A performance comparison between an APL interpreter and compiler

Wiedmann, Clark

doi:10.1145/800062.801219

Cited by 8 publications

(1 citation statement)

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“…A number of APL compiler implementation works [1,2,3,4,5,6,7] have been reported at this Conference over the years. The primary goal of APL compilation is to improve the execution efficiency of APL programs under interpretation.…”

Section: Introductionmentioning

confidence: 99%

On performance and space usage improvements for parallelized compiled APL code

Ching²,

1991

Proceedings of the International Conference on APL '91

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Loop combination has been a traditional optimization technique employed in APL compilers, but may introduce dependencies into the combined loop. We propose an analysis method by which the compiler can keep track of the change of the parallelism when combining high-level primitives.The analysis is necessary when the compiler needs to decide a trade-off between more parallelism and a further combination.We also show how the space usage, as well as the performance, improves by using system calls with the aid of garbage collection to implement a dynamic memory allocation. A modification of the memory management scheme can also increase available parallelism,Our experimental results indicate that the performance and the space usage improve appreciably with the above enhancements.On Performance and Space 234 APL 91

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Section: Introductionmentioning

confidence: 99%

On performance and space usage improvements for parallelized compiled APL code

Ching²,

1991

Proceedings of the International Conference on APL '91

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Speeding up Array Query Processing by Just-In-Time Compilation

Jucovschi

Baumann

Stancu-Mara

2008

2008 IEEE International Conference on Data Mining Workshops

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On performance and space usage improvements for parallelized compiled APL code

JuDz-ching

ChingWai-Mee²,

WuChuan-lin

1991

SIGAPL APL Quote Quad

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Loop combination has been a traditional optimization technique employed in APL compilers, but may introduce dependencies into the combined loop. We propose an analysis method by which the compiler can keep track of the change of the parallelism when combining high-level primitives. The analysis is necessary when the compiler needs to decide a trade-off between more parallelism and a further combination. We also show how the space usage, as well as the performance, improves by using system calls with the aid of garbage collection to implement a dynamic memory allocation. A modification of the memory management scheme can also increase available parallelism. Our experimental results indicate that the performance and the space usage improve appreciably with the above enhancements.

show abstract

A performance comparison between an APL interpreter and compiler

Cited by 8 publications

References 0 publications

On performance and space usage improvements for parallelized compiled APL code

On performance and space usage improvements for parallelized compiled APL code

Speeding up Array Query Processing by Just-In-Time Compilation

On performance and space usage improvements for parallelized compiled APL code

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