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

We introduce the Split-C language, a parallel extension of C intended for high performance programming on distributed memory multiprocessors, and demonstrate the use of the language in optimizing parallel programs. Split-C provides a global address space with a clear concept of locality and unusual

Co-Array Fortran, formerly known as F--, is a small extension of Fortran 95 for parallel processing. A Co-Array Fortran program is interpreted as if it were replicated a number of times and all copies were executed asynchronously. Each copy has its own set of data objects and is termed an image

Orca is a language for implementing parallel applications on loosely coupled distributed systems. Unlike most languages for distributed programming, it allows processes on different machines to share data. Such data are encapsulated in data-objects, which are instances of user-defined abstract data

ftAbstract approved-

The role of public surveys in measuring

uniform way, making the network appear to the application as an integrated, non-uniform memory access, shared-memory multiprocessor. This simplifies the development of applications and allows compute-intensive parallel programs to effectively harness the potential of multiple nodes. Amber programs

tration and data management as data is passed to, from, and among application invoca-grid computing. Even if a single large parallel cluster suffices, users will not always have access to the same system (e.g., big machines may be congested or temporarily unavailable because of maintenance). Thus

Software Engineering: static and dynamic program analysis, performance engineering, mining software repositories, statistical risk prediction, software system visualization. Simulation: discrete events simulation, statistical modeling. Machine learning: applied machine learning, data mining

General Block Min Max Criterion (GBMM) is a pre-2Dchopped robust estimation method designed by Dr. Schubert. It may be applied on image clarification, pollution detection … etc. This thesis tries to parallelize GBMM method not only to speedup it, but also to see whether a pre-chopped algorithm