libarm-compute32t64

Arm Compute Library

Arm Compute Library is a software library for computer vision and machine learning. It is a collection of low-level functions optimized for Arm CPU and GPU architectures targeted at image processing, computer vision, and machine learning. Arm Compute Library is usable as a shared or static library.

libarmnn33t64

Inference engine for CPUs, GPUs and NPUs - shared library

Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions.

python3-opt-einsum

Optimized Einsum is a tensor contraction order optimizer

Optimized einsum can significantly reduce the overall execution time of einsum-like expressions by optimizing the expression's contraction order and dispatching many operations to canonical BLAS, cuBLAS, or other specialized routines.

python3-pyspnego

Windows Negotiate Authentication Client and Server

Library to handle SPNEGO (Negotiate, NTLM, Kerberos) and CredSSP authentication. Also includes a packet parser that can be used to decode raw NTLM/SPNEGO/Kerberos tokens into a human readable format.

libatrilview3t64

MATE document viewing library

Atril is a simple multi-page document viewer. It can display and print PostScript (PS), Encapsulated PostScript (EPS), DJVU, DVI, EPUB, ComicBook and Portable Document Format (PDF) files.

libparpack2t64

Parallel subroutines to solve large scale eigenvalue problems

ARPACK software is capable of solving large scale symmetric, nonsymmetric, and generalized eigenproblems from significant application areas. The software is designed to compute a few (k) eigenvalues with user specified features such as those of largest real part or largest magnitude. Storage requirements are on the order of n*k locations. No auxiliary storage is required. A set of Schur basis vectors for the desired k-dimensional eigen-space is computed which is numerically orthogonal to working precision. Numerically accurate eigenvectors are available on request.