HPSM: uma API em linguagem c++ para programas com laços paralelos com suporte a multi-CPUs e Multi-GPUs

• Ano de defesa: 2016
• Autor(a) principal: Di Domenico, Daniel
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Ciência da Computação; UFSM; Programa de Pós-Graduação em Informática; Centro de Tecnologia
• Palavras-chave em Português: API C++; Programação paralela; Laços paralelos; Computação heterogênea; GPU; C++ API; Parallel programming; Parallel loops; Heterogenous computing; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/12171

Parallel architectures has been ubiquitous for some time now. However, the word ubiquitous can’t be applied to parallel programs, because there is a greater complexity to code them comparing to ordinary programs. This fact is aggravated when the programming also involves accelerators, like GPUs, which demand the use of tools with scpecific resources. Considering this setting, there are programming models that make easier the codification of parallel applications to explore accelerators, nevertheless, we don’t know APIs that allow implementing programs with parallel loops that can be processed simultaneously by multiple CPUs and multiple GPUs. This works presents a high-level C++ API called HPSM aiming to make easier and more efficient the codification of parallel programs intended to explore multi-CPU and multi-GPU architectures. Following this idea, the desire is to improve performance through the sum of resources. HPSM uses parallel loops and reductions implemented by three parallel back-ends, being Serial, OpenMP and StarPU. Our hypothesis estimates that scientific applications can explore heterogeneous processing in multi-CPU and multi-GPU to achieve a better performance than exploring just accelerators. Comparisons with other parallel programming interfaces demonstrated that HPSM can reduce a multi-CPU and multi-GPU code in more than 50%. The use of the new API can introduce impact to program performance, where experiments showed a variable overhead for each application, that can achieve a maximum value of 16,4%. The experimental results confirmed the hypothesis, because the N-Body, Hotspot e CFD applications achieved gains using just CPUs and just GPUs, as well as overcame the performance achieved by just accelerators (GPUs) through the combination of multi-CPU and multi-GPU.