Search Results for "parallel computing"
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Showing 28 open source projects for "parallel computing"
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NESSIE is a modern first-principle calculation software that can adequately address the need for ever-higher levels of numerical accuracy and high-performance in large-scale electronic structure simulations, as well as pioneer the fundamental study of quantum many-body effects in a large number of emerging nanomaterials. NESSIE is an electronic structure code that uses a real-space FEM discretization and domain-decomposition (DD) to perform all-electron ground-state DFT and real-time...
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PMCGPU
Parallel simulators for Membrane Computing on the GPU
Membrane Computing is a new research area (within Natural Computing) that aims to provide computing devices abstracted from the functioning and structure of living cells. These devices are called P systems. The objective of this project (PMCGPU) is to bring together all the researchers working on the development of parallel simulators for P systems, specially those using the GPU (e.g. -
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SAT-Assembler
Scalable and accurate targeted gene assembly for large-scale NGS data
...It recovers genes from gene families of particular interest to biologists with high coverage, low chimera rate, and extremely low memory usage compared with exiting gene assembly tools. Moreover, it is naturally compatible with parallel computing platforms. -
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myopentrader
Open-source complex-event based trading engine
...We have started MOT in 2012 and we have been using MOT ever since successfully. The best way of describing MOT is a flexible open-source trading framework. It is built from ground up as a parallel computing engine, which allows to do large scale parallel backtesting. MOT is distributed under the GPL v3 License and is free to use for everyone without warranty. MyOpenTrader.org is not responsible for any of your trading losses and can not be held accountable. Use the framework at your OWN risk. MOT is written purely in Java (1.6++) and has dependencies on a DB as well as a MQ Broker (such as ActiveMQ). -
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BriCS
SaaS for running simulation models in the cloud
The Bristol Cloud Service simulation runner is a cloud computing Software as a Service designed to enable users to quickly launch simulation code on Amazon AWS' Elastic Compute Cloud (EC2). BRiCS is written in Python using the Django framework and interacts with EC2 using the boto API. BriCS enables multiple simulation runs to be launched in parallel from a web browser. Model configuration (parameters) files are uploaded via the browser and results files are downloadable on completion. ... -
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MapPSO is a tool for Ontology Alignment, which uses Discrete Particle Swarm Optimisation. A particle swarm is used to search for the optimal alignment. The algorithm is massively parallel and adapts naturally on parallel architectures.
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ViennaX
A flexible plugin execution framework for scientific computing in C++
Welcome to the project homepage of ViennaX, a high-performance scientific computing C++ framework for executing plugins. A plugin system allows to wrap already available functionality into reusable components, which can have data input and output dependencies via a socket system. Different scheduler kernels are available, supporting serial and MPI-based distributed executions, supporting task and data parallelism. -
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Tuple Spaces
Tuple space with time outs and transactions.
Java implementation of a Tuplespace. Moved to https://github.com/mike-k-houghton/tuplespace A Tuple is an ordered list of items. A Tuple Space is a form of associative memory where entries, tuples, are stored in the space and are retrieved using search criteria that are based on the intrinsic properties of the tuples. The two key operations are put get And that is it! There are refinements on these operations including, for example, timeouts where the tuple will only exist... -
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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
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The most advanced C and C++ source code analyzerTrustInSoft Analyzer is a C and C++ source code analyzer powered by formal methods, mathematical & logical reasonings that allow for exhaustive analysis of source code. This analysis can be run without false positives or false negatives, so that every real bug in the code is found. Developers receive several benefits: a user-friendly graphical interface that directs developers to the root cause of bugs, and instant utility to expand the coverage of their existing tests. Unlike traditional source code analysis tools, TrustInSoft’s solution is not only the most comprehensive approach on the market but is also progressive, instantly deployable by developers, even if they lack experience with formal methods, from exhaustive analysis up to a functional proof that the software developed meets specifications.
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Microbase is a Grid infrastructure for deploying massively parallel applications. Our notification-driven task scheduler supports the enactment of complex data processing workflows, such as those commonly used in bioinformatics.
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SimKernel controls parameter scans and iterations for any given C++ code / scientific simulation. Parameters, iteration ranges, settings and dependencies are specified in Mathematica syntax. Parallel processing of parameter scanning via MPI.
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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
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BalanceParallel is a software project for parallel computing,compound it by one center control server and more parallel computing server.now it work on windows by completion port.
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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.
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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.
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The Application Agent (AA) is a software providing the platform and mechanism for the distributed/parallel applications to interact and negotiate with the underlying grid resource infrastructure such as Condor and SGE on one-to-one and dynamic basis.
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Python Integrated Parallel Programming EnviRonment (PIPPER), Python pre-parser that is designed to manage a pipeline, written in Python. It enables automated parallelization of loops. Think of it like OpenMP for Python, but it works in a computer cluster
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A data parallel scientific programming model. Compiles efficiently to different platforms like distributed memory (MPI), shared memory multi-processor (pthreads), Cell BE processor, Nvidia Cuda, SIMD vectorization (SSE, Altivec), and sequential C++ code.
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It is an implementation of Tuple-Space by using MPI library. Tuple-space is a logically shared memory in many parallel computing. It provides the appearance of a shared memory but do not require on underlying physical shared memory.
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This project aims to modify Parallel Virtual Machine (PVM) to use benefits of RDMA-capable network interconnects, commonly used in HPC clusters.
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The project is a data-parallel C++ library to teach parallel programming concepts. We intend to make this library portable, small, and easy to extend to work with real parallel message passing systems such as MPI and PVM. Darrell Raymond Ulm
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CPPScaLapack is a C++ class wrapper for PBLAS and ScaLAPACK with MPI. CPPScaLapack provides a user-friendly interface of high-speed parallel matrix calculation with BLAS and LAPACK techonologies for programers concerning with large-scale computing.
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Web Textual eXtraction Tools C++ Parallel web crawler, noun phrase idenification, Multi-lingual Part of Speech Tagging, Tarjan's Algorithm, Co-RelationShip Mappings...
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Designed to allow quick setup of powerful NOW-based clusters to run compute-intensive applications. Applications do not have to be designed specially for these clusters to benefit.
