Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad applica-tion combines computational “vertices ” with communica-tion “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set

or more networks. The participating processors may be scalar machines, multiprocessors, or special-purpose computers, enabling application components to execute on the architecture most appropriate to the algorithm. PVM provides a straightforward and general interface that permits the description

Parallel discrete event simulation (PDES), sometimes I called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises

show that excellent absolute performance can be attained—a general-purpose sort of 1012 Bytes of data executes in 319 seconds on a 240-computer, 960disk cluster—as well as demonstrating near-linear scaling of execution time on representative applications as we vary the number of computers used for a

The Dryad and DryadLINQ systems offer a new programming model for large scale data-parallel computing. They generalize previous execution environments such as SQL and MapReduce in three ways: by providing a general-purpose distributed execution engine for data-parallel applications; by adopting

fully visible to the person running the simulation: it displays the network graphics, animates the message flow and lets the user peek into objects and variables within the model. These features make OMNeT++ a good candidate for both research and educational purposes. The OMNeT++ simulation engine can

Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad application combines computational “vertices ” with communication “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set

A great deal of software is distributed in the form ofexecutable code. The ability to reverse engineer such

general-purpose probabilistic modeling language (such as BLOG) and a generic Metropolis-Hastings MCMC algorithm that supports user-supplied proposal distributions. Our algorithm gains flexibility by using MCMC states that are only partial descriptions of possible worlds; we provide conditions under which

explore some performance implications of both options using native implementations in two commercial relational database systems and in a special purpose inverted list engine. Our performance study shows that while RDBMSs are generally poorly suited for such queries, under certain conditions they can