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The need to integrate several versions of a program into a common one arises frequently, but it is a tedious and time consuming task to integrate programs by hand. To date, the only available tools for assisting with program integration are variants of text-based differential file comparators; these are of limited utility because one has no guarantees about how the program that is the product of an integration behaves compared to the programs that were integrated. This paper concerns the design of a semantics-based tool for automatically integrating program versions. The main contribution of the paper is an algorithm that takes as input three programs A, B, and Base, where A and 8 are two variants of Base. Whenever the changes made to Base to create A and B do not “interfere ” (in a sense defined in the paper), the algorithm produces a program M that integrates A and B. The algorithm is predicated on the assumption that differences in the behavior of the variant programs from that of Base, rather than differences in the text, are significant and must be preserved in M. Although it is undecidable whether a program modification actually leads to such a difference, it is possible to determine a safe approximation by comparing each of the variants with Base. To determine this information, the integration algorithm employs a program representation that is similar (although not identical) to the dependence graphs that have been used

INTRODUCTION By the late 1980s, memory system performance and CPU performance had already begun to diverge. This trend made effective use of the register file imperative for excellent performance. Although most compilers at that time allocated scalar variables to registers using graph coloring with marked success [12, 13, 14, 6], allocation of array values to registers only occurred in rare circumstances because standard data-flow analysis techniques could not uncover the available reuse of array memory locations. This deficiency was especially problematic for scientific codes since a majority of the computation involves array references. Our original paper addressed this problem by presenting an algorithm and experiment for a loop transformation, called scalar replacement, that exposed the reuse available in array references in an innermost loop. It also demonstrated experimentally how another loop transformation, called unroll-and-jam [2], could expose more opportunities for scalar…

Programs and systems of recurrence equations may be represented as sets of actions which are to be executed subject to precedence constraints. In many cases, actions may be labelled by integral vectors in some iteration domain, and precedence constraints may be described by affine relations. A schedule for such a program is a function which assigns an execution date to each action. Knowledge of such a schedule allows one to estimate the intrinsic degree of parallelism of the program and to compile a parallel version for multiprocessor architectures or systolic arrays. This paper deals with the problem of finding closed form schedules as affine or piecewise affine functions of the iteration vector. An efficient algorithm is presented which reduces the scheduling problem to a parametric linear program of small size, which can be readily solved by an efficient algorithm.

Given a program written in a simple imperative language (assignment statements, for loops, affine indices and loop limits), this paper presents an algorithm for analyzing the patterns along which values flow as the execution proceeds. For each array or scalar reference, the result is the name and iteration vector of the source statement as a function of the iteration vector of the referencing statement. The paper discusses several applications of the method: conversion of a program to a set of recurrence equations, array and scalar expansion, program verification and parallel program construction. Keywords dataflow analysis, semantics analysis, array expansion. 1 Introduction It is a well known fact that scientific programs spend most of their running time in executing loops operating on arrays. Hence if a restructuring or optimizing compiler is to do a good job, it must be able to do a thorough analysis of the addressing patterns in such loops. If taken in full generality, ...

The Multiflow compiler uses the trace scheduling algorithm to find and exploit instruction-level parallelism beyond basic blocks. The compiler generates code for VLIW computers that issue up to 28 operations each cycle and maintain more than 50 operations in flight. At Multiflow the compiler generated code for eight different target machine architectures and compiled over 50 million lines of FORTRAN and C applications and systems code. The requirement of finding large amounts of parallelism in ordinary programs, the trace scheduling algorithm, and the many unique features of the Multiflow hardware placed novel demands on the compiler. New techniques in instruction scheduling, register allocation, memory-bank management, and intermediate-code optimizations were developed, as were refinements to reduce the overhead of trace scheduling. This paper describes the Multiflow compiler and reports on the Multiflow practice and experience with compiling for instruction-level parallelism beyond basic blocks.

The centerpiece of this thesis is a new processing paradigm for exploiting instruction level parallelism. This paradigm, called the multiscalar paradigm, splits the program into many smaller tasks, and exploits fine-grain parallelism by executing multiple, possibly (control and/or data) depen-dent tasks in parallel using multiple processing elements. Splitting the instruction stream at statically determined boundaries allows the compiler to pass substantial information about the tasks to the hardware. The processing paradigm can be viewed as extensions of the superscalar and multiprocess-ing paradigms, and shares a number of properties of the sequential processing model and the dataflow processing model. The multiscalar paradigm is easily realizable, and we describe an implementation of the multis-calar paradigm, called the multiscalar processor. The central idea here is to connect multiple sequen-tial processors, in a decoupled and decentralized manner, to achieve overall multiple issue. The mul-tiscalar processor supports speculative execution, allows arbitrary dynamic code motion (facilitated by an efficient hardware memory disambiguation mechanism), exploits communication localities, and does all of these with hardware that is fairly straightforward to build. Other desirable aspects of the

This paper presents an overview of automatic program parallelization techniques. It covers dependence analysis techniques, followed by a discussion of program transformations, including straight-line code parallelization, do loop transformations, and parallelization of recursive routines. The last section of the paper surveys several experimental studies on the effectiveness of parallelizing compilers.

Previous research has used program transformation to introduce parallelism and to exploit data locality. Unfortunately, these two objectives have usually been considered independently. This work explores the tradeoffs between effectively utilizing parallelism and memory hierarchy on shared-memory multiprocessors. We present a simple, but surprisingly accurate, memory model to determine cache line reuse from both multiple accesses to the same memory location and from consecutive memory access. The model is used in memory optimizing and loop parallelization algorithms that effectively exploit data locality and parallelism in concert. We demonstrate the efficacy of this approach with very encouraging experimental results. 1 Introduction Transformations to exploit parallelism and to improve data locality are two of the most valuable compiler techniques in use today. Independently, each of these optimizations has been shown to result in dramatic improvements. This paper seeks to combine t...

A common problem in restructuring programs for vector or parallel execution is the suppression of false dependencies which originate in the reuse of the same memory cell for unrelated values. The method is simple and well understood in the case of scalars. This paper gives the general solution for the case of arrays. The expansion is done in two steps: first, modify all definitions of the offending array in order to obtain the single assignment property. Then, reconstruct the original data flow by adapting all uses of the array. This is done with the help of a new algorithm for solving parametric integer programs. The technique is quite general and may be used for other purposes, including program checking, collecting array predicates, etc... 1 Introduction 1.1 Motivation One of the most striking trends in today's computer architecture is the development of special purpose machines for numerical computations. The idea behind this effort is that by capitalizing on the pecul...