Search Results for "parallel computing datamaning"

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.

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. ...

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.

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). ...

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/

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.

...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.

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.

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.

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...

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.

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.

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.

LIME (Less-is-More) is parallel/concurrent programming environment based on C. Internally, it uses XML technology to describe tasks and their dependencies. Externally, it offers the ANSI C99 programming as well as a set of visually-oriented interfaces.

The CodeTime platform covers every aspect of parallel software from authoring, through distribution, to run-time. Its goals are: high programmer productivity; write once, run high performance anywhere; and wide acceptance.

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.

This project provides a distributed testing environment that extends JUnit. Giving the developer/tester an ability to run remote JUnits and create complex test suites that are composed of several remote JUnit tests running in parallel or serially.

The software provides a means to form a simple Java cluster. It can be also viewed as a parallel computing library in Java. The project is based on the following <a href='http://www-106.ibm.com/developerworks/java/library/j-super.html'>article</a>

This project is setup to maintain a set of Javaparty codes. This includes Javaparty benchmarks and applications. Javaparty (http://www.ipd.uka.de/JavaParty/) is a Java extension for easy and efficient parallel programming. It does so by extending the

RAFT-Net is a Fault Tolerant Parallel Distributed Framework. Using a Master-Worker scheme, the framework transparently distributes workunits to workers (drones). These workers can join and leave and as such, the network is very flexible. A modified