Parallelization of irregularly coupled regular meshes

Regular meshes are frequently used for modeling physical phenomena on both serial and parallel computers. One advantage of regular meshes is that efficient discretization schemes can be implemented in a straight forward manner. However, geometrically-complex objects, such as aircraft, cannot be easily described using a single regular mesh. Multiple interacting regular meshes are frequently used to describe complex geometries. Each mesh models a subregion of the physical domain. The meshes, or subdomains, can be processed in parallel, with periodic updates carried out to move information between the coupled meshes. In many cases, there are a relatively small number (one to a few dozen) subdomains, so that each subdomain may also be partitioned among several processors. We outline a composite run-time/compile-time approach for supporting these problems efficiently on distributed-memory machines. These methods are described in the context of a multiblock fluid dynamics problem developed at LaRC

Vienna FORTRAN: A FORTRAN language extension for distributed memory multiprocessors

Exploiting the performance potential of distributed memory machines requires a careful distribution of data across the processors. Vienna FORTRAN is a language extension of FORTRAN which provides the user with a wide range of facilities for such mapping of data structures. However, programs in Vienna FORTRAN are written using global data references. Thus, the user has the advantage of a shared memory programming paradigm while explicitly controlling the placement of data. The basic features of Vienna FORTRAN are presented along with a set of examples illustrating the use of these features

Loo.py: From Fortran to performance via transformation and substitution rules

A large amount of numerically-oriented code is written and is being written in legacy languages. Much of this code could, in principle, make good use of data-parallel throughput-oriented computer architectures. Loo.py, a transformation-based programming system targeted at GPUs and general data-parallel architectures, provides a mechanism for user-controlled transformation of array programs. This transformation capability is designed to not just apply to programs written specifically for Loo.py, but also those imported from other languages such as Fortran. It eases the trade-off between achieving high performance, portability, and programmability by allowing the user to apply a large and growing family of transformations to an input program. These transformations are expressed in and used from Python and may be applied from a variety of settings, including a pragma-like manner from other languages.Comment: ARRAY 2015 - 2nd ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming (ARRAY 2015

Pure functions in C: A small keyword for automatic parallelization

© 2017 IEEE. The need for parallel task execution has been steadily growing in recent years since manufacturers mainly improve processor performance by scaling the number of installed cores instead of the frequency of processors. To make use of this potential, an essential technique to increase the parallelism of a program is to parallelize loops. However, a main restriction of available tools for automatic loop parallelization is that the loops often have to be 'polyhedral' and that it is, e.g., not allowed to call functions from within the loops.In this paper, we present a seemingly simple extension to the C programming language which marks functions without side-effects. These functions can then basically be ignored when checking the parallelization opportunities for polyhedral loops. We extended the GCC compiler toolchain accordingly and evaluated several real-world applications showing that our extension helps to identify additional parallelization chances and, thus, to significantly enhance the performance of applications

PIPS Is not (just) Polyhedral Software Adding GPU Code Generation in PIPS

6 pagesInternational audienceParallel and heterogeneous computing are growing in audience thanks to the increased performance brought by ubiquitous manycores and GPUs. However, available programming models, like OPENCL or CUDA, are far from being straightforward to use. As a consequence, several automated or semi-automated approaches have been proposed to automatically generate hardware-level codes from high-level sequential sources. Polyhedral models are becoming more popular because of their combination of expressiveness, compactness, and accurate abstraction of the data-parallel behaviour of programs. These models provide automatic or semi-automatic parallelization and code transformation capabilities that target such modern parallel architectures. PIPS is a quarter-century old source-to-source transformation framework that initially targeted parallel machines but then evolved to include other targets. PIPS uses abstract interpretation on an integer polyhedral lattice to represent program code, allowing linear relation analysis on integer variables in an interprocedural way. The same representation is used for the dependence test and the convex array region analysis. The polyhedral model is also more classically used to schedule code from linear constraints. In this paper, we illustrate the features of this compiler infrastructure on an hypothetical input code, demonstrating the combination of polyhedral and non polyhedral transformations. PIPS interprocedural polyhedral analyses are used to generate data transfers and are combined with non-polyhedral transformations to achieve efficient CUDA code generation

Pure functions in C: A small keyword for automatic parallelization

© 2020, The Author(s). The need for parallel task execution has been steadily growing in recent years since manufacturers mainly improve processor performance by increasing the number of installed cores instead of scaling the processor’s frequency. To make use of this potential, an essential technique to increase the parallelism of a program is to parallelize loops. Several automatic loop nest parallelizers have been developed in the past such as PluTo. The main restriction of these tools is that the loops must be statically analyzable which, among other things, disallows function calls within the loops. In this article, we present a seemingly simple extension to the C programming language which marks functions without side-effects. These functions can then basically be ignored when the automatic parallelizer checks the parallelizability of loops. We integrated the approach into the GCC compiler toolchain and evaluated it by running several real-world applications. Our experiments show that the C extension helps to identify additional parallelization opportunities and, thus, to significantly increase the performance of applications