Program transformation

About: Program transformation is a research topic. Over the lifetime, 2468 publications have been published within this topic receiving 73415 citations.

Bidirectionalization transformation based on automatic derivation of view complement functions

Abstract: Bidirectional transformation is a pair of transformations: a view function and a backward transformation. A view function maps one data structure called source onto another called view. The corresponding backward transformation reflects changes in the view to the source. Its practically useful applications include replicated data synchronization, presentation-oriented editor development, tracing software development, and view updating in the database community. However, developing a bidirectional transformation is hard, because one has to give two mappings that satisfy the bidirectional properties for system consistency.In this paper, we propose a new framework for bidirectionalization that can automatically generate a useful backward transformation from a view function while guaranteeing that the two transformations satisfy the bidirectional properties. Our framework is based on two known approaches to bidirectionalization, namely the constant complement approach from the database community and the combinator approach from the programming language community, but it has three new features: (1) unlike the constant complement approach, it can deal with transformations between algebraic data structures rather than just tables; (2) unlike the combinator approach, in which primitive bidirectional transformations have to be explicitly given, it can derive them automatically; (3) it generates a view update checker to validate updates on views, which has not been well addressed so far. The new framework has been implemented and the experimental results show that our framework has promise.

Putting polyhedral loop transformations to work

Abstract: We seek to extend the scope and efficiency of iterative compilation techniques by searching not only for program transformation parameters but for the most appropriate transformations themselves. For that purpose, we need a generic way to express program transformations and compositions of transformations. In this article, we introduce a framework for the polyhedral representation of a wide range of transformations in a unified way. We also show that it is possible to generate efficient code after the application of polyhedral program transformations. Finally, we demonstrate an implementation of the polyhedral representation and code generation techniques in the Open64/ORC compiler.

A compiler approach to fast hardware design space exploration in FPGA-based systems

Abstract: The current practice of mapping computations to custom hardware implementations requires programmers to assume the role of hardware designers. In tuning the performance of their hardware implementation, designers manually apply loop transformations such as loop unrolling. designers manually apply loop transformations. For example, loop unrolling is used to expose instruction-level parallelism at the expense of more hardware resources for concurrent operator evaluation. Because unrolling also increases the amount of data a computation requires, too much unrolling can lead to a memory bound implementation where resources are idle. To negotiate inherent hardware space-time trade-offs, designers must engage in an iterative refinement cycle, at each step manually applying transformations and evaluating their impact. This process is not only error-prone and tedious but also prohibitively expensive given the large search spaces and with long synthesis times. This paper describes an automated approach to hardware design space exploration, through a collaboration between parallelizing compiler technology and high-level synthesis tools. We present a compiler algorithm that automatically explores the large design spaces resulting from the application of several program transformations commonly used in application-specific hardware designs. Our approach uses synthesis estimation techniques to quantitatively evaluate alternate designs for a loop nest computation. We have implemented this design space exploration algorithm in the context of a compilation and synthesis system called DEFACTO, and present results of this implementation on five multimedia kernels. Our algorithm derives an implementation that closely matches the performance of the fastest design in the design space, and among implementations with comparable performance, selects the smallest design. We search on average only 0.3% of the design space. This technology thus significantly raises the level of abstraction for hardware design and explores a design space much larger than is feasible for a human designer.

Combining source transformation and operator overloading techniques to compute derivatives for MATLAB programs

Abstract: Derivatives of mathematical functions play a key role in various areas of numerical and technical computing. Many of these computations are done in MATLAB, a popular environment for technical computing providing engineers and scientists with capabilities for mathematical computing, analysis, visualization, and algorithmic development. For functions written in the MATLAB language, a novel software tool is proposed to automatically transform a given MATLAB program into another MATLAB program capable of computing not only the original function but also user-specified derivatives of that function. That is, a program transformation known as automatic differentiation is performed to change the semantics of the program in a fashion based on the chain rule of differential calculus. The crucial ingredient of the tool is a combination of source-to-source transformation and operator overloading. The overall design of the tool is described and numerical experiments are reported demonstrating the efficiency of the resulting code for a sample problem.