The purpose of this benchmark is to propose several optimization techniques and to test their existence in current OpenMP compilers. Examples are the removal of redundant synchronization constructs, effective constructs for alternative code and orphaned directives. The effectiveness of the compiler generated code is measured by comparing different OpenMP constructs and compilers. If possible, we also compare with the hand coded &#x22;equivalent&#x22; solution. Six out of seven proposed optimization techniques are already implemented in different compilers. However, most compilers implement only one or two of them.

Matthias S. M&#xfc;ller

An OpenMP Compiler Benchmark

The state of modern computer systems has evolved to allow easy access to multiprocessor systems by supporting multiple processors on a single physical package. As the multiprocessor hardware evolves, new ways of programming it are also developed. Some inventions may merely be adopting and standardizing the older paradigms. One such evolving standard for programming shared-memory parallel computers is the OpenMP API. The Standard Performance Evaluation Corporation (SPEC) has created a suite of parallel programs called SPEC OMP to compare and evaluate modern shared-memory multiprocessor systems using the OpenMP standard. We have studied these benchmarks in detail to understand their performance on a modern architecture. In this paper, we present detailed measurements of the benchmarks. We organize, summarize, and display our measurements using a Quantitative Model. We present a detailed discussion and derivation of the model. Also, we discuss the important loops in the SPEC OMPM2001 benchmarks and the reasons for less than ideal speedup on our platform.

Vishal Aslot

Rudolf Eigenmann

Quantitative Performance Analysis of the SPEC OMPM2001 Benchmarks

The OpenMP standard defines an Application Programming Interface (API) for shared memory computers. Since its introduction in 1997, it has grown to become one of the most commonly used API's for parallel programming. But success in the market doesn't necessarily imply successful computer science. Is OpenMP a &#x22;good&#x22; programming environment? What does it even mean to call a programming environment good? And finally, once we understand how good or bad OpenMP is; what can we do to make it even better? In this paper, we will address these questions.

Timothy G. Mattson

How Good is OpenMP

In this work, we present an OpenMP implementation suitable for multiprogrammed environments on Intel-based SMP systems. This implementation consists of a runtime system and a resource manager, while we use the NanosCompiler to transform OpenMP-coded applications into code with calls to our runtime system. The resource manager acts as the operating system scheduler for the applications built with our runtime system. It executes a custom made scheduling policy to distribute the available physical processors to the active applications. The runtime system cooperates with the resource manager in order to adapt each application's generated parallelism to the number of processors allocated to it, according to the resource manager scheduling policy. We use the OpenMP version of the NAS Parallel Benchmark suite in order to evaluate the performance of our implementation. In our experiments we compare the performance of our implementation with that of a commercial OpenMP implementation. The comparison proves that our approach performs better both on a dedicated and on a heavily multiprogrammed environment.

Vasileios K. Barekas

Panagiotis E. Hadjidoukas

Eleftherios D. Polychronopoulos

Theodore S. Papatheodorou

A Multiprogramming Aware OpenMP Implementation

Eduard Ayguade

Barbara Chapman

Introduction

In this paper we describe the extension of the CAPO parallelization support tool to support multilevel parallelism based on OpenMP directives. CAPO generates OpenMP directives with extensions supported by the NanosCompiler to allow for directive nesting and definition of thread groups. We report some results for several benchmark codes and one full application that have been parallelized using our system.

Haoqiang Jin

Gabriele Jost

Jerry Yan

Marc Gonzalez

Xavier Martorell

Automatic Multilevel Parallelization Using OpenMP

In this paper we explore the idea of customizing and reusing loop schedules to improve the scalability of non-regular numerical codes in shared-memory architectures with non-uniform memory access latency. The main objective is to implicitly setup affinity links between threads and data, by devising loop schedules that achieve balanced work distribution within irregular data spaces and reusing them as much as possible along the execution of the program for better memory access locality. This transformation provides a great deal of flexibility in optimizing locality, without compromising the simplicity of the shared-memory programming paradigm. In particular, the programmer does not need to explicitly distribute data between processors. The paper presents practical examples from real applications and experiments showing the efficiency of the approach.

Dimitrios S. Nikolopoulos

Ernest Artiaga

Eduard Ayguad&#xe9;

Jes&#xfa;s Labarta

Scaling Non-Regular Shared-Memory Codes by Reusing Custom Loop Schedules

A performance-prediction model is presented, which describes different hierarchical workload decomposition strategies for particle in cell (PIC) codes on Clusters of Symmetric MultiProcessors. The devised workload decomposition is hierarchically structured: a higher-level decomposition among the computational nodes, and a lower-level one among the processors of each computational node. Several decomposition strategies are evaluated by means of the prediction model, with respect to the memory occupancy, the parallelization efficiency and the required programming effort. Such strategies have been implemented by integrating the high-level languages High Performance Fortran (at the inter-node stage) and OpenMP (at the intra-node one). The details of these implementations are presented, and the experimental values of parallelization efficiency are compared with the predicted results.

Sergio Briguglio

Beniamino Di Martino

Gregorio Vlad

A Performance-Prediction Model for PIC Applications on Clusters of Symmetric MultiProcessors: Validation with Hierarchical HPF+OpenMP Implementation

Dense linear algebra libraries need to cope efficiently with a range of input problem sizes and shapes. Inherently this means that parallel implementations have to exploit parallelism wherever it is present. While OpenMP allows relatively fine grain parallelism to be exploited in a shared memory environment it currently lacks features to make it easy to partition computation over multiple array indices or to overlap sequential and parallel computations. The inherent flexible nature of shared memory paradigms such as OpenMP poses other difficulties when it becomes necessary to optimise performance across successive parallel library calls. Notions borrowed from distributed memory paradigms, such as explicit data distributions help address some of these problems, but the focus on data rather than work distribution appears misplaced in an SMP context.

C. Addison

Y. Ren

M. van Waveren

OpenMP Issues Arising in the Development of Parallel BLAS and LAPACK Libraries

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Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

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