Search Results for "parallel computing datamaning"
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45 projects for "parallel computing datamaning" with 1 filter applied:
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PyOpenCL
OpenCL integration for Python, plus shiny features
PyOpenCL is a Python wrapper for the OpenCL framework, providing seamless access to parallel computing on CPUs, GPUs, and other accelerators. It enables developers to harness the full power of heterogeneous computing directly from Python, combining Python’s ease of use with the performance benefits of OpenCL. PyOpenCL also includes convenient features for managing memory, compiling kernels, and interfacing with NumPy, making it a preferred choice in scientific computing, data analysis, and machine learning workflows that demand acceleration. -
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LWJGL
Java library that enables cross-platform access to popular native APIs
LWJGL is a Java library that enables cross-platform access to popular native APIs useful in the development of graphics (OpenGL, Vulkan), audio (OpenAL) and parallel computing (OpenCL) applications. This access is direct and high-performance, yet also wrapped in a type-safe and user-friendly layer, appropriate for the Java ecosystem. LWJGL is an enabling technology and provides low-level access. It is not a framework and does not provide higher-level utilities than what the native libraries expose. ... -
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OpenFOAM
The Open Source CFD Toolbox
OpenFOAM - The Open Source CFD Toolbox. OpenFOAM is a generic, programmable software tool for Computational Fluid Dynamics (CFD). For more information, see: - Main Website: https://openfoam.org - C++ Source Documentation: https://cpp.openfoam.org - Issue (Bug) Tracking: https://bugs.openfoam.org - User Guide: https://cfd.direct/openfoam/user-guide -
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dispy
Distributed and Parallel Computing with/for Python.
dispy is a generic and comprehensive, yet easy to use framework for creating and using compute clusters to execute computations in parallel across multiple processors in a single machine (SMP), among many machines in a cluster, grid or cloud. dispy is well suited for data parallel (SIMD) paradigm where a computation (Python function or standalone program) is evaluated with different (large) datasets independently. dispy supports public / private / hybrid cloud computing, fog / edge computing. -
Run Any Workload on Compute Engine VMsCompute Engine delivers high-performance virtual machines for web apps, databases, containers, and AI workloads. Choose from general-purpose, compute-optimized, or GPU/TPU-accelerated machine types—or build custom VMs to match your exact specs. With live migration and automatic failover, your workloads stay online. New customers get $300 in free credits.
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Avian Parallel Computing
Develop parallel programs. Try various thread configs. GUI front-end.
Avian Computing seeks to efficiently create parallel programs by changing how we think about parallel programs. Avian Computing discourages thinking about lines of code and encourages us to use a new model: flocks of birds. Changing the model to flocks of birds makes it easier to think about the actions that we want to perform concurrently, which leads to simpler and quicker development of working parallel programs. -
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Incanter
Clojure-based, R-like statistical computing and graphics environment
Incanter is a Clojure-based, R-like statistical computing and visualization library running on the JVM. It integrates core numerical libraries like Parallel Colt and JFreeChart to deliver data manipulation, modeling, statistical tests, and charting in a REPL-friendly environment. Start by visiting the Incanter website for an overview, check out the documentation page for a listing of HOW-TOs and examples, and then download either an Incanter executable or a pre-built version of the latest build of Incanter, which includes all the necessary dependencies, and unpack the file (if you would like to build it from source, read Building Incanter). ... -
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A framework to run MATLAB programs as batch jobs. Features a structured input description, integrity constraints and GUI.Independent parts of a job can execute in parallel on a cluster computer. Developed at Freiburg Brain Imaging (FBI) - http://fbi.uniklinik-freiburg.de/
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PyCNN
Image Processing with Cellular Neural Networks in Python
Image Processing with Cellular Neural Networks in Python. Cellular Neural Networks (CNN) are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors. -
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Advanced Simulation Library
Free multiphysics simulation software package
...Its computational engine is based, among others, on the Lattice Boltzmann Methods (http://en.wikipedia.org/wiki/Lattice_Boltzmann_methods) and is written in OpenCL (http://en.wikipedia.org/wiki/OpenCL) which enable extraordinarily efficient deployment (http://asl.org.il/benchmarks) on a variety of massively parallel architectures, ranging from inexpensive FPGAs, DSPs and GPUs up to heterogeneous clusters and supercomputers. The engine is hidden entirely behind C++ classes, so that no OpenCL knowledge is required from application programmers. ASL can be utilized to model various coupled physical and chemical phenomena and employed in a multitude of fields: computational fluid dynamics, virtual sensing, industrial process data validation and reconciliation, image-guided surgery, computer-aided engineering, high-performance scientific computing, etc.. -
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Bat2015
Bachelor of Science (Informatik)
...With a focus on the MILP methods we implement a load balancing and speed up the solving process in a multiplicative way. Sometimes we have super-linear speedup with a small set of hardware. With a splitting of problems, parallel computing and distributing the actual best solution to all running processes we solve CBP much faster than a sequential processing can do. -
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Genetic Programming in OpenCL is a parallel implementation of genetic programming targeted at heterogeneous devices, such as CPU and GPU. It is written in OpenCL, an open standard for portable parallel programming across many computing platforms.
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LARA
Lightweight Architecture for boundedly Rational Agents
For the purpose of policy simulation in coupled social-ecological systems (e.g. energy supply), a credible modelling of actors – especially citizens – and their decision processes is needed. This requires a framework capable of handling high numbers of heterogeneous agents (several hundreds of thousands). LARA (Lightweight Architecture for boundedly Rational Agents) meets these requirements and fills the gap between frameworks without built-in psychological foundations and full-fledged... -
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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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AWK~plus is the next generation script practice environment. The AWK Language specifications and a main extension of GNU GAWK. Combination of Dynamic and Static typing. Parallel computing that a lock is free, and is thread safe at a language level.
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SPJS is a simple skeleton for creating job programs which can be executed on different CPU cores or can be submitted to a batch system or any other parallel computer farm.
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Simuquant
A quantum circuit simulator written in Scala
Simuquant is made to construct and simulate universal quantum circuits. It's GUI enables intuitive access and direct graphical feedback, making it useful in a classroom situation and the like. Simulation processing is done in parallel, utilizing parallel collections introduced in scala 2.9. This application has been created as part a bachelor-thesis at the Hamburg University of Applied Sciences. The thesis is available... -
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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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Platform for parallel computation in the Amazon cloud, including machine learning ensembles written in R for computational biology and other areas of scientific research. Home to MR-Tandem, a hadoop-enabled fork of X!Tandem peptide search engine.
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Equalizer is the standard middleware to create parallel OpenGL-based applications. Please visit https://github.com/Eyescale for current development information.
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Parallel Colt is a multithreaded version of Colt - a library for high performance scientific computing in Java. It contains efficient algorithms for data analysis, linear algebra, multi-dimensional arrays, Fourier transforms, statistics and histogramming
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Cca-forum unifies the Common Component Architecture tools and tutorial. It includes the CCA specifications, the Ccaffeine framework for HPC, and related tools. These support multilanguage scientific and parallel computing.
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GridSim allows modeling and simulation of entities in parallel and distributed computing systems such as users, applications, resources, and resource brokers/schedulers for design and evaluation of scheduling algorithms. http://www.gridbus.org/gridsim
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RT-BOINC stands for a Real-Time BOINC. It was designed for managing highly-interactive, short-term, and massively-parallel real-time applications. We implemented RT-BOINC on top of the recent BOINC server source codes.
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Tsunami is an open-source high performance computing language. With it you can write streaming data-parallel algorithms that utilize GPGPUs for orders-of-magnitude speed-up with the ease of writing sequential algorithms.
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PyMW is a Python module for parallel master-worker computing in a variety of environments. With the PyMW module, users can write a single program that scales from multicore machines to global computing platforms.
