12 projects tagged "Parallel Computing"
Updated 26 Apr 2013
JPPF
| Pop | 865.44 |
|---|---|
| Vit | 87.04 |
JPPF makes it easy to parallelize computationally intensive tasks and execute them on a Grid.
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 10 Oct 2012
POP-C++
| Pop | 56.78 |
|---|---|
| Vit | 2.03 |
POP-C++ is a comprehensive object-oriented system for developing applications in large distributed computing infrastructures such as Grid, P2P or Clouds. It consists of a programming suite (language, compiler) and a run-time system for running POP-C++ applications. The POP-C++ language is a minimal extension of C++ that implements the parallel object model with the integration of resource requirements into distributed objects. This extension is as close as possible to standard C++ so that programmers can easily learn POP-C++ and so that existing C++ libraries can be parallelized using POP-C++ without too much effort. The POP-C++ run-time is an object-oriented open design that aims at integrating different distributed computing tool kits into an infrastructure for executing requirement-driven object-oriented applications. It uses objects to serve objects: the system provides services for executing remote objects.
Updated 25 Sep 2012
Strategico
| Pop | 186.17 |
|---|---|
| Vit | 6.11 |
Strategico is an engine for running statistical analysis over groups of time series. It can manage one or more groups (projects) of time series: by default, you can get data from a database or CSV files, normalize them, and then save them inside the engine. The first statistical analysis implemented inside Strategico is the "Long Term Prediction": it automatically finds the best model that fits each time series. Some of the models implemented are mean, trend, linear, exponential smoothing, and Arima. Strategico is scalable: the statistical analysis over each time series (of a project) can be run separately and independently. It is suggested that you set up an HPC Cluster (High Performance Computing) and/or use a resource scheduler like slurm. It is developed with R, one of the most famous statistical languages.
Updated 28 May 2011
YvetteML
| Pop | 22.29 |
|---|---|
| Vit | 26.86 |
YML is a research project that aims to provide tools for using global computing middleware such as GRID, peer to peer, and metacomputing environments. The YML software architecture enables the definition of parallel applications, independently of the underlying middleware used. Parallel applications are defined using a workflow language called YvetteML.
Updated 09 May 2013
HPCC Systems
| Pop | 373.55 |
|---|---|
| Vit | 40.39 |
HPCC (High Performance Computing Cluster) stores and processes large quantities of data, processing billions of records per second using massive parallel processing technology. Large amounts of data across disparate data sources can be accessed, analyzed, and manipulated in fractions of seconds. HPCC functions as both a processing and a distributed data storage environment capable of analyzing terabytes of information.
Updated 30 Jul 2012
ScalaBLAST
| Pop | 42.64 |
|---|---|
| Vit | 2.32 |
ScalaBLAST is a high-performance multiprocessor implementation of the NCBI BLAST library. It supports all 5 primary program types (blastn, blastp, tblastn, tblastx, and blastx) and several output formats (pairwise, tabular, and XML). It will run on most multiprocessor systems which have MPI installed, and can run over a wide variety of interconnects, including infiniband, quadrics, and ethernet. It is designed to run a large number of queries against either large or small databases. It parallelizes the BLAST calculations by dynamically scheduling them across processors using a fault-resilient scheme.
Updated 14 May 2013
VCL
| Pop | 246.49 |
|---|---|
| Vit | 30.81 |
The VirtualCL (VCL) cluster platform is a wrapper for OpenCL that allows most unmodified applications to transparently utilize many OpenCL devices in a cluster as if all the devices are on the local computer.
Updated 23 Jul 2012
BitDew
| Pop | 82.28 |
|---|---|
| Vit | 1.53 |
BitDew is a programmable environment for the management and distribution of data for grid, desktop grid, and cloud systems. It can easily be integrated into large scale computational systems such as XtremWeb, BOINC, Hadoop, Condor, Glite, Unicore, OpenStack, and Eucalyptus. It provides key P2P, grid, and cloud technologies (DHT, BitTorrent, Amazon S3, DropBox) and high level programming interfaces with a simple API for creating, accessing, storing, and moving data with ease, even in highly dynamic and volatile environments.
Updated 25 Oct 2012
dispy
| Pop | 77.50 |
|---|---|
| Vit | 2.48 |
dispy is a Python framework for parallel execution of computations by distributing them across multiple processors in a single machine (SMP), or among many machines in a cluster or grid. The computations can be standalone programs or Python functions. dispy is well suited for the data parallel (SIMD) paradigm where a computation is evaluated with different (large) datasets independently (similar to Hadoop, MapReduce, Parallel Python). dispy features include automatic distribution of dependencies (files, Python functions, classes, modules), client-side and server-side fault recovery, scheduling of computations to specific nodes, encryption for security, sharing of computation resources if desired, and more.
