15 projects tagged "Parallel Computing"

Download Website Updated 15 May 2014 JPPF

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Pop 1,060.32
Vit 130.04

JPPF makes it easy to parallelize computationally intensive tasks and execute them on a Grid.

No download No website Updated 01 May 2014 HPCC Systems

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Pop 412.87
Vit 35.52

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.

Download Website Updated 03 Apr 2014 FastFlow

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Pop 328.80
Vit 13.24

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, GPGPUs, and clusters of them. 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 comparable or 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.

Download No website Updated 25 Sep 2012 Strategico

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Pop 156.69
Vit 4.52

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.

No download Website Updated 23 May 2014 VCL

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Pop 140.84
Vit 4.59

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 local.

No download Website Updated 08 Nov 2013 Son of Grid Engine

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Pop 119.38
Vit 8.59

Son of Grid Engine is a highly-scalable and versatile distributed resource manager for scheduling batch or interactive jobs on clusters or desktop farms. It is a community project to continue Sun's Grid Engine. It is competitive against proprietary systems and provides better scheduling features and scalability than other free DRMs like Torque, SLURM, Condor, and Lava.

No download Website Updated 07 Mar 2014 librsb

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Pop 105.85
Vit 5.20

librsb is a library for sparse matrix computations featuring the Recursive Sparse Blocks (RSB) matrix format. This format allows cache-efficient and multithreaded (that is, shared memory parallel) operations on large sparse matrices. The most common operations necessary to iterative solvers are available (matrix-vector multiplication, triangular solution, rows/columns scaling, diagonal extraction/setting, blocks extraction, norm computation, formats conversion). The RSB format is especially well-suited for symmetric and transposed multiplication variants. On these variants, librsb has been found to be faster than Intel MKL's implementation for CSR. Most numerical kernels code is auto-generated, and the supported numerical types can be chosen by the user at buildtime. librsb implements the Sparse BLAS standard, as specified in the BLAS Forum documents.

Download No website Updated 29 Jan 2014 Portable Computing Language (pocl)

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Pop 96.75
Vit 4.66

Portable Computing Language (pocl) aims to become an efficient implementation of the OpenCL standard. In addition to producing an easily-portable Open Source implementation, another major goal of the project is improving performance portability of OpenCL programs with compiler optimizations, reducing the need for target-dependent manual optimizations. At the core of pocl is a set of LLVM passes used to statically parallelize multiple work items with the kernel compiler, even in the presence of work group barriers. This enables parallelization of the fine-grained static concurrency in the work groups in multiple ways (SIMD, VLIW, superscalar, etc.). The code base is modularized to allow easy adding of new "device drivers" in the host-device layer. A generic multithreaded "target driver" is included. It allows running OpenCL applications on a host which supports the pthread library with multithreading at the work group granularity.

Download No website Updated 23 Jul 2012 BitDew

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Pop 69.39
Vit 1.47

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.

No download Website Updated 25 Oct 2012 dispy

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Pop 65.83
Vit 2.18

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.

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