STR2RTS: Refactored StreamIT Benchmarks into Statically Analyzable Parallel Benchmarks for WCET Estimation & Real-Time Scheduling

International audienceWe all had quite a time to find non-proprietary architecture-independent exploitable parallel benchmarks for Worst-Case Execution Time (WCET) estimation and real-time scheduling. However , there is no consensus on a parallel benchmark suite, when compared to the single-core era and the Mälardalen benchmark suite [12]. This document bridges part of this gap, by presenting a collection of benchmarks with the following good properties: (i) easily analyzable by static WCET estimation tools (written in structured C language, in particular neither goto nor dynamic memory allocation, containing flow information such as loop bounds); (ii) independent from any particular run-time system (MPI, OpenMP) or real-time operating system. Each benchmark is composed of the C source code of its tasks, and an XML description describing the structure of the application (tasks and amount of data exchanged between them when applicable). Each benchmark can be integrated in a full end-to-end empirical method validation protocol on multi-core architecture. This proposed collection of benchmarks is derived from the well known StreamIT [21] benchmark suite and will be integrated in the TACleBench suite [11] in a near future. All these benchmarks are available at https://gitlab.inria.fr/brouxel/STR2RTS

A Comparison of Parallel Graph Processing Implementations

The rapidly growing number of large network analysis problems has led to the emergence of many parallel and distributed graph processing systems---one survey in 2014 identified over 80. Since then, the landscape has evolved; some packages have become inactive while more are being developed. Determining the best approach for a given problem is infeasible for most developers. To enable easy, rigorous, and repeatable comparison of the capabilities of such systems, we present an approach and associated software for analyzing the performance and scalability of parallel, open-source graph libraries. We demonstrate our approach on five graph processing packages: GraphMat, the Graph500, the Graph Algorithm Platform Benchmark Suite, GraphBIG, and PowerGraph using synthetic and real-world datasets. We examine previously overlooked aspects of parallel graph processing performance such as phases of execution and energy usage for three algorithms: breadth first search, single source shortest paths, and PageRank and compare our results to Graphalytics.Comment: 10 pages, 10 figures, Submitted to EuroPar 2017 and rejected. Revised and submitted to IEEE Cluster 201