This article explores the use of compiler techniques to accomplish code compaction to yield smaller executables. The main contribution of this article is to show that careful, aggressive, interprocedural optimization, together with procedural abstraction of repeated code fragments, can yield

Over the past two decades tremendous progress has been made in both the design of parallel architectures and the compilers needed for exploiting parallelism on such architectures. In this paper we summarize the advances in compilation techniques for uncovering and effectively exploiting

In recent years there has been an increasing trend towards the incorporation of computers into a variety of devices where the amount of available memory is limited. This makes it desirable to try and reduce the size of applications where possible. This paper explores the use of compiler techniques

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. This paper presents an overview of techniques used in our work and in other recent research in this area. Using the results of a recent work [9] as a basis, several possible techniques for energy reduction through code compilation are presented. Examples with energy reduction of up to 40% on an Intel 486DX2

Hardware compilation techniques which use highlevel programming languages to describe and synthesize hardware are gaining popularity. They are especially useful for recon gurable computing systems since they provide a fast, easy to use, "software-like" programming environment for users

-level language. Therefore, these instructions are not used by most applications. We propose two code generation techniques to produce native code using these multimedia extensions for programs written in a high level language: classical vectorization and vectorization by unrolling. Vectorization by unrolling

Just a few years ago, parallel computers were tightly-coupled SIMD, VLIW, orMIMD machines. Now, they are clusters of workstations connected by communication networks yielding ever-higher band-width (e.g., Ethernet, FDDI, HiPPI, ATM). For these clusters, compiler research is centered on tech-niques

as the corresponding hand-written Fortran 90 programs. When compared with the performance of C-MEX files generated by the MathWorks MATLAB compiler, we observed that, for our tests, the Fortran 90 programs ran faster than the corresponding C-MEX programs on an SGI Power Challenge and on a Sun

. Several useful compiler and program transformation techniques for the superthreaded architectures [8] are presented in this paper. The superthreaded architecture adopts a thread pipelining execution model to facilitate runtime data dependence checking between threads, and to maximize thread