Abstract. Symbolic model checking methods have been extended recently to the verification of probabilistic systems. However, the representation of the transition matrix may be expensive for very large systems and may induce a prohibitive cost for the model checking algorithm. In this paper, we propose an approximation method to verify quantitative properties on discrete Markov chains. We give a randomized algorithm to approximate the probability that a property expressed by some positive LTL formula is satisfied with high confidence by a probabilistic system. Our randomized algorithm requires only a succinct representation of the system and is based on an execution sampling method. We also present an implementation and a few classical examples to demonstrate the effectiveness of our approach.

Abstract-The frenetic development of the current architectures places a strain on the current state-of-the-art programming environments. Harnessing the full potential of such architectures has been a tremendous task for the whole scientific computing community.We present DAGuE a generic framework for architecture aware scheduling and management of micro-tasks on distributed many-core heterogeneous architectures. Applications we consider can be represented as a Direct Acyclic Graph of tasks with labeled edges designating data dependencies. DAGs are represented in a compact, problem-size independent format that can be queried on-demand to discover data dependencies, in a totally distributed fashion. DAGuE assigns computation threads to the cores, overlaps communications and computations and uses a dynamic, fully-distributed scheduler based on cache awareness, data-locality and task priority. We demonstrate the efficiency of our approach, using several micro-benchmarks to analyze the performance of different components of the framework, and a Linear Algebra factorization as a use case.

No abstract

ISBN: 0-7695-152International audienceGlobal Computing platforms, large scale clusters and future TeraGRID systems gather thousands of nodes for computing parallel scientific applications. At this scale, node failures or disconnections are frequent events. This Volatility reduces the MTBF of the whole system in the range of hours or minutes. We present MPICH-V, an automatic Volatility tolerant MPI environment based on uncoordinated checkpoint/roll-back and distributed message logging. MPICH-V architecture relies on Channel Memories, Checkpoint servers and theoretically proven protocols to execute existing or new, SPMD and Master-Worker MPI applications on volatile nodes. To evaluate its capabilities, we run MPICH-V within a framework for which the number of nodes, Channels Memories and Checkpoint Servers can be completely configured as well as the node Volatility. We present a detailed performance evaluation of every component of MPICH-V and its global performance for non-trivial parallel applications. Experimental results demonstrate good scalability and high tolerance to node volatility