Search Results for "parallel computing"

A parallel-programming framework for concurrently running large numbers of small autonomous jobs, or microthreads, across multiple cores in a CPU or CPUs in a cluster. NeuraNEP emulates a distributed processing environment capable of handling millions of microthreads in parallel, for example running neural networks with millions of spiking cells. Microthreads are general processing elements that can also represent non-neural elements, such as cell populations, extracellular space, emulating...

Project provides a set of concurrent building blocks (Java & C/C++) that can be used to develop parallel/multi-threaded applications. Components are grouped into 4 categories: 1.Data Structures 2. Parallel Patterns 3.Parallel functions 4.Atomics and STM

Distributed Parallel Programming for Python! This package builds on traditional Python by enabling users to write distributed, parallel programs based on MPI message passing primitives. General python objects can be messaged between processors. Ru

Roomy is a programming language extension for writing parallel disk-based applications. All details of parallelism and disk I/O are hidden within the Roomy library.

Meerkat is a distributed programming environment. It consists of a virtual machine which is suited to parallel processing. The data model is based on the concept of actors, although it is much more permissive than the traditional description.

DKU is an embedded parallel language, or "piggy-back" language that uses function calls to invoke the language's execution model. It tunes task-size to the hardware while hiding hardware details for high performance portability of parallel code.

LIME (Less-is-More) is parallel/concurrent programming environment based on C. Internally, it uses XML technology to describe tasks and their dependencies. Externally, it offers the ANSI C99 programming as well as a set of visually-oriented interfaces.

The CodeTime platform covers every aspect of parallel software from authoring, through distribution, to run-time. Its goals are: high programmer productivity; write once, run high performance anywhere; and wide acceptance.

RAFT-Net is a Fault Tolerant Parallel Distributed Framework. Using a Master-Worker scheme, the framework transparently distributes workunits to workers (drones). These workers can join and leave and as such, the network is very flexible. A modified

Compiles your applications really fast on a cluster of PVM nodes.