Speculative computation, parallelism, and functional programming

Burton, F. Warren

doi:10.1109/tc.1985.6312218

Cited by 64 publications

(22 citation statements)

References 5 publications

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“…Task-level speculative computation has been long proposed as a means for extracting higher levels of parallelism [3]. With the emergence of multithreaded processors [23], many researchers have proposed the use of threads for exploiting speculative parallelism in both hardware and software [11,25,2].…”

Section: Previous Workmentioning

confidence: 99%

Compiler-Driven Dependence Profiling to Guide Program Parallelization

Wu¹,

Kejariwal

Caşcaval³

2008

Languages and Compilers for Parallel Computing

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Abstract. As hardware systems move toward multicore and multithreaded architectures, programmers increasingly rely on automated tools to help with both the parallelization of legacy codes and effective exploitation of all available hardware resources. Thread-level speculation (TLS) has been proposed as a technique to parallelize the execution of serial codes or serial sections of parallel codes. One of the key aspects of TLS is task selection for speculative execution.In this paper we propose a cost model for compiler-driven task selection for TLS. The model employs profile-based analysis of may-dependences to estimate the probability of successful speculation. We discuss two techniques to eliminate potential inter-task dependences, thereby improving the rate of successful speculation. We also present a profiling tool, DProf, that is used to provide run-time information about maydependences to the compiler and map dynamic dependences to the source code. This information is also made available to the programmer to assist in code rewriting and/or algorithm redesign.We used DProf to quantify the potential of this approach and we present results on selected applications from the SPEC CPU2006 and SEQUOIA benchmarks.

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Section: Previous Workmentioning

confidence: 99%

Compiler-Driven Dependence Profiling to Guide Program Parallelization

Wu¹,

Kejariwal

Caşcaval³

2008

Languages and Compilers for Parallel Computing

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“…This is known as "speculative computing", [10]. The Nimrod/O simulated annealing implementation can anticipate the step after next, adding tasks that may be needed then.…”

Section: Nimrod/o Algorithmsmentioning

confidence: 99%

Model Optimization and Parameter Estimation with Nimrod/O

Abramson

Peachey

Lewis

2006

Computational Science – ICCS 2006

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Abstract. Optimization problems where the evaluation step is computationally intensive are becoming increasingly common in both engineering design and model parameter estimation. We describe a tool, Nimrod/O, that expedites the solution of such problems by performing evaluations concurrently, utilizing a range of platforms from workstations to widely distributed parallel machines. Nimrod/O offers a range of optimization algorithms adapted to take advantage of parallel batches of evaluations. We describe a selection of case studies where Nimrod/O has been successfully applied, showing the parallelism achieved by this approach.

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“…Speculative parallelism seems to demand a mixture of future's eagerness and delay's lack of a guarantee that an expression will actually be evaluated, with the extra feature, present in neither future nor delay, that evaluation of an expression might begin and then be abandoned in midstream. Thus a promising avenue for supporting speculative computing might involve associating an "eagerness" parameter or priority with each task (36) and modifying the Multilisp scheduler to make its best efforts to always be executing the runnable tasks with the highest priorities. In such a scheme, mandatory tasks would be assigned the highest possible priority and speculative tasks would occupy a range of lower priorities.…”

Section: Speculative Computationsmentioning

confidence: 99%

An assessment of multilisp: Lessons from experience

Halstead

1986

Int J Parallel Prog

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Multilisp is a parallel programming language derived from the Scheme dialect of Lisp by addition of the future construct. It has been implemented on Concert, a 32-processor shared-memory multiprocessor. A statistics-gathering feature of Concert Multilisp produces parallelism profiles showing the number of processors busy with computing or overhead, as a function of time. Experience gained using parallelism profiles and other measurement tools on several application programs has revealed three basic ways in which future generates concurrency. These ways are illustrated on two example programs: the Lisp mapping function mapcar and the partitioning routine from Quicksort. Experience with Multilisp programming exposes issues relating to side effects, error and exception handling, low-level operations for explicit manipulation of futures and tasks, and speculative computing, which are also discussed. The basic outlines of Multilisp are now fairly clear and have stood the test of being used for several applications, but further language design work is especially needed in the areas of speculative computing and exception handling.

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Speculative computation, parallelism, and functional programming

Cited by 64 publications

References 5 publications

Compiler-Driven Dependence Profiling to Guide Program Parallelization

Compiler-Driven Dependence Profiling to Guide Program Parallelization

Model Optimization and Parameter Estimation with Nimrod/O

An assessment of multilisp: Lessons from experience

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