We discuss and evaluate three optimizations for reducing memory management overhead and data copying costs in SISAL 1.2 programs that build arrays. The first, called framework preconstruction, eliminates superfluous allocate-deallocate sequences in cyclic computations. The second, called aggregate storage subsumption, reduces the management overhead for compound array components. The third, called predictive storage preallocation, eliminates superfluous data copying in filtered array constructions and simplifies their parallelization. We have added all three optimizations to the Optimizing SISAL Compiler with rewarding improvements in SISAL program performance on vector-parallel machines such as those built by Cray Computer Corporation, Convex, and Cray Research.>

tion of retiring FORTRAN. They addressed such questions as: Is FORTRAN the best tool for decomposing problems for parallel execution? Is FORTRAN the programming language we should carry into the 21st century? Are there any alternatives? While McGraw argued forcefully in favor of retiring FORTRAN, concerns about performance crippled his position. He could not rebut the claim that only Fo RTRAN could provide the performance required for scientific computing. In this report, we use the current performance of CRAY SISAL, a functional language for large-scale scientific computing, to counter that claim. If McGraw had had our data in 1984, he could have countered what some say is the only defense in favor of keeping FORTRAN.

Single-assignment languages like SISAL offer parallelism at all levels—among arbitrary operations, conditionals, loop iterations, and function calls. All control and data dependencies are local, and can be easily determined from the program. Various studies of SISAL programs have shown that they contain massive amounts of potential parallelism. There are two major challenges in converting this potential parallelism into real speedup on multiprocessor systems. First, it is important to carefully select the useful parallelism in a SISAL program, so as to obtain good speedup by trading off parallelism with overhead. Second, it is important to do sequential optimizations, so that the sequential components (tasks) of the SISAL program have comparable execution times with sequential languages such as Fortran, Pascal and C. The POSC compiler system described in this paper addresses both issues by integrating previous work on efficient sequential implementation of SISAL programs with previous work on selecting the useful parallelism in a SISAL program. The combined approach is validated by real speedup measurements on a Sequent Balance multiprocessor.

The authors compare the performance of SISAL, an application language for parallel numerical computations, and Fortran. The intent is to show that applicative programs, when compiled using a set of powerful yet simple optimization techniques, can achieve sequential execution speeds comparable to Fortran, and automatically utilize conventional shared memory multiprocessors.>