5 projects tagged "Parallel processing"
Updated 05 Oct 2010
Charm++
| Pop | 24.96 |
|---|---|
| Vit | 1.00 |
Charm++ is a portable adaptive runtime system for parallel applications. Application developers create an object-based decomposition of the problem of interest, and the runtime system manages issues of communication, mapping, load balancing, fault tolerance, and more. Sequential code implementing the methods of these parallel objects is written in C++. Calls to libraries in C++, C, and Fortran are common and straightforward. Charm++ is portable across individual workstations, clusters, accelerators (Cell SPEs and GPUs), and supercomputers such as those sold by IBM (Blue Gene, POWER) and Cray (XT3/4/5/6). Applications based on Charm++ are used on at least 5 of the 20 most powerful computers in the world.
Updated 30 Apr 2010
RunJRun
| Pop | 28.35 |
|---|---|
| Vit | 33.38 |
RunJRun is a very simple system for doing parallel processing in Java, using Amazon Elastic Compute Cloud (EC2) instances as compute nodes. The basic compute unit is a Runnable, Serializable Java object, a "task" for short. A user submits a list of such tasks to RunJRun. Each task then has its run() method invoked on an EC2 instance. To use it, you'll need an Amazon Machine Image (AMI) that has the RunJRun server-side software installed; several such AMIs are available.
Updated 09 Dec 2012
FastFlow
| Pop | 122.72 |
|---|---|
| Vit | 6.32 |
FastFlow is a pattern-based programming framework targeting streaming applications. It implements pipeline, farm, divide and conquer, and their composition, as well as generic streaming networks. It is specifically designed to support the development and the seamless porting of existing applications on multi-core. The layered template-based C++ design ensures flexibility and extendibility. Its lock-free/fence-free run-time support minimizes cache invalidation traffic and enforces the development of high-performance (high-throughput, low-latency) scalable applications. It has been proven faster than TBB, OpenMP, and Cilk on several micro-benchmarcks and real-world applications, especially when dealing with fine-grained parallelism and high-throughput applications.
Updated 30 Apr 2013
GNU Parallel
| Pop | 546.22 |
|---|---|
| Vit | 53.58 |
GNU parallel is a shell tool for executing jobs in parallel locally or using remote computers. A job is typically a single command or a small script that has to be run for each of the lines in the input. The typical input is a list of files, a list of hosts, a list of users, a list of URLs, or a list of tables. If you use xargs today you will find GNU parallel very easy to use, as GNU parallel is written to have the same options as xargs. If you write loops in shell, you will find GNU parallel may be able to replace most of the loops and make them run faster by running several jobs in parallel. If you use ppss or pexec you will find GNU parallel will often make the command easier to read. GNU parallel makes sure output from the commands is the same output as you would get had you run the commands sequentially. This makes it possible to use output from GNU parallel as input for other programs.
Updated 26 Apr 2013
JPPF
| Pop | 873.85 |
|---|---|
| Vit | 88.98 |
JPPF makes it easy to parallelize computationally intensive tasks and execute them on a Grid.
