Performance and programmability of GrPPI for parallel stream processing on multi-cores

Springer Science and Business Media LLC
Adriano Marques Garcia1, Dalvan Griebler2, Cláudio Schepke3, Juan Carlos Figueroa García4, Javier Fernández Muñoz4, Luiz Gustavo Fernandes2
1Department of Computer Science, University of Turin, Turin, Italy
2School of Technology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, RS, Brazil
3Laboratory of Advances Studies in Computation (LEA), Federal University of Pampa, Alegrete, RS, Brazil
4Department of Computer Science, University Carlos III of Madrid, Madrid, Spain

Tóm tắt

AbstractGrPPI library aims to simplify the burdening task of parallel programming. It provides a unified, abstract, and generic layer while promising minimal overhead on performance. Although it supports stream parallelism, GrPPI lacks an evaluation regarding representative performance metrics for this domain, such as throughput and latency. This work evaluates GrPPI focused on parallel stream processing. We compare the throughput and latency performance, memory usage, and programmability of GrPPI against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks and benchmarks with handwritten parallel code using the same backends supported by GrPPI. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is often competitive with handwritten parallel code, the infeasibility of fine-tuning GrPPI is a crucial drawback for emerging applications. Despite this, programmability experiments estimate that GrPPI can potentially reduce the development time of parallel applications by about three times.

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