While automatic parallelization of loops usually relies on compile-time analysis of data dependences, for some loops the data dependences cannot be determined at compile time. An example is loops accessing arrays with subscripted subscripts. To parallelize these loops, it is necessary to perform run-time analysis. In this paper, we present a new algorithm to parallelize these loops at run time. Our scheme handles any type of data dependence in the loop without requiring any special architectural support in the multiprocessor. Furthermore, compared to an older scheme with the same generality, our scheme significantly reduces the amount of processor communication required and increases the overlap among dependent iterations. We evaluate our algorithm with parameterized loops running on the 32-processor Cedar shared-memory multiprocessor. The results show speedups over the serial code up to 14 with the full overhead of run-time analysis and of up to 27 if part of the analysis is reused across loop invocations. Moreover, the algorithm outperforms the older scheme in nearly all cases, reaching speedups of up to times when the loop has many dependences.
|Original language||English (US)|
|Number of pages||10|
|Journal||Proceedings of the ACM/IEEE Supercomputing Conference|
|State||Published - 1994|
|Event||Proceedings of the 1994 Supercomputing Conference - Washington, DC, USA|
Duration: Nov 14 1994 → Nov 18 1994