Vector models for data-parallel computing
Vector models for data-parallel computing
Automatic recognition of induction variables and recurrence relations by abstract interpretation
PLDI '90 Proceedings of the ACM SIGPLAN 1990 conference on Programming language design and implementation
Detecting coarse-grain parallelism using an interprocedural parallelizing compiler
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Symbolic analysis for parallelizing compilers
ACM Transactions on Programming Languages and Systems (TOPLAS)
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Symbolic Analysis: A Basis for Parallelization, Optimization, and Scheduling of Programs
Proceedings of the 6th International Workshop on Languages and Compilers for Parallel Computing
X10: an object-oriented approach to non-uniform cluster computing
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
A programming language
Data and Computation Transformations for Brook Streaming Applications on Multiprocessors
Proceedings of the International Symposium on Code Generation and Optimization
Global-view abstractions for user-defined reductions and scans
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
A translation system for enabling data mining applications on GPUs
Proceedings of the 23rd international conference on Supercomputing
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Multiprocessors are becoming prevalent in the PC world. Major CPU vendors such as Intel and Advanced Micro Devices have migrated to multicore processors. However, this also means that computers will run an application at full speed only if that application is parallelized. To take advantage of more than a fraction of compute resource on a die, we develop a compiler to parallelize a common and powerful programming paradigm, namely reduction. Our goal is to exploit the full potential of reductions for efficient execution of applications on multiprocessors, including multicores. Note that reduction operations are common in streaming applications, financial computing and HPC domain. In fact, 9% of all MPI invocations in the NAS Parallel Benchmarks are reduction library calls. Recognizing implicit reductions in Fortran and C is important for parallelization on multiprocessors. Recent languages such as Brook Streaming language and Chapel language allow users to specify reduction functions. Our compiler provides a unified framework for processing both implicit and user-defined reductions. Both types of reductions are propagated and analyzed interprocedurally. Our global algorithm can enhance the scope of user-defined reductions and parallelize coarser-grained reductions. Thanking to the powerful algorithm and representation, we obtain an average speedup of 3 on 4 processors. The speedup is only 1.7 if only intraprocedural scalar reductions are parallelized.