Search Results for "parallel"

Futhark is a small programming language designed to be compiled into efficient parallel code. It is a statically typed, data-parallel, and purely functional array language in the ML family, and comes with a heavily optimizing ahead-of-time compiler that presently generates either GPU code via CUDA and OpenCL, or multi-threaded CPU code. Futhark is not designed for graphics programming, but can instead use the compute power of the GPU to accelerate data-parallel array computations. ...

...These computations are online-compiled and executed on a range of architectures. Accelerate is a free, general-purpose, open-source library that simplifies the process of developing software that targets massively parallel architectures including multicore CPUs and GPUs. Embedded in the advanced functional programming language Haskell, Accelerate programs are declarative, statically-typed, pure, functional, and ready to exploit all of the performance of modern parallel hardware. The combination of a strong type system, high-level code, and interactive development environment, allows you to develop code quickly with the confidence that it is correct.

Idris‑dev is the development version of Idris 1, a general-purpose functional programming language featuring full dependent types, designed for writing type-safe programs and proofs within the language itself. It compiles to C and JavaScript (for Node.js and browsers), and supports code generation via substitute backends. This repository represents the latest development version of the language, and may contain bugs that are being actively worked on. For those who wish to use a more stable...

Discover a huge fictional game world and supply resources for its exploration. Unleash mysterious secrets or just race through the hills. This software is under development and thus neither complete nor bug-free. However, fun can occasionally be had.

HLearn is a Haskell-based machine learning library focused on composability, algebraic structure, and performance. It provides a functional approach to building machine learning algorithms by leveraging algebraic properties such as monoids and groups. This allows for parallel, incremental, and distributed computation in a mathematically consistent way. HLearn aims to provide implementations of common algorithms like k-means, naive Bayes, and others while maintaining the expressiveness and safety of the Haskell language.