Affiliation:
1. Thomas J. Watson Research Center, P.O. Box 704, Yorkrown Heighls, NY
Abstract
APL-style programs use high level primitives on arrays instead of DO-loops whenever possible. For such programs, the average size of a basic blocks is much large than those in their FORTRAN counterparts. Hence, it is sufficiently profitable and relative easy to concentrate on basic blocks when parallelizing APL-style programs. But such an approach must depend on an APL compiler. The APL/370 compiler we have been developing aims at implementing automatic parallelization of APL programs at basic block level.
The compiler exploits functional parallelism on data independent sub-expressions and data parallelism of array primitives on array elements. The compiler front end does a local data dependency analysis and emits synchronization flags at function nodes. The back end does partitioning of (assembly code) array loop. A set of low-level synchronization primitives on MVS has also been developed. This will enable us to run compiled applications in parallel mode on IBM 3090 multi-processors to access the effectiveness of various scheduling methods on a shared memory model.
Publisher
Association for Computing Machinery (ACM)
Cited by
2 articles.
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1. The key to a data parallel compiler;Proceedings of the 3rd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming;2016-06-02
2. Structured APL;ACM SIGAPL APL Quote Quad;1993-09