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Although code transformations are routinely applied to improve the performance of programs for both scalar and parallel machines, the properties of code-improving transformations are not well understood. In this article we present a framework that enables the exploration, both analytically and experimentally, of properties of code-improving transformations. The major component of the framework is a specification language, Gospel, for expressing the conditions needed to safely apply a transformation and the actions required to change the code to implement the transformation. The framework includes a technique that facilitates an analytical investigation of code-improving transformations using the Gospel specifications. It also contains a tool, Genesis, that automatically produces a transformer that implements the transformations specified in Gospel. We demonstrate the usefulness of the framework by exploring the enabling and disabling properties of transformations. We first present analytical results on the enabling and disabling properties of a set of code transformations, including both traditional and parallelizing transformations, and then describe experimental results showing the types of transformations and the enabling and disabling interactions actually found in a set of programs.

Abstract Although code optimizations are necessary to parallelize code, few guidelines exist for determining when and where to apply optimizations to produce the most efficient code. The order of applying optimizations can also have an impact on the efficiency of the final target code. However, determining the appropriate optimizations is difficult due to the complex interactions among the optimizations, scheduler and architecture. To aid in selecting appropriate optimizations, an optimizer generator (Genesis) is presented that produces an optimizer from specifications of optimizations. This paper describes the design and implementation of Genesis and demonstrates how such a generator could be used by optimizer designers. Some experiences with the generator are also described.

Creating highly optimized code for parallel machines is a difficult task, as the efficiency of the transformed the code depends of the types of tranformations applied.

article Free Access Share on Automatic generation of global optimizers Authors: Deborah Whitfield Department of Computer Science, University of Pittsburgh, Pittsburgh, PA Department of Computer Science, University of Pittsburgh, Pittsburgh, PAView Profile , Mary Lou Soffa Department of Computer Science, University of Pittsburgh, Pittsburgh, PA Department of Computer Science, University of Pittsburgh, Pittsburgh, PAView Profile Authors Info & Claims ACM SIGPLAN NoticesVolume 26Issue 6June 1991 pp 120–129https://doi.org/10.1145/113446.113456Published:01 May 1991Publication History 22citation315DownloadsMetricsTotal Citations22Total Downloads315Last 12 Months9Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

As an approach to deriving an application order of optimizing transformations, a framework is developed for examining the interactions of the transformations. The framework is based on an axiomatic specification technique and includes both pre-conditions and post conditions that must exist before and after applying optimizations. For a selected set of optimizations, the framework is used to determine those interactions among the optimizations that can create conditions and those that can destroy conditions for applying other optimizations. From these interactions, an application order is derived to obtain the potential benefits of the optimizations that can be applied to a program. In some cases, the ordering of a pair of optimizations is unambiguous in that one optimization can either create or destroy the conditions for the other. In the few cases where there is a cyclic interaction, the ordering is resolved based on the perceived importance of the two optimizations.