David L. Kuck

Bio: David L. Kuck is an academic researcher. The author has contributed to research in topics: Computation. The author has an hindex of 1, co-authored 1 publications receiving 485 citations.

The program dependence graph and its use in optimization

Abstract: In this paper we present an intermediate program representation, called the program dependence graph (PDG), that makes explicit both the data and control dependences for each operation in a program. Data dependences have been used to represent only the relevant data flow relationships of a program. Control dependences are introduced to analogously represent only the essential control flow relationships of a program. Control dependences are derived from the usual control flow graph. Many traditional optimizations operate more efficiently on the PDG. Since dependences in the PDG connect computationally related parts of the program, a single walk of these dependences is sufficient to perform many optimizations. The PDG allows transformations such as vectorization, that previously required special treatment of control dependence, to be performed in a manner that is uniform for both control and data dependences. Program transformations that require interaction of the two dependence types can also be easily handled with our representation. As an example, an incremental approach to modifying data dependences resulting from branch deletion or loop unrolling is introduced. The PDG supports incremental optimization, permitting transformations to be triggered by one another and applied only to affected dependences.

Efficiently computing static single assignment form and the control dependence graph

Abstract: In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

A Regular Layout for Parallel Adders

Abstract: With VLSI architecture, the chip area and design regularity represent a better measure of cost than the conventional gate count. We show that addition of n-bit binary numbers can be performed on a chip with a regular layout in time proportional to log n and with area proportional to n.

Compiler transformations for high-performance computing

Abstract: In the last three decades a large number of compiler transformations for optimizing programs have been implemented. Most optimizations for uniprocessors reduce the number of instructions executed by the program using transformations based on the analysis of scalar quantities and data-flow techniques. In contrast, optimizations for high-performance superscalar, vector, and parallel processors maximize parallelism and memory locality with transformations that rely on tracking the properties of arrays using loop dependence analysis.This survey is a comprehensive overview of the important high-level program restructuring techniques for imperative languages, such as C and Fortran. Transformations for both sequential and various types of parallel architectures are covered in depth. We describe the purpose of each transformation, explain how to determine if it is legal, and give an example of its application.Programmers wishing to enhance the performance of their code can use this survey to improve their understanding of the optimizations that compilers can perform, or as a reference for techniques to be applied manually. Students can obtain an overview of optimizing compiler technology. Compiler writers can use this survey as a reference for most of the important optimizations developed to date, and as bibliographic reference for the details of each optimization. Readers are expected to be familiar with modern computer architecture and basic program compilation techniques.

Automatic translation of FORTRAN programs to vector form

Abstract: The recent success of vector computers such as the Cray-1 and array processors such as those manufactured by Floating Point Systems has increased interest in making vector operations available to the FORTRAN programmer The FORTRAN standards committee is currently considering a successor to FORTRAN 77, usually called FORTRAN 8x, that will permit the programmer to explicitly specify vector and array operationsAlthough FORTRAN 8x will make it convenient to specify explicit vector operations in new programs, it does little for existing code In order to benefit from the power of vector hardware, existing programs will need to be rewritten in some language (presumably FORTRAN 8x) that permits the explicit specification of vector operations One way to avoid a massive manual recoding effort is to provide a translator that discovers the parallelism implicit in a FORTRAN program and automatically rewrites that program in FORTRAN 8xSuch a translation from FORTRAN to FORTRAN 8x is not straightforward because FORTRAN DO loops are not always semantically equivalent to the corresponding FORTRAN 8x parallel operation The semantic difference between these two constructs is precisely captured by the concept of dependence A translation from FORTRAN to FORTRAN 8x preserves the semantics of the original program if it preserves the dependences in that programThe theoretical background is developed here for employing data dependence to convert FORTRAN programs to parallel form Dependence is defined and characterized in terms of the conditions that give rise to it; accurate tests to determine dependence are presented; and transformations that use dependence to uncover additional parallelism are discussed