3 research outputs found

    A Simple MPI Library for Lightweight Manycore Processors

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    TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Ciências da Computação.Nas últimas décadas, melhorar o desempenho de núcleos individuais e aumentar o nú- mero de núcleos de alta potência por chip foram as principais tendências na construção de processadores. No entanto, esta combinação levou não apenas a um aumento no poder computacional, mas também a um aumento considerável no seu consumo de energia. Há uma preocupação crescente entre a comunidade científica a respeito da eficiência ener- gética dos supercomputadores modernos. Nos últimos anos, muitos esforços têm sido feitos em pesquisas, buscando soluções alternativas capazes de resolver este problema de escalabilidade e eficiência energética. O desempenho e a eficiência energética providos pelos manycores leves são inegáveis. Contudo, a falta de suporte avançado e portátil para esses processadores, como interfaces padrão de alto desempenho para o desenvolvi- mento de código portável, torna o desenvolvimento de software um desafio. Atualmente, duas abordagens são empregadas tentando aumentar a programabilidade em manycores leves: Sistemas operacionais (SOs) e sistemas de execução (runtimes). A primeira fornece portabilidade mas expõe interfaces de programação complexas no nível do SO aos desen- volvedores. Já a segunda se concentra em fornecer interfaces ricas e de alto desempenho, as quais são específicas do fabricante e resultam em software não portável. Portanto, as soluções existentes forçam os desenvolvedores a escolher entre a portabilidade do software ou um processo de desenvolvimento mais rápido. Para resolver esse dilema, neste traba- lho é proposta uma biblioteca MPI leve e portável (LWMPI) projetada do zero para lidar com as restrições e complexidades dos manycores leves. A LWMPI foi integrada a um SO direcionado a esses processadores, oferecendo assim uma melhor programabilidade e portabilidade implícita para manycores leves, sem incorrer em sobrecargas de desempe- nho excessivas que inviabilizariam o seu uso. Para fornecer uma avaliação abrangente da LWMPI, foram utilizadas três aplicações de uma suíte de benchmarking representativa, usada para avaliar o desempenho de manycores leves, além de um benchmark sintético. Os resultados obtidos no processador Kalray MPPA-256 revelaram que a LWMPI atinge uma performance e uma escalabilidade de desempenho melhor do que uma solução feita especificamente para essa análise e que se utiliza puramente das abstrações de IPC do Nanvix, ao mesmo tempo em que oferece uma interface de programação mais rica.In the last decades, improving the performance of individual cores and increasing the number of high power cores per chip were the main trends in the construction of proces- sors. However, this combination led not only to an increase in the computing capacity, but also to a considerable growth in energy consumption. There is a crescent concern among the scientific community about the energy efficiency of modern supercomputers. In the last years, many efforts have been made in research, searching for alternative solutions capable of solving this problem of scalability and energy efficiency. The performance and energy efficiency provided by lightweight manycores is undeniable. Although, the lack of rich and portable support for these processors, such as high-performance standard inter- faces that deliver portable source codes, makes software development a challenging task. Currently, two approaches are employed trying to improve programmability in lightweight manycores: Operating Systems (OSes) and baremetal runtime systems. The former pro- vides portability but exposes complex OS-level programming interfaces to developers. The latter focuses on providing rich and high performance interfaces, which are vendor- specific and yield to non-portable software. Thus, the existing solutions force software engineers to choose between software portability or a faster development process. To address this dilemma, we propose a portable and lightweight MPI library (LWMPI) de- signed from scratch to cope with restrictions and intricacies of lightweight manycores. We integrated LWMPI into a distributed OS that targets these processors, thus featuring bet- ter programmability and implicit portability for lightweight manycores, without incurring excessive performance overheads that could hinder its use. To deliver a comprehensive evaluation of LWMPI, we relied on three applications from a representative benchmark suite used to assess the performance of lightweight manycores, and a synthetic benchmark. Our results obtained on the Kalray MPPA-256 processor unveiled that LWMPI present better performance and scalability when compared with a specifically made solution that uses the raw Nanvix Inter-Process Communication (IPC) abstractions, while exposing a richer programming interface

    GPU Array Access Auto-Tuning

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    GPUs have been used for years in compute intensive applications. Their massive parallel processing capabilities can speedup calculations significantly. However, to leverage this speedup it is necessary to rethink and develop new algorithms that allow parallel processing. These algorithms are only one piece to achieve high performance. Nearly as important as suitable algorithms is the actual implementation and the usage of special hardware features such as intra-warp communication, shared memory, caches, and memory access patterns. Optimizing these factors is usually a time consuming task that requires deep understanding of the algorithms and the underlying hardware. Unlike CPUs, the internal structure of GPUs has changed significantly and will likely change even more over the years. Therefore it does not suffice to optimize the code once during the development, but it has to be optimized for each new GPU generation that is released. To efficiently (re-)optimize code towards the underlying hardware, auto-tuning tools have been developed that perform these optimizations automatically, taking this burden from the programmer. In particular, NVIDIA -- the leading manufacturer for GPUs today -- applied significant changes to the memory hierarchy over the last four hardware generations. This makes the memory hierarchy an attractive objective for an auto-tuner. In this thesis we introduce the MATOG auto-tuner that automatically optimizes array access for NVIDIA CUDA applications. In order to achieve these optimizations, MATOG has to analyze the application to determine optimal parameter values. The analysis relies on empirical profiling combined with a prediction method and a data post-processing step. This allows to find nearly optimal parameter values in a minimal amount of time. Further, MATOG is able to automatically detect varying application workloads and can apply different optimization parameter settings at runtime. To show MATOG's capabilities, we evaluated it on a variety of different applications, ranging from simple algorithms up to complex applications on the last four hardware generations, with a total of 14 GPUs. MATOG is able to achieve equal or even better performance than hand-optimized code. Further, it is able to provide performance portability across different GPU types (low-, mid-, high-end and HPC) and generations. In some cases it is able to exceed the performance of hand-crafted code that has been specifically optimized for the tested GPU by dynamically changing data layouts throughout the execution
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