O aumento constante da demanda por sistemas computacionais cada vez mais eficientes tem motivado a busca por sistemas híbridos customizados compostos por GPP (General Purpose Processor), FPGAs (Field-Programmable Gate Array) e GPUs (Graphics Processing Units). Quando utilizados em conjunto possibilitam otimizar a relação entre desempenho e consumo de energia. Tais sistemas dependem de técnicas que façam o mapeamento mais adequado considerando o perfil do código fonte. Nesse sentido, este projeto propõe uma técnica para realizar o mapeamento entre GPP e FPGA. Para isso, utilizou-se como base uma abordagem de mineração de dados que avalia a similaridade entre código fonte. A técnica aqui desenvolvida obteve taxas de acertos de 65,67% para códigos sintetizados para FPGA com a ferramenta LegUP e 59,19% para Impulse C, considerando que para GPP o código foi compilado com o GCC (GNU Compiler Collection) utilizando o suporte a OpenMP. Os resultados demonstraram que esta abordagem pode ser empregada como um ponto de decisão inicial no processo de mapeamento em sistemas híbridos, somente analisando o perfil do código fonte sem que haja a necessidade de execução do mesmo para a tomada de decisão. / The steady increasing on demand for efficient computer systems has been motivated the search for customized hybrid systems composed by GPP (general purpose processors), FPGAs (Field- Programmable Gate Array) and GPUs (Graphics Processing Units). When they are used together allow to exploit their computing resources to optimize performance and power consumption. Such systems rely on techniques make the most appropriate mapping considering the profile of source code. Thus, this project proposes a technique to perform the mapping between GPP and FPGA. For this, it is applied a technique based on a data mining approach that evaluates the similarity between source code. The proposed method obtained hit rate 65.67% for codes synthesized in FPGA using LegUP tool and 59.19% for Impulse C tool, whereas for GPP, the source code was compiled on GCC (GNU Compiler Collection) using OpenMP. The results demonstrated that this approach can be used as an initial decision point on the mapping process in hybrid systems, only analyzing the profile of the source code without the need for implementing it for decision-making.

O aumento constante da demanda por sistemas computacionais cada vez mais eficientes tem motivado a busca por sistemas híbridos customizados compostos por GPP (General Purpose Processor), FPGAs (Field-Programmable Gate Array) e GPUs (Graphics Processing Units). Quando utilizados em conjunto possibilitam otimizar a relação entre desempenho e consumo de energia. Tais sistemas dependem de técnicas que façam o mapeamento mais adequado considerando o perfil do código fonte. Nesse sentido, este projeto propõe uma técnica para realizar o mapeamento entre GPP e FPGA. Para isso, utilizou-se como base uma abordagem de mineração de dados que avalia a similaridade entre código fonte. A técnica aqui desenvolvida obteve taxas de acertos de 65,67% para códigos sintetizados para FPGA com a ferramenta LegUP e 59,19% para Impulse C, considerando que para GPP o código foi compilado com o GCC (GNU Compiler Collection) utilizando o suporte a OpenMP. Os resultados demonstraram que esta abordagem pode ser empregada como um ponto de decisão inicial no processo de mapeamento em sistemas híbridos, somente analisando o perfil do código fonte sem que haja a necessidade de execução do mesmo para a tomada de decisão. / The steady increasing on demand for efficient computer systems has been motivated the search for customized hybrid systems composed by GPP (general purpose processors), FPGAs (Field- Programmable Gate Array) and GPUs (Graphics Processing Units). When they are used together allow to exploit their computing resources to optimize performance and power consumption. Such systems rely on techniques make the most appropriate mapping considering the profile of source code. Thus, this project proposes a technique to perform the mapping between GPP and FPGA. For this, it is applied a technique based on a data mining approach that evaluates the similarity between source code. The proposed method obtained hit rate 65.67% for codes synthesized in FPGA using LegUP tool and 59.19% for Impulse C tool, whereas for GPP, the source code was compiled on GCC (GNU Compiler Collection) using OpenMP. The results demonstrated that this approach can be used as an initial decision point on the mapping process in hybrid systems, only analyzing the profile of the source code without the need for implementing it for decision-making.

Advanced digital signal processing systems require specialized high-performance embedded computer architectures. The term high-performance translates to large amounts of data and computations per time unit. The term embedded further implies requirements on physical size and power efficiency. Thus the requirements are of both functional and non-functional nature. This thesis addresses the development of high-performance digital signal processing systems relying on manycore technology. We propose building two-level hierarchical computer architectures for this domain of applications. Further, we outline a tool flow based on methods and analysis techniques for automated, multi-objective mapping of such applications on distributed memory manycore processors. In particular, the focus is put on how to provide a means for tunable strategies for mapping of task graphs on array structured distributed memory manycores, with respect to given application constraints. We argue for code mapping strategies based on predicted execution performance, which can be used in an auto-tuning feedback loop or to guide manual tuning directed by the programmer. Automated parallelization, optimisation and mapping to a manycore processor benefits from the use of a concurrent programming model as the starting point. Such a model allows the programmer to express different types and granularities of parallelism as well as computation characteristics of importance in the addressed class of applications. The programming model should also abstract away machine dependent hardware details. The analytical study of WCDMA baseband processing in radio base stations, presented in this thesis, suggests dataflow models as a good match to the characteristics of the application and as execution model abstracting computations on a manycore. Construction of portable tools further requires a manycore machine model and an intermediate representation. The models are needed in order to decouple algorithms, used to transform and map application software, from hardware. We propose a manycore machine model that captures common hardware resources, as well as resource dependent performance metrics for parallel computation and communication. Further, we have developed a multifunctional intermediate representation, which can be used as source for code generation and for dynamic execution analysis. Finally, we demonstrate how we can dynamically analyse execution using abstract interpretation on the intermediate representation. It is shown that the performance predictions can be used to accurately rank different mappings by best throughput or shortest end-to-end computation latency.