[This article originally appeared in the ACM SIGAPL APL93 Conference Proceedings.]
Although multicomputers are becoming feasible for solving large problems, they are difficult to program: Extraction of parallelism from scalar languages is possible, but limited. Parallelism in algorithm design is difficult for those who think in von Neumann terms. Portability of programs and programming skills can only be achieved by hiding the underlying machine architecture from the user, yet this may impact performance on a specific host. APL, J, and other applicative array languages with adequately rich semantics can do much to solve these problems. The paper discusses the value of abstraction and semantic richness, performance issues, portability, potential degree of parallelism, data distribution, process creation, communication and synchronization, frequency of program faults, and clarity of expression. The BLAS are used as a basis for comparison with traditional supercomputing languages.
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