Catálogo de publicaciones - libros

In [1], Dieter an Mey proposes the addition of an AUTO attribute to the OpenMP DEFAULT clause. A DEFAULT(AUTO) clause would cause the compiler to automatically determine the scoping of variables that are not explicitly classified as shared, private or reduction. While this new feature would be useful and powerful, its implementation would rely on automatic parallelization technology, which has been shown to have significant limitations. In this paper, we implement support for the DEFAULT(AUTO) clause in the Polaris parallelizing compiler. Our modified version of the compiler will translate regions with DEFAULT(AUTO) clauses into regions that have explicit scopings for all variables. An evaluation of our implementation on a subset of the SPEC OpenMP Benchmark Suite shows that with current automatic parallelization technologies, a number of important regions cannot be statically scoped, resulting in a significant loss of speedup. We also compare our compiler’s performance to that of an Early Access version of the Sun Studio 9 Fortran 95 compiler [2].

Although OpenMP has become the leading standard in parallel programming languages, the implementation of its runtime environment is not well discussed in the literature. In this paper, we introduce some of the key data structures required to implement OpenMP workshares in our runtime library and also discuss considerations on how to improve its performance. This includes items such as how to set up a workshare control block queue, how to initialize the data within a control block, how to improve barrier performance and how to handle implicit barrier and nowait situations. Finally, we discuss the performance of this implementation focusing on the EPCC benchmark.

This paper discusses an approach to implement OpenMP on clusters by translating it to Global Arrays (GA). The basic translation strategy from OpenMP to GA is described. GA requires a data distribution; we do not expect the user to supply this; rather, we show how we perform data distribution and work distribution according to OpenMP static loop scheduling. An inspector-executor strategy is employed for irregular applications in order to gather information on accesses to potentially non-local data, group non-local data transfers and overlap communications with local computations. Furthermore, a new directive INVARIANT is proposed to provide information about the dynamic scope of data access patterns. This directive can help us generate efficient codes for irregular applications using the inspector-executor approach. Our experiments show promising results for the corresponding regular and irregular GA codes.

OpenMP allows programmers to specify nested parallelism in parallel applications. In the case of scientific applications, parallel loops are the most important source of parallelism. In this paper we present an automatic mechanism to dynamically detect the best way to exploit the parallelism when having nested parallel loops. This mechanism is based on the number of threads, the problem size, and the number of iterations on the loop. To do that, we claim that programmers must specify the potential application parallelism and give the runtime the responsibility to decide the best way to exploit it. We have implemented this mechanism inside the IBM XL runtime library. Evaluation shows that our mechanism dynamically adapts the parallelism generated to the application and runtime parameters, reaching the same speedup as the best static parallelization (with a priori information).