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Orientador: Rodolfo Jardim de Azevedo / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-17T02:02:14Z (GMT). No. of bitstreams: 1 Goldstein_FelipePortavales_M.pdf: 2303926 bytes, checksum: c44512059a990654552904a0f94d74f2 (MD5) Previous issue date: 2010 / Resumo: A adoção de processadores com múltiplos núcleos pela indústria, levou à necessidade de novas técnicas para facilitar a programação de software paralelo. A técnica chamada memórias transacionais é uma das mais promissoras. Esta técnica é capaz de executar tarefas concorrentemente de forma otimista, o que permite um bom desempenho. Outra vantagem é que a sua utilização é muito mais simples comparada com a técnica clássica de exclusão mútua. Neste trabalho é proposto o primeiro modelo de memória transacional para arquiteturas híbridas, neste caso a arquitetura alvo é o processador Cell BE. O processador Cell BE é especialmente complexo por causa das dificuldades que a arquitetura deste processador impõe ao programador quando se necessita acessar a memória global compartilhada. O modelo proposto age como uma camada entre o programa e a memória principal, permitindo um acesso transparente aos dados, garantindo coerência e realizando o controle de concorrência de forma automática. O modelo proposto utiliza Software Cache combinado com a memória transacional para facilitar o acesso à memória externa a partir dos SPEs. Ele foi implementado e testado utilizando 8 aplicativos benchmark diferentes, mostrando sua viabilidade para casos de uso reais. Foi feita uma análise detalhada de cada parte da arquitetura proposta com relação ao impacto no desempenho geral do sistema. Este modelo foi capaz de obter um desempenho até duas vezes superior à implementação utilizando um mutex global. As vantagens da utilização se concentram principalmente na facilidade de uso, garantias de coerência e por evitar alguns tipos de bugs que seriam comuns em uma implementação com mutex, como por exemplo dead-locks. Este trabalho obteve o prêmio de melhor artigo no SBAC-PAD 2008 / Abstract: The adoption of multi-core processors by the industry has pushed towards the development of new techniques to simplify programming parallel software. The technique called transactional memories is one of the most promising. This technique is able to execute multiple tasks concurrently in an optimistic way to achieve a better performance. Another advantage is that the usage of this technique is simpler than the classic mutual exclusion. This work proposes the first transactional memory model for hybrid architectures, in this case the target architecture is the Cell BE processor. The Cell BE is specially complex because of the dificulties when acessing the main shared memory from one of the SPEs. The proposed model acts as a layer between the program running and the main shared memory, allowing transparent access to the data, guaranteeing coherency and automatic concurrency control. The proposed model uses a Software Cache combined with a transactional memory to facilitate the acess to the main memory from the SPEs. This model was implemented and tested using 8 benchmark applications, showing its feasability in real use cases. A detailed analysis of its internal parts has been made to show the impact of each part in the overal system performance. The model was able to achieve a performance up to two times better than a similar implementation using a global mutex. The advantages of this model rely on its usability, coherency guaranty and because it is able to avoid concurrency programming bugs such as dead-lock, which are common in a mutex implementation. This work won the best paper award at SBAC-PAD 2008 / Mestrado / Arquitetura de Computadores / Mestre em Ciência da Computação

This thesis contributes to the field of performance analysis in High Performance Computing with new concepts for in-memory event tracing. Event tracing records runtime events of an application and stores each with a precise time stamp and further relevant metrics. The high resolution and detailed information allows an in-depth analysis of the dynamic program behavior, interactions in parallel applications, and potential performance issues. For long-running and large-scale parallel applications, event-based tracing faces three challenges, yet unsolved: the number of resulting trace files limits scalability, the huge amounts of collected data overwhelm file systems and analysis capabilities, and the measurement bias, in particular, due to intermediate memory buffer flushes prevents a correct analysis. This thesis proposes concepts for an in-memory event tracing workflow. These concepts include new enhanced encoding techniques to increase memory efficiency and novel strategies for runtime event reduction to dynamically adapt trace size during runtime. An in-memory event tracing workflow based on these concepts meets all three challenges: First, it not only overcomes the scalability limitations due to the number of resulting trace files but eliminates the overhead of file system interaction altogether. Second, the enhanced encoding techniques and event reduction lead to remarkable smaller trace sizes. Finally, an in-memory event tracing workflow completely avoids intermediate memory buffer flushes, which minimizes measurement bias and allows a meaningful performance analysis. The concepts further include the Hierarchical Memory Buffer data structure, which incorporates a multi-dimensional, hierarchical ordering of events by common metrics, such as time stamp, calling context, event class, and function call duration. This hierarchical ordering allows a low-overhead event encoding, event reduction and event filtering, as well as new hierarchy-aided analysis requests. An experimental evaluation based on real-life applications and a detailed case study underline the capabilities of the concepts presented in this thesis. The new enhanced encoding techniques reduce memory allocation during runtime by a factor of 3.3 to 7.2, while at the same do not introduce any additional overhead. Furthermore, the combined concepts including the enhanced encoding techniques, event reduction, and a new filter based on function duration within the Hierarchical Memory Buffer remarkably reduce the resulting trace size up to three orders of magnitude and keep an entire measurement within a single fixed-size memory buffer, while still providing a coarse but meaningful analysis of the application. This thesis includes a discussion of the state-of-the-art and related work, a detailed presentation of the enhanced encoding techniques, the event reduction strategies, the Hierarchical Memory Buffer data structure, and a extensive experimental evaluation of all concepts.

This thesis contributes to the field of performance analysis in High Performance Computing with new concepts for in-memory event tracing. Event tracing records runtime events of an application and stores each with a precise time stamp and further relevant metrics. The high resolution and detailed information allows an in-depth analysis of the dynamic program behavior, interactions in parallel applications, and potential performance issues. For long-running and large-scale parallel applications, event-based tracing faces three challenges, yet unsolved: the number of resulting trace files limits scalability, the huge amounts of collected data overwhelm file systems and analysis capabilities, and the measurement bias, in particular, due to intermediate memory buffer flushes prevents a correct analysis. This thesis proposes concepts for an in-memory event tracing workflow. These concepts include new enhanced encoding techniques to increase memory efficiency and novel strategies for runtime event reduction to dynamically adapt trace size during runtime. An in-memory event tracing workflow based on these concepts meets all three challenges: First, it not only overcomes the scalability limitations due to the number of resulting trace files but eliminates the overhead of file system interaction altogether. Second, the enhanced encoding techniques and event reduction lead to remarkable smaller trace sizes. Finally, an in-memory event tracing workflow completely avoids intermediate memory buffer flushes, which minimizes measurement bias and allows a meaningful performance analysis. The concepts further include the Hierarchical Memory Buffer data structure, which incorporates a multi-dimensional, hierarchical ordering of events by common metrics, such as time stamp, calling context, event class, and function call duration. This hierarchical ordering allows a low-overhead event encoding, event reduction and event filtering, as well as new hierarchy-aided analysis requests. An experimental evaluation based on real-life applications and a detailed case study underline the capabilities of the concepts presented in this thesis. The new enhanced encoding techniques reduce memory allocation during runtime by a factor of 3.3 to 7.2, while at the same do not introduce any additional overhead. Furthermore, the combined concepts including the enhanced encoding techniques, event reduction, and a new filter based on function duration within the Hierarchical Memory Buffer remarkably reduce the resulting trace size up to three orders of magnitude and keep an entire measurement within a single fixed-size memory buffer, while still providing a coarse but meaningful analysis of the application. This thesis includes a discussion of the state-of-the-art and related work, a detailed presentation of the enhanced encoding techniques, the event reduction strategies, the Hierarchical Memory Buffer data structure, and a extensive experimental evaluation of all concepts.