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<PackageId>Tensor</PackageId>

<title>F# Tensor (multidimensional array) library with SIMD and GPU acceleration</title>

<summary>Provides n-dimensional arrays (tensors) in host memory or on CUDA GPUs. Support many operations from NumPy.</summary>

<description>

Tensor (n-dimensional array) library for F#

Core features:

- n-dimensional arrays (tensors) in host memory or on CUDA GPUs

- element-wise operations (addition, multiplication, absolute value, etc.)

- basic linear algebra operations (dot product, SVD decomposition, matrix inverse, etc.)

- reduction operations (sum, product, average, maximum, arg max, etc.)

- logic operations (comparision, and, or, etc.)

- views, slicing, reshaping, broadcasting (similar to NumPy)

- scatter and gather by indices

- standard functional operations (map, fold, etc.)

Data exchange:

- read/write support for HDF5 (.h5)

- interop with standard F# types (Seq, List, Array, Array2D, Array3D, etc.)

Performance:

- host: SIMD and BLAS accelerated operations

- by default Intel MKL is used (shipped with NuGet package)

- other BLASes (OpenBLAS, vendor-specific) can be selected by configuration option

- CUDA GPU: all operations performed locally on GPU and cuBLAS used for matrix operations

Requirements:

- Linux, MacOS or Windows on x64

- Linux requires libgomp.so.1 installed.

Additional algorithms are provided in the Tensor.Algorithm package.

</description>

<PackageTags>Tensor ndarray array MKL SIMD BLAS CUDA CUBLAS HDF5</PackageTags>

<PackageProjectUrl>http://www.deepml.net/Tensor</PackageProjectUrl>

<Platforms>x64</Platforms>

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<Compile Include="ScalarPrimitives.fs" />

<Compile Include="TensorRng.fs" />

<Compile Include="TensorLayout.fs" />

<Compile Include="TensorBackend.fs" />

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<Compile Include="Tensor.fs" />

<Compile Include="TensorVal.fs" />

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