A primitive-based strategy for producing efficient code for very high level programs

Ching, Wai-Mee; Carini, Paul; Ju, Dz-Ching

doi:10.1016/0096-0551(93)90038-3

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Ching and D. Ju have spent significant work on the ELI language and other APL-class language implementations, especially on parallelized code and optimization. [Ching 1990;Ching, et al 1993;Ching and Katz 1994;Hendriks and Ching 1990; J. D. Bunda and J. A. Gerth presented a method for doing table driven parsing of APL which suggested a parallel optimization for parsing, but did not elucidate the algorithm [1984].…”

Section: Related Workmentioning

confidence: 99%

Accelerating information experts through compiler design

Hsu

2015

Proceedings of the 2nd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming

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Dyalog APL is a tool of thought for information experts, enabling rapid development of domain-centric software without the costly software engineering feedback loop often required. The Dyalog APL interpreter introduces performance constraints that hinder the analysis of large data sets, especially on highly-parallel computing architectures. The Co-dfns compiler project aims to reduce the overheads involved in creating high-performance code in APL. It focuses on integrating with the APL environment and compiles a familiar subset of the language, delivering significant performance and platform independence to information experts without requiring code rewrites and conversion into other languages.The design of the Co-dfns compiler, itself an APL program, possesses a unique architecture that permits implementation without branching, recursion, or other complex forms of control flow. By integrating specific optimizations, the generated code competes with hand-written C code in the domain of financial simulations, exceeding it when integrated into the environment. Preliminary results demonstrate platform independent performance across CPUs and GPUs without modification of the source. Work continues to improve performance both of the architecture and the generated code. Eventually, the project hopes to convincingly demonstrate a wider range of techniques that extend the suitable domain for effective array programming.

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Section: Related Workmentioning

confidence: 99%

Accelerating information experts through compiler design

Hsu

2015

Proceedings of the 2nd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming

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“…3. The optimization strategy is primitive (function)-based [9]. The main function in the back end, TREELIST, walks through a parse tree from the lower right node up to the root in a semi-recursive fashion and for each internal node calls various PF-functions.…”

Section: The Base Compiler and The Parallelizing Back Endmentioning

confidence: 99%

Automatic Parallelization of Array-oriented Programs for a Multi-core Machine

Ching

Zheng

2012

Int J Parallel Prog

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We present the work on automatic parallelization of array-oriented programs for multi-core machines. Source programs written in standard APL are translated by a parallelizing APL-to-C compiler into parallelized C code, i.e. C mixed with OpenMP directives. We describe techniques such as virtual operations and datapartitioning used to effectively exploit parallelism structured around array-primitives. We present runtime performance data, showing the speedup of the resulting parallelized code, using different numbers of threads and different problem sizes, on a 4-core machine, for several examples.

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The key to a data parallel compiler

Hsu

2016

Proceedings of the 3rd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming

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We present a language-driven strategy for the construction of compilers that are inherently data-parallel in their design and implementation. Using an encoding of the inter-node relationships between nodes in an AST called a Node Coordinate Matrix, we demonstrate how an operator called the Key operator, that applies a function over groupings of array cells grouped and ordered by their keys, when used in conjunction with a Node Coordinate Matrix, permits arbitrary computation over sub-trees of an AST using purely dataparallel array programming techniques. We discuss the use of these techniques when applied to an experimental commercial compiler called Co-dfns, which is a fully data-parallel compiler developed using the techniques discussed here.

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A primitive-based strategy for producing efficient code for very high level programs

Cited by 3 publications

References 2 publications

Accelerating information experts through compiler design

Accelerating information experts through compiler design

Automatic Parallelization of Array-oriented Programs for a Multi-core Machine

The key to a data parallel compiler

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