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A bstract-Stream Processing applications are spread across different sectors o f industry and people's daily lives. The increasing data we produce, such as audio, video, im age, and text are demanding quickly and efficiently com putation. It can be done through Stream Parallelism , which is still a challenging task and m ost reserved for experts. We introduce a Stream Processing fram ework for assessing Parallel Programming Interfaces (PPIs).Our fram ework targets m ulti-core architectures and C++ stream processing applications, providing an API that abstracts the details o f the stream operators o f these applications. Therefore, users can easily identify all the basic operators and im plem ent parallelism through different PPIs. In this paper, we present the proposed fram ework, im plem ent three applications using its API, and show how it w orks, by using it to parallelize and evaluate the applications w ith the PPIs Intel TBB, FastFlow, and SPar. The perform ance results were consistent with the literature.

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Since the demand for computing power increases, new architectures emerged to obtain better performance. Reducing the power and energy consumption of these architectures is one of the main challenges to achieving high-performance computing. Current research trends aim at developing new software and hardware techniques to achieve the best performance and energy trade-offs. In this work, we investigate the impact of different CPU frequency scaling techniques such as ondemand, performance, and powersave on the power and energy consumption of multi-core based computer infrastructure. We apply these techniques in PAMPAR, a parallel benchmark suite implemented in PThreads, OpenMP, MPI-1, and MPI-2 (spawn). We measure the energy and execution time of 10 benchmarks, varying the number of threads. Our results show that although powersave consumes up to 43.1% less power than performance and ondemand governors, it consumes the triple of energy due to the high execution time. Our experiments also show that the performance governor consumes up to 9.8% more energy than ondemand for CPU-bound benchmarks. Finally, our results show that PThreads has the lowest power consumption, consuming less than the sequential version for memory-bound benchmarks. Regarding performance, the performance governor achieved 3% of performance over the ondemand.