[5]  Michael F. P. O'Boyle,et al.  Feedback Assisted Iterative Compilation , 2000 .

[6]  T. Kisuki,et al.  Iterative Compilation in Program Optimization , 2000 .

[7]  Ken Kennedy,et al.  Parascope:a Parallel Programming Environment , 1988 .

[8]  David A. Padua,et al.  On the Automatic Parallelization of the Perfect Benchmarks , 1998, IEEE Trans. Parallel Distributed Syst..

[9]  FeautrierLaboratoire Masi.  Some Eecient Solutions to the Aane Scheduling Problem Part Ii Multidimensional Time , 1992 .

[10]  Michael F. P. O'Boyle,et al.  MARS: A Distributed Memory Approach to Shared Memory Compilation , 1998, LCR.

[11]  Sanjay V. Rajopadhye,et al.  Generation of Efficient Nested Loops from Polyhedra , 2000, International Journal of Parallel Programming.

[12]  Ken Kennedy,et al.  The ParaScope parallel programming environment , 1993, Proc. IEEE.

[13]  L. Rauchwerger,et al.  The LRPD Test: Speculative Run-Time Parallelization of Loops with Privatization and Reduction Parallelization , 1999, IEEE Trans. Parallel Distributed Syst..

[14]  Jingling Xue.  Automating Non-Unimodular Loop Transformations for Massive Parallelism , 1994, Parallel Comput..

[15]  Corinne Ancourt,et al.  Scanning polyhedra with DO loops , 1991, PPOPP '91.

[16]  Monica S. Lam,et al.  Maximizing Multiprocessor Performance with the SUIF Compiler , 1996, Digit. Tech. J..

[17]  Scott A. Mahlke,et al.  High-level synthesis of nonprogrammable hardware accelerators , 2000, Proceedings IEEE International Conference on Application-Specific Systems, Architectures, and Processors.

[18]  Yunheung Paek,et al.  Parallel Programming with Polaris , 1996, Computer.

[19]  P. Feautrier.  Some Eecient Solutions to the Aane Scheduling Problem Part Ii Multidimensional Time , 1992 .

[20]  William Pugh,et al.  Nonlinear array dependence analysis , 1994 .

[21]  William Pugh,et al.  Optimization within a unified transformation framework , 1996 .

[22]  Pierre Boulet,et al.  Loop Parallelization Algorithms: From Parallelism Extraction to Code Generation , 1998, Parallel Comput..

[23]  Paul Feautrier,et al.  Fuzzy array dataflow analysis , 1995, PPOPP '95.

[24]  Gang Ren,et al.  A comparison of empirical and model-driven optimization , 2003, PLDI '03.

[25]  W. Kelly,et al.  Code generation for multiple mappings , 1995, Proceedings Frontiers '95. The Fifth Symposium on the Frontiers of Massively Parallel Computation.