An empirical study of automatic restructuring of vision programs for simd machines

Abstract

The feasibility of Automatic restructuring low-level Image Processing applications for parallel processing is studied through experiments using an automatic restructurer developed by us. To measure performance we present parallel processing speedup results due to automatic restructuring for these applications. The effectiveness of scalar expansion and loop interchanging to increase the degree of vectorization is discussed. The applications are classified in terms of a small set of features. We analyze the applications in which automatic restructuring resulted in a “poor” speedup, identifying those application characteristics that have the greatest effect on it. The experiment suggests that automatic restructuring can be a useful tool for exploiting parallelism from the sequential form of low-level image processing applications.