ArrayFire | A Tensor Library for Fast GPU Computing
"Used by 10,000s of developers, ArrayFire is easy-to-use and blazingly fast." ArrayFire supports hundreds of accelerated tensor computing functions, in the following areas: Array handling Computer vision Image processing Linear algebra Machine learning Standard math Signal Processing Statistics Vector algorithms Data structures in ArrayFire are smartly managed to avoid costly memory transfers and
MAGMA
Overview Matrix Algebra on GPU and Multi-core Architectures (MAGMA) is a collection of next-generation linear algebra libraries for heterogeneous computing. The MAGMA package supports interfaces for current linear algebra packages and standards (e.g., LAPACK and BLAS) to enable computational scientists to easily port any linear algebra–reliant software components to heterogeneous computing systems
NVIDIA cuDNN | NVIDIA Developer
Accelerated Learning cuDNN provides kernels, targeting Tensor Cores, to deliver best available performance on compute-bound operations. It offers heuristics for choosing the right kernel for a given problem size. Expressive Op Graph API The user defines computations as a graph of operations on tensors. The cuDNN library has both a direct C API and an open-source C++ frontend for convenience. Most
ViennaCL - Linear Algebra Library using CUDA, OpenCL, and OpenMP
ViennaCL is a free open-source linear algebra library for computations on many-core architectures (GPUs, MIC) and multi-core CPUs. The library is written in C++ and supports CUDA, OpenCL, and OpenMP (including switches at runtime). The highlights of the latest 1.7.x release family are: Fast sparse matrix-matrix multiplications, outperforming CUBLAS and MKL.Fine-grained parallel algebraic multigrid
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