Compiling a High-Level Directive-Based Programming Model for GPGPUs,

Abstract. OpenACC is an emerging directive-based programming model for programming accelerators that typically enable non-expert programmers to achieve portable and productive performance of their applications. In this paper, we present the research and development challenges, and our solutions to create an open-source OpenACC compiler in a main stream compiler framework (OpenUH of a branch of Open64). We discuss in details our loop mapping techniques, i.e. how to distribute loop iterations over the GPGPU's threading architectures, as well as their impacts on performance. The runtime support of this programming model are also presented. The compiler was evaluated with several commonly used benchmarks, and delivered similar performance to those obtained using a commercial compiler. We hope this implementation to serve as compiler infrastructure for researchers to explore advanced compiler techniques, to extend OpenACC to other programming languages, or to build performance tools used with OpenACC programs

PERFORMANCE EVALUATION OF MEMORY AND COMPUTATIONALLY BOUND CHEMISTRY APPLICATIONS ON STREAMING GPGPUS AND MULTI-CORE X86 CPUS

In recent years, multi-core processors have come to dominate the field in desktop and high performance computing. Graphics processors traditionally used in CAD, video games, and other 3-d applications, have become more programmable and are now suitable for general purpose computing. This thesis explores multi-core processors and GPU performance and limitations in two computational chemistry applications: a memory bound component of ab-initio modeling and a computationally bound Monte Carlo simulation. For the applications presented in this thesis, exploiting multiple processors is done using a variety of tools and languages including OpenMP and MKL. Brook+ and the Compute Abstraction Layer streaming environments are used to accelerate applications on AMD GPUs. This thesis gives qualitative assertions about these languages and tools regarding ease of use and optimization in addition to quantitative analyses of performance. GPUs can yield modest performance improvements with little effort in some applications and even larger speedups with simple optimizations

Intel-oneAPI para Computación Heterogénea

Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento de Departamento de Arquitectura de Computadores y Automática, Curso 2020/2021"oneAPI is a cross-industry, open, standards-based unified programming model that delivers a common developer experience across accelerator architectures—for faster application performance, more productivity, and greater innovation." -www.oneapi.com The Intel DPC++ Compatibility Tool is a component of the Intel oneAPI base toolkit. This tool automatically transforms CUDA code into Data Parallel C++ (DPC++) assisting in the migration process. This project consists of an analysis of the DPC++ Compatibility Tool, considering the manual intervention required and the problems encountered while migrating the Rodinia benchmarks. And a comparative study of the performance obtained by the migrated code."oneAPI es un modelo de programación unificado, abierto y basado en estándares, que ofrece una experiencia de desarrollador común en todas las arquitecturas de aceleradores, para un rendimiento de aplicaciones más rápido, más productividad y una mayor innovación." -www.oneapi.com La herramienta de compatibilidad DPC++ de Intel es un componente del oneAPI Base Toolkit. esta herramienta transforma automáticamente código CUDA en Data Parallel C++ (DPC++) ayudando en el proceso de migración. Este proyecto consiste en un análisis de la herramienta de compatibilidad DPC++, considerando la intervención manual requerida y los problemas encontrados al migrar los benchmarks de Rodinia. Y un estudio comparativo del rendimiento obtenido por el código migrado.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu