Towards Big Biology: high-performance verification of large concurrent systems

Bal, H.E. [Promotor]Fokkink, W.J. [Promotor]Kielmann, T. [Copromotor

Model checking concurrent and real-time systems : the PAT approach

Ph.DDOCTOR OF PHILOSOPH

High-Performance and Power-Aware Graph Processing on GPUs

Graphs are a common representation in many problem domains, including engineering, finance, medicine, and scientific applications. Different problems map to very large graphs, often involving millions of vertices. Even though very efficient sequential implementations of graph algorithms exist, they become impractical when applied on such actual very large graphs. On the other hand, graphics processing units (GPUs) have become widespread architectures as they provide massive parallelism at low cost. Parallel execution on GPUs may achieve speedup up to three orders of magnitude with respect to the sequential counterparts. Nevertheless, accelerating efficient and optimized sequential algorithms and porting (i.e., parallelizing) their implementation to such many-core architectures is a very challenging task. The task is made even harder since energy and power consumption are becoming constraints in addition, or in same case as an alternative, to performance. This work aims at developing a platform that provides (I) a library of parallel, efficient, and tunable implementations of the most important graph algorithms for GPUs, and (II) an advanced profiling model to analyze both performance and power consumption of the algorithm implementations. The platform goal is twofold. Through the library, it aims at saving developing effort in the parallelization task through a primitive-based approach. Through the profiling framework, it aims at customizing such primitives by considering both the architectural details and the target efficiency metrics (i.e., performance or power)

Algorithms for SCC Decomposition

We study and improve the OBF technique [Barnat, J. and P.Moravec, Parallel algorithms for finding SCCs in implicitly given graphs, in: Proceedings of the 5th International Workshop on Parallel and Distributed Methods in Verification (PDMC 2006), LNCS (2007)], which was used in distributed algorithms for the decomposition of a partitioned graph into its strongly connected components. In particular, we introduce a recursive variant of OBF and experimentally evaluate several different implementations of it that vary in the degree of parallelism. For the evaluation we used synthetic graphs with a few large components and graphs with many small components. We also experimented with graphs that arise as state spaces in real model checking applications. The experimental results are compared with that of other successful SCC decomposition techniques [Orzan, S., ''On Distributed Verification and Verified Distribution,'' Ph.D. thesis, Free University of Amsterdam (2004); Fleischer, L.K., B. Hendrickson and A. Pinar, On identifying strongly connected components in parallel, in: Parallel and Distributed Processing, IPDPS Workshops, Lecture Notes in Computer Science 1800, 2000, pp. 505-511]