Improving the Performance of OpenMP by Array Privatization

2003

A so-called SPMD style OpenMP program can achieve scalability on ccNUMA systems by means of array privatization, and earlier research has shown good performance under this approach. Since it is hard to write SPMD OpenMP code, we showed a strategy for the automatic translation of many OpenMP constructs into SPMD style in our previous work. In this paper, we first explain how to interprocedurally detect whether the OpenMP program consistently schedules the parallel loops. If the parallel loops are consistently scheduled, we may carry out array privatization according to OpenMP semantics. We give two examples of code patterns that can be handled despite the fact that they are not consistent, and where the strategy used to translate them differs from the straightforward approach that can otherwise be applied.

Concurrency and Computation: Practice and Experience, 2002

OpenMP is emerging as a viable high-level programming model for shared memory parallel systems. It was conceived to enable easy, portable application development on this range of systems, and it has also been implemented on cache-coherent Non-Uniform Memory Access (ccNUMA) architectures. Unfortunately, it is hard to obtain high performance on the latter architecture, particularly when large numbers of threads are involved. In this paper, we discuss the difficulties faced when writing OpenMP programs for ccNUMA systems, and explain how the vendors have attempted to overcome them. We focus on one such system, the SGI Origin 2000, and perform a variety of experiments designed to illustrate the impact of the vendor's efforts. We compare codes written in a standard, loop-level parallel style under OpenMP with alternative versions written in a Single Program Multiple Data (SPMD) fashion, also realized via OpenMP, and show that the latter consistently provides superior performance. A carefully chosen set of language extensions can help us translate programs from the former style to the latter (or to compile directly, but in a similar manner). Syntax for these extensions can be borrowed from HPF, and some aspects of HPF compiler technology can help the translation process. It is our expectation that an extended language, if well compiled, would improve the attractiveness of OpenMP as a language for high-performance computation on an important class of modern architectures. Copyright © 2002 John Wiley & Sons, Ltd.

Concurrency and Computation: Practice and Experience, 2007

OpenMP has gained wide popularity as an API for parallel programming on shared memory and distributed shared memory platforms. Despite its broad availability, there remains a need for a portable, robust, open source, optimizing OpenMP compiler for C/C++/Fortran 90, especially for teaching and research, e.g. into its use on new target architectures, such as SMPs with chip multithreading, as well as learning how to translate for clusters of SMPs. In this paper, we present our efforts to design and implement such an OpenMP compiler on top of Open64, an open source compiler framework, by extending its existing analysis and optimization and adopting a source-to-source translator approach where a native back end is not available. The compilation strategy we have adopted and the corresponding runtime support are described. The OpenMP validation suite is used to determine the correctness of the translation. The compiler's behavior is evaluated using benchmark tests from the EPCC microbenchmarks and the NAS parallel benchmark.

Lecture Notes in Computer Science, 2013

OpenMP is an explicit parallel programming model that offers reasonable productivity. Its memory model assumes a shared address space, and hence the direct translation -as done by common OpenMP compilers -requires an underlying shared-memory architecture. Many lab machines include 10s of processors, built from commodity components and thus include distributed address spaces. Despite many efforts to provide higher productivity for these platforms, the most common programming model uses message passing, which is substantially more tedious to program than shared-address-space models. This paper presents a compiler/runtime system that translates OpenMP programs into message passing variants and executes them on clusters up to 64 processors. We build on previous work that provided a proof of concept of such translation. The present paper describes compiler algorithms and runtime techniques that provide the automatic translation of a first class of OpenMP applications: those that exhibit regular write array subscripts and repetitive communication. We evaluate the translator on representative benchmarks of this class and compare their performance against hand-written MPI variants. In all but one case, our translated versions perform close to the hand-written variants.

2008

Abstract. OpenMP is a portable shared memory programming interface that promises high programmer productivity for multithreaded applications. It is designed for small and middle sized shared memory systems. We have developed strategies to extend OpenMP to clusters via compiler translation to a Global Arrays program. In this paper, we describe our implementation of the translation in the Open64 compiler, and we focus on the strategies to improve sequential region translations. Our work is based upon the open source Open64 compiler suite for C, C++, and Fortran90/95. 1