Brief announcement: the problem based benchmark suite

  • Authors:

  • Julian Shun;Guy E. Blelloch;Jeremy T. Fineman;Phillip B. Gibbons;Aapo Kyrola;Harsha Vardhan Simhadri;Kanat Tangwongsan

  • Affiliations:

  • Carnegie Mellon University, PIttsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Georgetown University, Washington, DC, USA;Intel Labs, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA

  • Venue:
  • Proceedings of the twenty-fourth annual ACM symposium on Parallelism in algorithms and architectures
  • Year:
  • 2012

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Abstract

This announcement describes the problem based benchmark suite (PBBS). PBBS is a set of benchmarks designed for comparing parallel algorithmic approaches, parallel programming language styles, and machine architectures across a broad set of problems. Each benchmark is defined concretely in terms of a problem specification and a set of input distributions. No requirements are made in terms of algorithmic approach, programming language, or machine architecture. The goal of the benchmarks is not only to compare runtimes, but also to be able to compare code and other aspects of an implementation (e.g., portability, robustness, determinism, and generality). As such the code for an implementation of a benchmark is as important as its runtime, and the public PBBS repository will include both code and performance results. The benchmarks are designed to make it easy for others to try their own implementations, or to add new benchmark problems. Each benchmark problem includes the problem specification, the specification of input and output file formats, default input generators, test codes that check the correctness of the output for a given input, driver code that can be linked with implementations, a baseline sequential implementation, a baseline multicore implementation, and scripts for running timings (and checks) and outputting the results in a standard format. The current suite includes the following problems: integer sort, comparison sort, remove duplicates, dictionary, breadth first search, spanning forest, minimum spanning forest, maximal independent set, maximal matching, K-nearest neighbors, Delaunay triangulation, convex hull, suffix arrays, n-body, and ray casting. For each problem, we report the performance of our baseline multicore implementation on a 40-core machine.