GNU Parallel is a tool for executing shell jobs in parallel using one or more computers. Jobs can consist of single commands or of scripts and they are executed on lists of files, hosts, users or other items.

This package provides a library for parallel programming.

GNU Parallel is a tool for executing shell jobs in parallel using one or more computers. Jobs can consist of single commands or of scripts and they are executed on lists of files, hosts, users or other items.

This package provides utility functions that enhance the parallel package and support the built-in parallel backends of the future package. For example, availableCores gives the number of CPU cores available to your R process as given by R options and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores. Another example is makeClusterPSOCK, which is backward compatible with parallel::makePSOCKcluster while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.

Xyce is a SPICE-compatible, high-performance analog circuit simulator, capable of solving extremely large circuit problems by supporting large-scale parallel computing platforms. It also supports serial execution.

Parallel allows you to run any code in parallel Processes (to use all CPUs) or Threads(to speedup blocking operations). It is best suited for map-reduce or e.g. parallel downloads/uploads.

This package provides a unified parallelization framework for multiple backends. This package is designed for internal package and interactive usage. The main operation is parallel mapping over lists. It supports local, multicore, mpi and BatchJobs mode. It allows tagging of the parallel operation with a level name that can be later selected by the user to switch on parallel execution for exactly this operation.

This package provides a fast parallelized alternative to R's native dist function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices, which supports a broad variety of predefined distance functions from other R packages, as well as user- defined functions written in C++. For ease of use, the parDist function extends the signature of the dist function and uses the same parameter naming conventions as distance methods of existing R packages.

Parallel Python module (PP) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. PP module features cross-platform portability and dynamic load balancing. Thus applications written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other applications with variable CPU loads).

This package provides a parallel environment which allows two potentially different texts to be typeset in two columns, while maintaining alignment. The two columns may be on the same page, or on facing pages. This arrangement of text is commonly used when typesetting translations, but it can have value when comparing any two texts.

Run an array of functions in parallel

Parallel Python module (PP) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. PP module features cross-platform portability and dynamic load balancing. Thus applications written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other applications with variable CPU loads).

This crate provides a simple primitive for spawning threads in bulk and waiting for them to complete. Threads are allowed to borrow local variables from the main thread.

This package defines classes of monads that can perform multiple executions in parallel and combine their results. For any monad that's an instance of the class, the package re-implements a subset of the Control.Monad interface, but with parallel execution.

This package can speed up Test::Unit, RSpec, Cucumber, and Spinach tests by running them concurrently across multiple CPU cores.

The doctest program checks examples in source code comments. It is modeled after doctest for Python (<https://docs.python.org/3/library/doctest.html>). . Documentation is at <https://github.com/martijnbastiaan/doctest-parallel#readme>.

HDF5 is a suite that makes possible the management of extremely large and complex data collections.

NetCDF is an interface for scientific data access and a software library that provides an implementation of the interface. The netCDF library defines a machine-independent format for representing scientific data. Together, the interface, library, and format support the creation, access, and sharing of scientific data.

This is a simple Common Lisp library to evaluate some forms in parallel.

This is a simple Common Lisp library to evaluate some forms in parallel.

Parallel::ForkManager is intended for use in operations that can be done in parallel where the number of processes to be forked off should be limited.

This is a simple Common Lisp library to evaluate some forms in parallel.

PDI supports loose coupling of simulation codes with data handling the simulation code is annotated in a library-agnostic way, libraries are used from the specification tree.