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

Bounding the execution time of parallel applications on unrelated multiprocessors

Heterogeneous multiprocessors, that consist of processor types with different execution capabilities, are critical today, and in future, to offer high performance and high energy efficiency. In order to use them in hard real-time systems to support parallel processing, a tight estimation of the upper bound on the completion time (WCET) of parallel applications is needed. This paper presents, for the first time, a closed-form solution for the calculation of the WCET for task-based parallel applications modeled as directed acyclic-graphs (DAG) using the general unrelated multiprocessor model that is capable of modeling a wide range of heterogeneous multiprocessor platforms. The paper contributes with a polynomial time algorithm to calculate the WCET (i.e., makespan) for the unrelated model. In addition, it presents simulation results that are based on modeling a set of representative OpenMP task-based parallel applications from the BOTS benchmark suite

Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable

There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 20 important graph problems. We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We have made the implementations developed in this work publicly-available as the Graph-Based Benchmark Suite (GBBS).Comment: This is the full version of the paper appearing in the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 201

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