Using the run-time sizes of data structures to guide parallel-thread creation

Huelsbergen, Lorenz; Larus, James R.; Aiken, Alexander

doi:10.1145/182409.182442

Cited by 39 publications

(15 citation statements)

References 25 publications

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“…We are aware of very little work that considers user-defined higher-order programs, though Le Métayer's work [28] can handle predefined higher-order functions with known costs, and Benzinger's work on worst-case complexity analysis for NuPrl [3] similarly supports higher-order functions if the complexity information is provided explicitly. Huelsbergen, Larus and Aiken [20] have defined an abstract interpretation of a higher-order, strict language for determining computation costs that dependent on the size of data structures. This static analysis is combined with run-time size information to deliver dynamic granularity estimates.…”

Section: Abstract Interpretationsmentioning

confidence: 99%

Static determination of quantitative resource usage for higher-order programs

et al. 2010

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We describe a new automatic static analysis for determining upper-bound functions on the use of quantitative resources for strict, higher-order, polymorphic, recursive programs dealing with possibly-aliased data. Our analysis is a variant of Tarjan's manual amortised cost analysis technique. We use a type-based approach, exploiting linearity to allow inference, and place a new emphasis on the number of references to a data object. The bounds we infer depend on the sizes of the various inputs to a program. They thus expose the impact of specific inputs on the overall cost behaviour.The key novel aspect of our work is that it deals directly with polymorphic higher-order functions without requiring source-level transformations that could alter resource usage. We thus obtain safe and accurate compile-time bounds. Our work is generic in that it deals with a variety of quantitative resources. We illustrate our approach with reference to dynamic memory allocations/deallocations, stack usage, and worst-case execution time, using metrics taken from a real implementation on a simple micro-controller platform that is used in safety-critical automotive applications.

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Section: Abstract Interpretationsmentioning

confidence: 99%

Static determination of quantitative resource usage for higher-order programs

et al. 2010

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“…The solution to these expressions then gives upper bounds on the execution time. Other dynamic runtime analyses are often used in conjunction with a static resource analysis, for example to approximate the sizes of key data structures . In this context, they form ancillary analyses to the main resource analysis.…”

Section: Constructing Resource Analysesmentioning

confidence: 99%

Resource analyses for parallel and distributed coordination

Trinder

Cole

Hammond

et al. 2011

Concurrency and Computation

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SUMMARYPredicting the resources that are consumed by a program component is crucial for many parallel or distributed systems. In this context, the main resources of interest are execution time, space and communication/synchronisation costs. There has recently been significant progress in resource analysis technology, notably in type-based analyses and abstract interpretation. At the same time, parallel and distributed computing are becoming increasingly important.This paper synthesises progress in both areas to survey the state-of-the-art in resource analysis for parallel and distributed computing. We articulate a general model of resource analysis and describe parallel/distributed resource analysis together with the relationship to sequential analysis. We use three parallel or distributed resource analyses as examples and provide a critical evaluation of the analyses. We investigate why the chosen analysis is effective for each application and identify general principles governing why the resource analysis is effective.

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“…This study is not the first to propose using cost prediction to determine when to cut off parallelism. One approach, developed in early work in functional programing, uses list size to determine cut offs [24]. Using list size alone is limited, because the technique assumes linear work complexity for every parallel operation.…”

Section: Related Workmentioning

confidence: 99%

Oracle scheduling

2011

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A classic problem in parallel computing is determining whether to execute a task in parallel or sequentially. If small tasks are executed in parallel, the task-creation overheads can be overwhelming. If large tasks are executed sequentially, processors may spin idle. This granularity problem, however well known, is not well understood: broadly applicable solutions remain elusive.We propose techniques for controlling granularity in implicitly parallel programming languages. Using a cost semantics for a general-purpose language in the style of the lambda calculus with support for parallelism, we show that task-creation overheads can indeed slow down parallel execution by a multiplicative factor. We then propose oracle scheduling, a technique for reducing these overheads, which bases granularity decisions on estimates of task-execution times. We prove that, for a class of computations, oracle scheduling can reduce task creation overheads to a small fraction of the work without adversely affecting available parallelism, thereby leading to efficient parallel executions.We realize oracle scheduling in practice by a combination of static and dynamic techniques. We require the programmer to provide the asymptotic complexity of every function and use run-time profiling to determine the implicit, architecture-specific constant factors. In our experiments, we were able to reduce overheads of parallelism down to between 3 and 13 percent, while achieving 6-to 10-fold speedups.

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Using the run-time sizes of data structures to guide parallel-thread creation

Abstract: Dynamic granularity estimation is a new technique for automatically identifying expressions in functional languages for parallel evaluation. Expressions with little computation relative to thread-creation costs should evaluate sequentially for maximum performance.

Cited by 39 publications

References 25 publications

Static determination of quantitative resource usage for higher-order programs

Static determination of quantitative resource usage for higher-order programs

Resource analyses for parallel and distributed coordination

Oracle scheduling

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