Tool within FSL designed for fieldmap-based correction of geometric distortions in EPI images, crucial for accurate fMRI analysis.

A library that encapsulates the reference implementation of the NIfTI format as well as the legacy code necessary to perform I/O operations on NIfTI files.

Geant4 is a toolkit for the simulation of the passage of particles through matter. Its areas of application include high energy, nuclear and accelerator physics, as well as studies in medical and space science.

Geant4 is a toolkit for the simulation of the passage of particles through matter. Its areas of application include high energy, nuclear and accelerator physics, as well as studies in medical and space science.

Geant4 is a toolkit for the simulation of the passage of particles through matter. Its areas of application include high energy, nuclear and accelerator physics, as well as studies in medical and space science.

Geant4 is a toolkit for the simulation of the passage of particles through matter. Its areas of application include high energy, nuclear and accelerator physics, as well as studies in medical and space science.

Geant4 is a toolkit for the simulation of the passage of particles through matter. Its areas of application include high energy, nuclear and accelerator physics, as well as studies in medical and space science.

Geant4 is a toolkit for the simulation of the passage of particles through matter. Its areas of application include high energy, nuclear and accelerator physics, as well as studies in medical and space science.

The Gurobi Optimizer is a commercial optimization solver for linear programming (LP), quadratic programming (QP), quadratically constrained programming (QCP), mixed integer linear programming (MILP), mixed-integer quadratic programming (MIQP), and mixed-integer quadratically constrained programming (MIQCP). See here for more info: https://www.gurobi.com/documentation/9.0/quickstart_linux/cs_python.html.

OpenFabrics Interfaces (OFI) is a framework focused on exporting fabric communication services to applications. OFI is best described as a collection of libraries and applications used to export fabric services. The key components of OFI are: application interfaces, provider libraries, kernel services, daemons, and test applications.

Libfabric is a core component of OFI. It is the library that defines and exports the user-space API of OFI, and is typically the only software that applications deal with directly. It works in conjunction with provider libraries, which are often integrated directly into libfabric.

This package is low-level user-level Intel's communications interface. The PSM2 API is a high-performance vendor-specific protocol that provides a low-level communications interface for the Intel Omni-Path family of high-speed networking devices.

This package provides headers of the HFI1 Linux driver.

The Tensorboard Data Server is the backend component of TensorBoard that efficiently processes and serves log data. It improves TensorBoard's performance by handling large-scale event files asynchronously, enabling faster data loading and reduced memory usage.

JAX is Autograd and XLA, brought together for high-performance numerical computing, including large-scale machine learning research. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.

Tensorboard is a visualization toolkit for TensorFlow, designed to provide metrics tracking, model visualization, and performance analysis. It allows users to generate interactive dashboards for monitoring training progress, visualizing computational graphs, and analyzing data distributions.

Gloo is a collective communications library. It comes with a number of collective algorithms useful for machine learning applications. These include a barrier, broadcast, and allreduce.

TensorFlow is a flexible platform for building and training machine learning models. It provides a library for high performance numerical computation and includes high level Python APIs, including both a sequential API for beginners that allows users to build models quickly by plugging together building blocks and a subclassing API with an imperative style for advanced research.

TensorFlow is a flexible platform for building and training machine learning models. It provides a library for high performance numerical computation and includes high level Python APIs, including both a sequential API for beginners that allows users to build models quickly by plugging together building blocks and a subclassing API with an imperative style for advanced research.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

JAX is Autograd and XLA, brought together for high-performance numerical computing, including large-scale machine learning research. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.

ParMETIS is an MPI-based parallel library that implements a variety of algorithms for partitioning unstructured graphs, meshes, and for computing fill-reducing orderings of sparse matrices. ParMETIS extends the functionality provided by METIS and includes routines that are especially suited for parallel AMR computations and large scale numerical simulations. The algorithms implemented in ParMETIS are based on the parallel multilevel k-way graph-partitioning, adaptive repartitioning, and parallel multi-constrained partitioning schemes developed in our lab.

MUMPS (MUltifrontal Massively Parallel sparse direct Solver) solves a sparse system of linear equations A x = b using Gaussian elimination.

SuiteSparse is a suite of sparse matrix algorithms, including: UMFPACK, multifrontal LU factorization; CHOLMOD, supernodal Cholesky; SPQR, multifrontal QR; KLU and BTF, sparse LU factorization, well-suited for circuit simulation; ordering methods (AMD, CAMD, COLAMD, and CCOLAMD); CSparse and CXSparse, a concise sparse Cholesky factorization package; and many other packages.

This package contains all of the above-mentioned parts.

ParMETIS is an MPI-based parallel library that implements a variety of algorithms for partitioning unstructured graphs, meshes, and for computing fill-reducing orderings of sparse matrices. ParMETIS extends the functionality provided by METIS and includes routines that are especially suited for parallel AMR computations and large scale numerical simulations. The algorithms implemented in ParMETIS are based on the parallel multilevel k-way graph-partitioning, adaptive repartitioning, and parallel multi-constrained partitioning schemes developed in our lab.