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Besides its obvious scientific uses, NumJs can also be used as an efficient multi-dimensional container of generic data. NumJs is licensed under the MIT license, enabling reuse with almost no restrictions.

linear-algebra ndarray nodejs array multi multidimensional dimension higher image volume webgl tensor matrix linear algebra science numerical computing stride shape numpyModular multidimensional arrays for JavaScript.ndarrays can be transposed, flipped, sheared and sliced in constant time per operation. They are useful for representing images, audio, volume graphics, matrices, strings and much more. They work both in node.js and with browserify.

ndarray array multi multidimensional dimension higher image volume webgl tensor matrix linear algebra science numerical computing stride shapeSciRust is a Scientific computing library written in Rust programming language. The objective is to design a generic library which can be used as a backbone for scientific computing. Its current areas of focus includes Matrices, Linear algebra, Statistics, and Signal processing.

scientific computing algebra matrixThe Dambach Multi-Core Library makes it easy to create .Net programs that run faster on multi-core machines than their traditionally programmed counterparts.

computing concurrency core dambach efficiency multicore parallelFor creating distributed jobs using AWS Lambda functions. Publishes or updates specified code as a Lambda function. This method is not required if the function has been previously published and does not need to updated.

distributed computing lambda awsThis project aims to provide an interface for parallel computation using arrows. Currently, all Haskell modules in this repository are on hackage in version 0.1.1.0. They are named Parallel-Arrows-<NameOfSubmodule>.

haskell arrows parallel computing arrowThis module solves sparse symmetric positive definite linear systems, by finding the Cholesky decomposition(the LDL^T decomposition, and not the LL^T decomposition), and then doing forward substitution and backward substitution. It is basically a Javascript port of the paper "Algorithm 8xx: a concise sparse Cholesky factorization package". This kind of solver has many applications in digital geometry processing.Decomposes M into the Cholesky decomposition of the form LDL^T. A function is returned that can be used to solve the equation Mx = b, for some given value of b.

numerical computing sparse matrix solver linear system choleskyThis library can be used to generate cache efficient map/reduce operations for ndarrays.Note that in the above, i is not an actual Array, the indexing notation is just syntactic sugar.

scijs ndarray component scientific computing volume image array typed scalar math linear algebra signal operation scan map reduce cache arithmeticReturn a compute instance with all the methods from batchdb plus the extra ones documented here.The jobs will be spawned in opts.shell, which defaults to the $SHELL environment variable or 'cmd' on windows and 'sh' everywhere else.

batch compute queue jobs computingSource code for NuPIC website.

nupic numenta platform intelligent computing machine intelligence hierarchical temporal memory htmNormally Furious.js would automatically detect the optimal backend, but it is possible to specify it manually. If you plan to use Node-WebCL, you'll need to install the upstream version of Node-WebCL, and its dependencies.

ndarray array matrix tensor hpc computing blas science scientific numeric math mathematics statisticsThis is an early implementation of Node.JS bindings to OpenCL supporting all features of OpenCL up to the latests specification available on Khronos.org. This implementation is different from node-webcl in the sense that it is close to OpenCL C host methods. A WebCL object model would be available later by simply wrapping the low level methods of node-opencl.

opencl gpu high-performance compute computingModern multicore systems can process lots of data in parallel but writing parallel code can be tricky. Pararr.js provides an easy-to-use API for parallel computing in Node and parallel implementations of standard array functions like map or filter that utilize all available cores in the system when calculating their result. ##Considerations Pararr creates a V8 instance for each CPU core which has an effect memory consumption and startup time. When a calculation is dispatched to a worker the function and its data is copied and sent to the corresponding instance which causes an shorter or longer delay depending mainly on the data volume. Generally speaking we can benefit from parallelization in this form when data volumes are small and CPU cycles is a bottleneck.

parallel computing multi-core functional array functions
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