_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


python-ttpy 1.2.0-0.22dff3d
Dependencies: gmp@6.3.0 mpfr@4.2.2 openblas@0.3.30
Propagated dependencies: python-numpy@1.26.4 python-scipy@1.12.0 python-six@1.17.0
Channel: guix-hpc
Location: guix-hpc/packages/python-science.scm (guix-hpc packages python-science)
Home page: https://github.com/oseledets/ttpy
Licenses: Expat
Build system: python
Synopsis: Python implementation of the Tensor Train (TT) toolbox
Description:

Python implementation of the Tensor Train (TT) toolbox. It contains several important packages for working with the TT-format in Python. It is able to do TT-interpolation, solve linear systems, eigenproblems, solve dynamical problems. Several computational routines are done in Fortran (which can be used separately), and are wrapped with the f2py tool.

python-latexify-py 0.0.7
Propagated dependencies: python-dill@0.4.0
Channel: guix-hpc
Location: guix-hpc/packages/python-science.scm (guix-hpc packages python-science)
Home page: https://github.com/google/latexify_py
Licenses:
Build system: python
Synopsis: Generates LaTeX source from Python functions.
Description:

Generates LaTeX source from Python functions.

python-easydict 1.9
Channel: guix-hpc
Location: guix-hpc/packages/python-science.scm (guix-hpc packages python-science)
Home page: https://github.com/makinacorpus/easydict
Licenses:
Build system: python
Synopsis: Access dict values as attributes (works recursively).
Description:

Access dict values as attributes (works recursively).

python-torch-vision 0.2.2
Dependencies: python-pytorch@2.9.0 python-pillow@6.1.0 python-scipy@1.12.0
Channel: guix-hpc
Location: guix-hpc/packages/python-science.scm (guix-hpc packages python-science)
Home page: https://github.com/pytorch/vision
Licenses: Modified BSD
Build system: python
Synopsis: image and video datasets and models for torch deep learning
Description:

image and video datasets and models for torch deep learning

python-pillow 6.1.0
Dependencies: freetype@2.13.3 lcms@2.13.1 libjpeg-turbo@2.1.4 libtiff@4.4.0 libwebp@1.3.2 openjpeg@2.5.0 zlib@1.3.1
Channel: guix-hpc
Location: guix-hpc/packages/python-science.scm (guix-hpc packages python-science)
Home page: https://python-pillow.org
Licenses: X11-style
Build system: pyproject
Synopsis: Fork of the Python Imaging Library
Description:

The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.

python-torch-diffeq 0.2.2
Dependencies: python-pytorch@2.9.0 python-pillow@6.1.0 python-scipy@1.12.0
Channel: guix-hpc
Location: guix-hpc/packages/python-science.scm (guix-hpc packages python-science)
Home page: https://github.com/rtqichen/torchdiffeq
Licenses: Expat
Build system: python
Synopsis: Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
Description:

This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpropagation through ODE solutions is supported using the adjoint method for constant memory cost. For usage of ODE solvers in deep learning applications.

As the solvers are implemented in PyTorch, algorithms in this repository are fully supported to run on the GPU.

python-pyevtk 1.6.0
Propagated dependencies: python-numpy@1.26.4
Channel: guix-hpc
Location: guix-hpc/packages/python-science.scm (guix-hpc packages python-science)
Home page: https://github.com/pyscience-projects/pyevtk
Licenses: FreeBSD
Build system: pyproject
Synopsis: Export data as binary VTK files
Description:

Export data as binary VTK files

chameleon-hip 1.4.0
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/chameleon
Licenses: CeCILL-C
Build system: cmake
Synopsis: Dense linear algebra solver
Description:

Chameleon is a dense linear algebra solver relying on sequential task-based algorithms where sub-tasks of the overall algorithms are submitted to a run-time system. Such a system is a layer between the application and the hardware which handles the scheduling and the effective execution of tasks on the processing units. A run-time system such as StarPU is able to manage automatically data transfers between not shared memory area (CPUs-GPUs, distributed nodes).

starpu-example-cppgemm 0.1.0
Dependencies: fmt@9.1.0 openblas@0.3.30 starpu@1.4.12
Propagated dependencies: openmpi@4.1.6
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://github.com/Blixodus/starpu_gemm
Licenses: CeCILL-C
Build system: cmake
Synopsis: C++ StarPU example of a distributed gemm
Description:

Example showing how to use starpu for implementing a distributed gemm in C++.

pastix-nompi 6.4.0
Dependencies: parsec@0.0-0.6022a61 gfortran@14.3.0 hwloc@2.12.2 starpu@1.4.12 scotch@7.0.7 openblas@0.3.30 python@3.11.14 python-numpy@1.26.4
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/pastix
Licenses: CeCILL
Build system: cmake
Synopsis: Sparse matrix direct solver
Description:

PaStiX (Parallel Sparse matriX package) is a scientific library that provides a high performance parallel solver for very large sparse linear systems based on direct methods. Numerical algorithms are implemented in single or double precision (real or complex) using LLt, LDLt and LU with static pivoting (for non symmetric matrices having a symmetric pattern). This solver also provides some low-rank compression methods to reduce the memory footprint and/or the time-to-solution.

pastix-6.2-nopython-notest 6.2.2
Dependencies: gfortran@14.3.0 hwloc@2.12.2 starpu@1.4.12 scotch@7.0.7 openblas@0.3.30 openmpi@4.1.6 python@3.11.14 python-numpy@1.26.4
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/pastix
Licenses: CeCILL
Build system: cmake
Synopsis: Sparse matrix direct solver
Description:

PaStiX (Parallel Sparse matriX package) is a scientific library that provides a high performance parallel solver for very large sparse linear systems based on direct methods. Numerical algorithms are implemented in single or double precision (real or complex) using LLt, LDLt and LU with static pivoting (for non symmetric matrices having a symmetric pattern). This solver also provides some low-rank compression methods to reduce the memory footprint and/or the time-to-solution.

starpu-example-dgemm 0.1.0
Dependencies: openblas@0.3.30 starpu@1.4.12 openmpi@4.1.6
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/mini-examples/starpu_example_dgemm/
Licenses: CeCILL-C
Build system: cmake
Synopsis: StarPU example of a distributed gemm
Description:

Example showing how to use starpu for implementing a distributed gemm.

chameleon-nmad 1.4.0
Dependencies: openblas@0.3.30 starpu@1.4.12 nmad@2025-03-18
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/chameleon
Licenses: CeCILL-C
Build system: cmake
Synopsis: Dense linear algebra solver
Description:

Chameleon is a dense linear algebra solver relying on sequential task-based algorithms where sub-tasks of the overall algorithms are submitted to a run-time system. Such a system is a layer between the application and the hardware which handles the scheduling and the effective execution of tasks on the processing units. A run-time system such as StarPU is able to manage automatically data transfers between not shared memory area (CPUs-GPUs, distributed nodes).

scalfmm 3.0
Dependencies: openblas@0.3.30 fftw@3.3.10 fftwf@3.3.10
Propagated dependencies: openmpi@4.1.6
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/ScalFMM.git
Licenses: CeCILL-C
Build system: cmake
Synopsis: Fast Multipole Method Framework
Description:

ScalFMM is a C++ library that implements a kernel-independent Fast Multipole Method.

python-mpi4py 4.1.0
Dependencies: openmpi@4.1.6
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://github.com/mpi4py/mpi4py
Licenses: Modified BSD
Build system: pyproject
Synopsis: Python bindings for the Message Passing Interface standard
Description:

MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors.

mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point and collective communications of any picklable Python object as well as optimized communications of Python objects (such as NumPy arrays) that expose a buffer interface.

python-genfem 1.2
Propagated dependencies: python@3.11.14 python-ddmpy@0.1 python-mpi4py@4.1.0 python-numpy@1.26.4 python-scipy@1.12.0 python-sympy@1.13.3 openmpi@4.1.6
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/genfem.git
Licenses: CeCILL-C
Build system: pyproject
Synopsis: A simple FEM matrix generator in python
Description:

Assembles fem matrices using an efficient vectorization method.

pmtool 1.0.0
Dependencies: recutils@1.9
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/eyrauddu/pmtool
Licenses: GPL 3+
Build system: cmake
Synopsis: pmtool: Post-Mortem Tool
Description:

pmtool aims at performing post-mortem analyses of the behavior of StarPU applications. Provide lower bounds on makespan. Study the performance of different schedulers in a simple context. Limitations: ignore communications for the moment; branch comms attempts to remove this limitation.

chameleon-simgrid-nosmpi 1.4.0
Dependencies: starpu-simgrid@1.4.12 simgrid@4.1 openblas@0.3.30 openmpi@4.1.6
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/chameleon
Licenses: CeCILL-C
Build system: cmake
Synopsis: Dense linear algebra solver
Description:

Chameleon is a dense linear algebra solver relying on sequential task-based algorithms where sub-tasks of the overall algorithms are submitted to a run-time system. Such a system is a layer between the application and the hardware which handles the scheduling and the effective execution of tasks on the processing units. A run-time system such as StarPU is able to manage automatically data transfers between not shared memory area (CPUs-GPUs, distributed nodes).

parsec-mpi 0.0-0.6022a61
Dependencies: hwloc@2.12.2 bison@3.8.2 flex@2.6.4
Propagated dependencies: openmpi@4.1.6
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://bitbucket.org/mfaverge/parsec.git
Licenses: FreeBSD
Build system: cmake
Synopsis: Runtime system based on dynamic task generation mechanism
Description:

PaRSEC is a generic framework for architecture aware scheduling and management of micro-tasks on distributed many-core heterogeneous architectures.

chameleon-hip-nompi 1.4.0
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/chameleon
Licenses: CeCILL-C
Build system: cmake
Synopsis: Dense linear algebra solver
Description:

Chameleon is a dense linear algebra solver relying on sequential task-based algorithms where sub-tasks of the overall algorithms are submitted to a run-time system. Such a system is a layer between the application and the hardware which handles the scheduling and the effective execution of tasks on the processing units. A run-time system such as StarPU is able to manage automatically data transfers between not shared memory area (CPUs-GPUs, distributed nodes).

pastix-nopython-notest 6.4.0
Dependencies: gfortran@14.3.0 hwloc@2.12.2 starpu@1.4.12 scotch@7.0.7 openblas@0.3.30 openmpi@4.1.6 python@3.11.14 python-numpy@1.26.4
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/pastix
Licenses: CeCILL
Build system: cmake
Synopsis: Sparse matrix direct solver
Description:

PaStiX (Parallel Sparse matriX package) is a scientific library that provides a high performance parallel solver for very large sparse linear systems based on direct methods. Numerical algorithms are implemented in single or double precision (real or complex) using LLt, LDLt and LU with static pivoting (for non symmetric matrices having a symmetric pattern). This solver also provides some low-rank compression methods to reduce the memory footprint and/or the time-to-solution.

chameleon-openmp 1.4.0
Dependencies: openblas@0.3.30
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/chameleon
Licenses: CeCILL-C
Build system: cmake
Synopsis: Dense linear algebra solver
Description:

Chameleon is a dense linear algebra solver relying on sequential task-based algorithms where sub-tasks of the overall algorithms are submitted to a run-time system. Such a system is a layer between the application and the hardware which handles the scheduling and the effective execution of tasks on the processing units. A run-time system such as StarPU is able to manage automatically data transfers between not shared memory area (CPUs-GPUs, distributed nodes).

starpu-example-stencil 0.1.0-0.40ec571
Dependencies: openblas@0.3.30 starpu@1.4.12 openmpi@4.1.6
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://gitlab.inria.fr/solverstack/mini-examples/starpu_example_stencil/
Licenses: CeCILL-C
Build system: cmake
Synopsis: StarPU example of a distributed regular 2D stencil
Description:

Example showing how to use starpu to implement a distributed regular 2D stencil with communication-avoiding techniques

parsec 0.0-0.6022a61
Dependencies: hwloc@2.12.2 bison@3.8.2 flex@2.6.4
Channel: guix-hpc
Location: guix-hpc/packages/solverstack.scm (guix-hpc packages solverstack)
Home page: https://bitbucket.org/mfaverge/parsec.git
Licenses: FreeBSD
Build system: cmake
Synopsis: Runtime system based on dynamic task generation mechanism
Description:

PaRSEC is a generic framework for architecture aware scheduling and management of micro-tasks on distributed many-core heterogeneous architectures.

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