Mixed Parallel Programming Models Using Parallel Tasks

TL;DR: The authors explore and evaluate some of the factors required for the automatic construction of SLAs, with broad consideration for Financial Risk and the potential formulation of a Grid Economy as a commodity market, which may in future involve the trading and hedging of risk, options, futures and structured products.

TL;DR: This chapter first presents taxonomies on approaches for resource allocation across resource sharing networks such as Grids, and examines existing systems and classifies them under their architectures, operational models, support for the life-cycle of virtual organisations, and resource control techniques.

TL;DR: This study proposes a multithreaded parallel routing algorithm for LSI design that reduces the wait time of the threads, and experimentally confirmed that the routing quality was not degraded compared to the sequential execution.

TL;DR: This chapter presents HLA_Grid_RePast, a middleware platform for executing large scale collaborating RePast agent-based models on the Grid, and provides performance results and analysis on Quality of Service from a deployment of the system between UK and Singapore.

TL;DR: Four programming systems that attempt to cleanly integrate the two forms of parallelism in a programming model are described: Fx, Opus, data-parallel Orca, and Braid.

TL;DR: This paper examines diagonally implicit iteration methods for solving implicit Runge–Kutta methods with high stage order on parallel computers and shows that the reduced accuracy often shown when integrating stiff problems by means of DIRK methods is not shown by the D IRK methods developed in this paper.

TL;DR: This paper explores a new compiler optimization for regular scientific applications-the simultaneous exploitation of task and data parallelism, implemented as part of the PARADIGM HPF compiler framework the authors have developed.

TL;DR: This paper addresses the problem of optimizing throughput in task pipelines and presents two new solution algorithms based on dynamic programming and finds the optimal mapping of k tasks onto P processors in O(P4k2) time.