Search Results for "cpu"

...It was designed to make writing high-performance image and array processing code much easier on modern machines. It works on all major operating systems and with several CPU architectures (X86, ARM, MIPS, Hexagon, PowerPC) and GPU Compute APIs (CUDA, OpenCL, OpenGL, among others). It isn't a standalone programming language however; rather it is embedded in C++ which means that you write C++ code, building an in-memory representation of a Halide pipeline using Halide's C++ API. This representation can then be compiled to an object file, or a JIT-compile and run in the same process. ...

...The design prioritizes predictable performance: decoders avoid dynamic allocation by default, return explicit “short read” signals, and run well in streaming or sandbox-free environments. The project ships battle-tested, CPU-friendly implementations for common image/container formats and bit-twiddly primitives like Huffman and checksum routines. Its standard library emphasizes portability and constant-time behavior where appropriate, which is valuable in security-sensitive contexts. Because the language is purpose-built, the resulting C output is small, auditable, and easy to embed in larger systems.

...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. ...

...Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard. Expectation-Maximization, pseudo-marginal and ABC methods, and message passing algorithms.