Abstract
Stream processing applications have seen an increasing demand with the raised availability of sensors, IoT devices, and user data. Modern systems can generate millions of data items per day that require to be processed timely. To deal with this demand, application programmers must consider parallelism to exploit the maximum performance of the underlying hardware resources. In this work, we introduce improvements to stream processing applications by exploiting fine-grained data parallelism (via Map and MapReduce) inside coarse-grained stream parallelism stages. The improvements are including techniques for identifying data parallelism in sequential codes, a new language, semantic analysis, and a set of definition and transformation rules to perform source-to-source parallel code generation. Moreover, we investigate the feasibility of employing higher-level programming abstractions to support the proposed optimizations. For that, we elect SPar programming model as a use case, and extend it by adding two new attributes to its language and implementing our optimizations as a new algorithm in the SPar compiler. We conduct a set of experiments in representative stream processing and data-parallel applications. The results showed that our new compiler algorithm is efficient and that performance improved by up to 108.4x in data-parallel applications. Furthermore, experiments evaluating stream processing applications towards the composition of stream and data parallelism revealed new insights. The results showed that such composition may improve latencies by up to an order of magnitude. Also, it enables programmers to exploit different degrees of stream and data parallelism to accomplish a balance between throughput and latency according to their necessity.
Published Version
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