Compiler transformations for high-performance computing
ACM Computing Surveys (CSUR)
Flattening and parallelizing irregular, recurrent loop nests
PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
Compiler optimizations for eliminating barrier synchronization
PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
A compiler framework for extracting superword level parallelism
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
Fine-grained parallel traversals of irregular data structures
Proceedings of the 21st international conference on Parallel architectures and compilation techniques
Breaking SIMD shackles with an exposed flexible microarchitecture and the access execute PDG
PACT '13 Proceedings of the 22nd international conference on Parallel architectures and compilation techniques
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Many loop nests in scientific codes contain a parallelizable outer loop but have an inner loop for which the number of iterations varies between different iterations of the outer loop. When running this kind of loop nest on a SIMD machine, the SIMD-inherent restriction to single program counter common to all processors will cause a performance degradation relative to comparable MIMD implementations. This problem is not due to limited parallelism or bad load balance, it is merely a problem of control flow.This paper presents a loop transformation, which we call loop flattening, that overcomes this limitation by letting each processor advance to the next loop iteration containing useful computation, if there is such an iteration for the given processor. We study a concrete example derived from a molecular dynamics code and compare performance results for flattened and unflattened versions of this kernel on two SIMD machines, the CM-2 and the DECmpp 12000. We then evaluate loop flattening from the compiler's perspective in terms of applicability, cost, profitability, and safety. We conclude with arguing that loop flattening, whether performed by the programmer or by the compiler, introduces negligible overhead and can significantly improve the performance of scientific codes for solving irregular problems.