Search Results for "computations"

...Each of these is composable and avoid the addition of “special” extensions by being as general as possible. Koka tracks the (side) effects of every function in its type, where pure and effectful computations are distinguished. The precise effect typing gives Koka rock-solid semantics backed by well-studied category theory, which makes Koka particularly easy to reason about for both humans and compilers. Effect handlers let you define advanced control abstractions, like exceptions, async/await, or probabilistic programs, as a user library in a typed and composable way. ...

...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. The language supports regular nested data-parallelism, as well as a form of imperative-style in-place modification of arrays, while still preserving the purity of the language via the use of a uniqueness type system. While the Futhark language and compiler is an ongoing research project, it is quite usable for real programming and can compile nontrivial programs which then run on real machines at high speed.

Data.Array.Accelerate defines an embedded language of array computations for high-performance computing in Haskell. Computations on multi-dimensional, regular arrays are expressed in the form of parameterized collective operations (such as maps, reductions, and permutations). 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. ...

Collection of scripts for transfinite ordinals computations

The tensorflow-haskell package provides Haskell-language bindings for TensorFlow, giving Haskell developers the ability to build and run computation graphs, machine learning models, and leverage TensorFlow's ecosystem—though it is not an official Google release. As an expedient we use docker for building. Once you have docker working, the following commands will compile and run the tests. Run the install_macos_dependencies.sh script in the tools/ directory. The script installs dependencies...

...Haxl can automatically batch multiple requests to the same data source, request data from multiple data sources concurrently, cache previous requests, and memoize computations. Having all this handled for you behind the scenes means that your data-fetching code can be much cleaner and clearer than it would otherwise be if it had to worry about optimizing data-fetching. To use Haxl in your own application, you will likely need to build one or more data sources: the thin layer between Haxl and the data that you want to fetch, be it a database, a web API, a cloud service, or whatever. ...