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Author Tseng, Chau-Wen ♦ Anderson, Jennifer M. ♦ Amarasinghe, Saman P. ♦ Lam, Monica S.
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Data Reorganization Transformation ♦ Unified Compilation Technique ♦ Automatic Data Decomposition ♦ Distributed Address Space Machine ♦ Hardware Support ♦ Simulation Study ♦ Access Non-local Data ♦ Parallel Overhead ♦ Memory Access ♦ Example Application ♦ Scalable Performance ♦ Address Space ♦ Communication Analysis ♦ Address Space Machine ♦ Parallel Machine ♦ Synchronization Overhead ♦ Compilation Technique ♦ Harmful Cache Effect
Description Parallel machines with shared address spaces are easy to program because they provide hardware support that allows each processor to transparently access non-local data. However, obtaining scalable performance can be difficult due to memory access and synchronization overhead. In this paper, we use profiling and simulation studies to identify the sources of parallel overhead. We demonstrate that compilation techniques for distributed address space machines can be very effective when used in compilers for shared address space machines. Automatic data decomposition can co-locate data and computation to improve locality. Data reorganization transformations can reduce harmful cache effects. Communication analysis can eliminate barrier synchronization. We present a set of unified compilation techniques that exemplify this convergence in compilers for shared and distributed address space machines, and illustrate their effectiveness using two example applications.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 1995-01-01
Publisher Institution IN PROCEEDINGS OF THE 1995 ACM INTERNATIONAL CONFERENCE ON SUPERCOMPUTING