# packages

pkgId: 0c6e9623d44bc1f16286802454e5dc68e13daf4e5698116fdade1706abdb3ddd
name: scls-gcc
arch: src
version: 2026
epoch: 0
release: 1.amzn2023
summary: Default build with GCC compilers and OpenBLAS
rpm_license: BSD-3-Clause-LBNL
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-2026-1.amzn2023.src.rpm
checksum_type: sha256
description:
  Default build with GCC compilers and OpenBLAS

  This is a meta-package that depends on every package in the SCLS gcc
  flavor. Installing it will pull in the complete scientific computing stack.


pkgId: 53da80fa971f3c66ce474eacd3d9eacd77de314cd386e9d06e73693e2473f360
name: scls-gcc-armadillo
arch: src
version: 15.2.7
epoch: 0
release: 1.amzn2023
summary: Fast C++ matrix library with syntax similar to MATLAB and Octave
url: https://arma.sourceforge.net/
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-armadillo-15.2.7-1.amzn2023.src.rpm
checksum_type: sha256
description:
  Armadillo is a C++ linear algebra library (matrix maths)
  aiming towards a good balance between speed and ease of use.
  Integer, floating point and complex numbers are supported,
  as well as a subset of trigonometric and statistics functions.
  Various matrix decompositions are provided through optional
  integration with LAPACK and ATLAS libraries.
  A delayed evaluation approach is employed (during compile time)
  to combine several operations into one and reduce (or eliminate)
  the need for temporaries. This is accomplished through recursive
  templates and template meta-programming.
  This library is useful if C++ has been decided as the language
  of choice (due to speed and/or integration capabilities), rather
  than another language like Matlab or Octave.


pkgId: 7ec028a4411d0da3dac67c793d397b96283562e4cb915efbce58540db79efa4b
name: scls-gcc-arpack-ng
arch: src
version: 3.9.1
epoch: 0
release: 1.amzn2023
summary: Fortran 77 subroutines designed to solve large scale eigenvalue problems.
url: https://github.com/opencollab/arpack-ng
rpm_license: BSD
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-arpack-ng-3.9.1-1.amzn2023.src.rpm
checksum_type: sha256
description:
  ARPACK-NG is a collection of Fortran 77 subroutines designed to solve
  large scale eigenvalue problems.

  The package is designed to compute a few eigenvalues and corresponding
  eigenvectors of a general n by n matrix A. It is most appropriate for
  large sparse or structured matrices A where structured means that a
  matrix-vector product w <- Av requires order n rather than the usual
  order n**2 floating point operations. This software is based upon an
  algorithmic variant of the Arnoldi process called the Implicitly
  Restarted Arnoldi Method (IRAM).


pkgId: b9962ad3ee6fc2216caff6b9341176d3266dc893798cc2c72aabab7083c4fab0
name: scls-gcc-blaspp
arch: src
version: 2025.05.28
epoch: 0
release: 1.amzn2023
summary: C++ API for the Basic Linear Algebra Subprograms (BLAS)
url: https://icl.utk.edu/slate/
rpm_license: BSD-3-Clause
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-blaspp-2025.05.28-1.amzn2023.src.rpm
checksum_type: sha256
description:
  The Basic Linear Algebra Subprograms (BLAS) have been around for many
  decades and serve as the de facto standard for performance-portable and
  numerically robust implementation of essential linear algebra
  functionality. Originally, they were written in Fortran, and later
  furnished with a C API (CBLAS).

  The objective of BLAS++ is to provide a convenient, performance
  oriented API for development in the C++ language, that,
  for the most part, preserves established conventions, while, at the
  same time, takes advantages of modern C++ features, such as:
  namespaces, templates, exceptions, etc.

  BLAS++ is part of the SLATE project
  (Software for Linear Algebra Targeting Exascale), which is funded by
  the Department of Energy as part of its Exascale Computing Initiative
  (ECP). Closely related to BLAS++ is the LAPACK++ project,
  which provides a C++ API for LAPACK.


pkgId: 1097e0326a33f5e05719125422c5cc0ae10fe04b8ae7e90f23d2f5b57dd5037d
name: scls-gcc-blaze
arch: src
version: 3.8.2
epoch: 0
release: 1.amzn2023
summary: A high-performance C++ math library for dense and sparse arithmetic
url: https://bitbucket.org/blaze-lib/blaze
rpm_license: BSD
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-blaze-3.8.2-1.amzn2023.src.rpm
checksum_type: sha256
description:
  Blaze is an open-source, high-performance C++ math library for dense and \
  sparse arithmetic. With its state-of-the-art Smart Expression Template \
  implementation Blaze combines the elegance and ease of use of a \
  domain-specific language with HPC-grade performance, making it one of \
  the most intuitive and fastest C++ math libraries available. \


pkgId: a2a51b4291d1927fba20f60450ea00689c61047d7870b7c3e5a0f6794f236d16
name: scls-gcc-cmake
arch: src
version: 4.3.3
epoch: 0
release: 1.amzn2023
summary: Cross-platform make system
url: https://cmake.org/
rpm_license: BSD-3-Clause
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-cmake-4.3.3-1.amzn2023.src.rpm
checksum_type: sha256
description:
  CMake is used to control the software compilation process using simple
  platform and compiler independent configuration files. CMake generates
  native makefiles and workspaces that can be used in the compiler
  environment of your choice. CMake is quite sophisticated: it is possible
  to support complex environments requiring system configuration, preprocessor
  generation, code generation, and template instantiation.


pkgId: 00df6609a706b5fba2d09bacde37d1ea9576c3deb43c0e3ede242c5a9a645196
name: scls-gcc-examples
arch: src
version: 2026
epoch: 0
release: 1.amzn2023
summary: Example programs for the SCLS gcc flavor
rpm_license: BSD-3-Clause-LBNL
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-examples-2026-1.amzn2023.src.rpm
checksum_type: sha256
description:
  Example programs for the SCLS gcc flavor.

  This meta-package pulls in the example programs shipped by packages in the
  SCLS gcc flavor (e.g. PETSc, SLEPc, SUNDIALS). It is optional — install it
  only if you want the upstream example sources on disk.


pkgId: 473e97a70c94e907457940cc2e66a8f783f93850ceedebb09ccd4a42324b426f
name: scls-gcc-googletest
arch: src
version: 1.17.0
epoch: 0
release: 1.amzn2023
summary: GoogleTest - Google Testing and Mocking Framework
url: https://github.com/google/googletest
rpm_license: BSD-3-Clause
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-googletest-1.17.0-1.amzn2023.src.rpm
checksum_type: sha256
description:
  Googletest is a comprehensive testing framework for C++ designed to support test-driven development and Google's extensive internal test suites. It provides a rich set of assertions, user-defined assertions, death tests, and easy-to-use fixtures for writing both small and large test cases. Googletest is portable and can be used across diverse platforms. It encourages organized, expressive, and efficient testing practices, and integrates well with various build systems. This makes it suitable for both new and existing C++ projects looking to implement robust testing.


pkgId: baa09209148da1242db962fc4e026db5af761f9540aaca8943e3c53abe6ddde9
name: scls-gcc-gperftools
arch: src
version: 2.18.1
epoch: 0
release: 1.amzn2023
summary: Very fast malloc and performance analysis tools
url: https://github.com/gperftools/gperftools
rpm_license: BSD
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-gperftools-2.18.1-1.amzn2023.src.rpm
checksum_type: sha256
description:
  Perf Tools is a collection of performance analysis tools, including a
  high-performance multi-threaded malloc() implementation that works
  particularly well with threads and STL, a thread-friendly heap-checker,
  a heap profiler, and a cpu-profiler.

  This package provides the gperftools shared libraries (tcmalloc,
  profiler) and development headers. Starting with gperftools 2.17 the
  bundled pprof analyzer was removed upstream; install Google's Go
  version separately (`go install github.com/google/pprof@latest`) to
  post-process profile output.


pkgId: 591a12035d12dfb1aab72ac175e9110dec44c1521f8579bf567d6b8a371def4a
name: scls-gcc-hdf5
arch: src
version: 1.14.6
epoch: 0
release: 1.amzn2023
summary: A general purpose library and file format for storing scientific data
url: https://www.hdfgroup.org/solutions/hdf5
rpm_license: BSD
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-hdf5-1.14.6-1.amzn2023.src.rpm
checksum_type: sha256
description:
  HDF5 is a general purpose library and file format for storing scientific data.
  HDF5 can store two primary objects: datasets and groups. A dataset is
  essentially a multidimensional array of data elements, and a group is a
  structure for organizing objects in an HDF5 file. Using these two basic
  objects, one can create and store almost any kind of scientific data
  structure, such as images, arrays of vectors, and structured and unstructured
  grids. You can also mix and match them in HDF5 files according to your needs.


pkgId: d250c6656bba140df64239ac6fffb51ff9ae7aafbba4899b334ca35e5e195ba1
name: scls-gcc-hwloc
arch: src
version: 2.13.0
epoch: 0
release: 1.amzn2023
summary: Portable Hardware Locality
url: https://www-lb.open-mpi.org/software/hwloc/
rpm_license: BSD
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-hwloc-2.13.0-1.amzn2023.src.rpm
checksum_type: sha256
description:
  The Portable Hardware Locality (hwloc) software package provides a
  portable abstraction (across OS, versions, architectures, ...) of
  the hierarchical topology of modern architectures, including NUMA
  memory nodes (DRAM, HBM, non-volatile memory, CXL, etc.), sockets,
  shared caches, cores and simultaneous multithreading.

  It also gathers various system attributes such as cache and memory
  information as well as the locality of I/O devices such as network
  interfaces, InfiniBand HCAs or GPUs.


pkgId: dd95f8605e1fa63234bd507e72735a03aada6fcd7e1dc0b8a9d743f83713381b
name: scls-gcc-lapackpp
arch: src
version: 2025.05.28
epoch: 0
release: 1.amzn2023
summary: C++ API for the Linear Algebra Package (LAPACK)
url: https://icl.utk.edu/slate/
rpm_license: BSD-3-Clause
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-lapackpp-2025.05.28-1.amzn2023.src.rpm
checksum_type: sha256
description:
  The Linear Algebra PACKage (LAPACK) is a standard software library for numerical linear algebra. It provides routines for solving systems of linear equations and linear least squares problems, eigenvalue problems, and singular value decomposition. It also includes routines to implement the associated matrix factorizations such as LU, QR, Cholesky, etc. LAPACK was originally written in FORTRAN 77, and moved to Fortran 90 in version 3.2 (2008). LAPACK provides routines for handling both real and complex matrices in both single and double precision.

  The objective of LAPACK++ is to provide a convenient, performance oriented API for development in the C++ language, that, for the most part, preserves established conventions, while, at the same time, takes advantages of modern C++ features, such as: namespaces, templates, exceptions, etc.


pkgId: e8eaf88176aed435f4cac0e785c22c099716e7c78a45a41504ec82ec90508152
name: scls-gcc-libevent
arch: src
version: 2.1.12
epoch: 0
release: 1.amzn2023
summary: Abstract asynchronous event notification library
url: https://libevent.org/
rpm_license: BSD and ISC
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-libevent-2.1.12-1.amzn2023.src.rpm
checksum_type: sha256
description:
  The libevent API provides a mechanism to execute a callback function
  when a specific event occurs on a file descriptor or after a timeout
  has been reached. libevent is meant to replace the asynchronous event
  loop found in event driven network servers. An application just needs
  to call event_dispatch() and can then add or remove events dynamically
  without having to change the event loop.


pkgId: 87018609d94c1c43a27ae10ac95272dc06231ddeb273409d4f89c949cac8ee02
name: scls-gcc-metis
arch: src
version: 5.2.1
epoch: 0
release: 1.amzn2023
summary: Serial Graph Partitioning and Fill-reducing Matrix Ordering
url: https://glaros.dtc.umn.edu/gkhome/views/metis
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-metis-5.2.1-1.amzn2023.src.rpm
checksum_type: sha256
description:
  METIS is a set of serial programs for partitioning graphs,
  partitioning finite element meshes, and producing fill reducing
  orderings for sparse matrices.
  The algorithms implemented in METIS are based on the multilevel
  recursive-bisection, multilevel k-way, and multi-constraint
  partitioning schemes developed in our lab.
  METIS is distributed with OpenMP support.


pkgId: 62e5a60939ef51a6cda086dc916bf0928e90e42904f09e6f00da4c32a6c25ed0
name: scls-gcc-mumps
arch: src
version: 5.9.0
epoch: 0
release: 1.amzn2023
summary: A MUltifrontal Massively Parallel sparse direct Solver
url: https://mumps-solver.org
rpm_license: CeCILL-C
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-mumps-5.9.0-1.amzn2023.src.rpm
checksum_type: sha256
description:
  MUMPS implements a direct solver for large sparse linear systems, with a
  particular focus on symmetric positive definite matrices. It can
  operate on distributed matrices e.g. over a cluster. It has Fortran and
  C interfaces, and can interface with ordering tools such as Scotch.


pkgId: a34f410f0554437c15bc62ba5a95d6970c8cd13bcd0cc6cd6ec7adb77996011f
name: scls-gcc-openblas
arch: src
version: 0.3.33
epoch: 0
release: 1.amzn2023
summary: An optimized BLAS library based on GotoBLAS2
url: https://github.com/OpenMathLib/OpenBLAS
rpm_license: BSD-3-Clause
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-openblas-0.3.33-1.amzn2023.src.rpm
checksum_type: sha256
description:
  OpenBLAS is an optimized BLAS (Basic Linear Algebra Subprograms) library based
  on GotoBLAS2 1.13 BSD version.

  For more information about OpenBLAS, please see:

  * The documentation at openmathlib.org/OpenBLAS/docs/,
  * The home page at openmathlib.org/OpenBLAS/.

  For a general introduction to the BLAS routines, please refer to the extensive
  documentation of their reference implementation hosted at netlib:

  https://www.netlib.org/blas.

  On that site you will likewise find documentation for the reference
  implementation of the higher-level library LAPACK - the Linear Algebra Package
  that comes included with OpenBLAS.

  If you are looking for a general primer or refresher on Linear Algebra, the set
  of six 20-minute lecture videos by Prof. Gilbert Strang on either MIT
  OpenCourseWare here or YouTube here may be helpful.


pkgId: 0743448a02af10030bef11f20891b0abc59eb5ed31f7d71e173826697029f5d2
name: scls-gcc-openmpi
arch: src
version: 5.0.10
epoch: 0
release: 1.amzn2023
summary: A powerful implementation of MPI/SHMEM
url: https://www.open-mpi.org
rpm_license: BSD
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-openmpi-5.0.10-1.amzn2023.src.rpm
checksum_type: sha256
description:
  Open MPI is an open source implementation of the Message Passing
  Interface specification (https://www.mpi-forum.org/) developed and
  maintained by a consortium of research, academic, and industry
  partners.

  Open MPI also includes an implementation of the OpenSHMEM parallel
  programming API (https://www.openshmem.org/).  OpenSHMEM is a
  Partitioned Global Address Space (PGAS) abstraction layer, which
  provides fast inter-process communication using one-sided
  communication techniques.


pkgId: f1dd11f65d7552d68cb9f9a8c966765413a2de2df92df1c18fde0c9ff2d70c11
name: scls-gcc-parmetis
arch: src
version: 4.0.3
epoch: 0
release: 1.amzn2023
summary: Parallel Graph Partitioning and Fill-reducing Matrix Ordering
url: https://github.com/KarypisLab/ParMETIS
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-parmetis-4.0.3-1.amzn2023.src.rpm
checksum_type: sha256
description:
  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 at the Karzpis Lab, University of Minesota.


pkgId: d0bdd6bf922e1df493ba30cb736dbea3c723e777f8c8b7477aec57c40b34b50e
name: scls-gcc-pmix
arch: src
version: 5.0.10
epoch: 0
release: 1.amzn2023
summary: An extended/exascale implementation of the PMIx Standard
url: https://github.com/openpmix/openpmix
rpm_license: BSD
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-pmix-5.0.10-1.amzn2023.src.rpm
checksum_type: sha256
description:
  The Process Management Interface (PMI) has been used for quite some time as a
  means of exchanging wireup information needed for interprocess communication. Two
  versions (PMI-1 and PMI-2) have been released as part of the MPICH effort. While
  PMI-2 demonstrates better scaling properties than its PMI-1 predecessor, attaining
  rapid launch and wireup of the roughly 1M processes executing across 100k nodes
  expected for exascale operations remains challenging.

  PMI Exascale (PMIx) represents an attempt to resolve these questions by providing
  an extended version of the PMI standard specifically designed to support clusters
  up to and including exascale sizes. The overall objective of the project is to
  eliminate some current restrictions that impact scalability, and provide
  a reference implementation of the PMIx-server that demonstrates the desired level of
  scalability.


pkgId: 178f7d59611593b321278d73eed2c3096b15a26e5502204e3a506bc9fca11959
name: scls-gcc-scalapack
arch: src
version: 2.2.3
epoch: 0
release: 1.amzn2023
summary: A subset of LAPACK routines redesigned for heterogeneous computing
url: https://www.netlib.org/scalapack/
rpm_license: BSD
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-scalapack-2.2.3-1.amzn2023.src.rpm
checksum_type: sha256
description:
  The ScaLAPACK (or Scalable LAPACK) library includes a subset
  of LAPACK routines redesigned for distributed memory MIMD
  parallel computers. It is currently written in a
  Single-Program-Multiple-Data style using explicit message
  passing for inter-processor communication. It assumes
  matrices are laid out in a two-dimensional block cyclic
  decomposition.

  ScaLAPACK is designed for heterogeneous computing and is
  portable on any computer that supports MPI or PVM.

  Like LAPACK, the ScaLAPACK routines are based on
  block-partitioned algorithms in order to minimize the frequency
  of data movement between different levels of the memory hierarchy.
  (For such machines, the memory hierarchy includes the off-processor
  memory of other processors, in addition to the hierarchy of registers,
  cache, and local memory on each processor.) The fundamental building
  blocks of the ScaLAPACK library are distributed memory versions (PBLAS)
  of the Level 1, 2 and 3 BLAS, and a set of Basic Linear Algebra
  Communication Subprograms (BLACS) for communication tasks that arise
  frequently in parallel linear algebra computations. In the ScaLAPACK
  routines, all inter-processor communication occurs within the PBLAS and the
  BLACS. One of the design goals of ScaLAPACK was to have the ScaLAPACK
  routines resemble their LAPACK equivalents as much as possible.


pkgId: 649df0e8e6347e2ccdb4ce7475b2298af08c6e0c296e0928379b7da1e59e62b6
name: scls-gcc-scotch
arch: src
version: 7.0.11
epoch: 0
release: 1.amzn2023
summary: Graph, mesh and hypergraph partitioning library
url: https://www.labri.fr/perso/pelegrin/scotch/
rpm_license: CeCILL-C
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-scotch-7.0.11-1.amzn2023.src.rpm
checksum_type: sha256
description:
  Scotch is a software package for graph and mesh/hypergraph partitioning and
  sparse matrix ordering. The parallel scotch libraries (PT-Scotch) provide
  MPI-based parallel graph partitioning capabilities.


pkgId: 5c8309d5d55f8afef77860df6071c2d0630dd37c6766aaf80cde425f2b7e2015
name: scls-gcc-slate
arch: src
version: 2025.05.28
epoch: 0
release: 1.amzn2023
summary: Software for Linear Algebra Targeting Exascale
url: https://icl.bitbucket.io/slate/
rpm_license: BSD-3-Clause
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-slate-2025.05.28-1.amzn2023.src.rpm
checksum_type: sha256
description:
  Software for Linear Algebra Targeting Exascale (SLATE) is being developed as part of the Exascale Computing Project (ECP), which is a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration (NNSA). SLATE will deliver fundamental dense linear algebra capabilities for current and upcoming distributed-memory systems, including GPU-accelerated systems as well as more traditional multi core-only systems.

  SLATE will provide coverage of existing LAPACK and ScaLAPACK functionality, including parallel implementations of Basic Linear Algebra Subroutines (BLAS), linear systems solvers, least squares solvers, and singular value and eigenvalue solvers. In this respect, SLATE will serve as a replacement for LAPACK and ScaLAPACK, which, after two decades of operation, cannot be adequately retrofitted for modern, GPU-accelerated architectures.

  SLATE is built on top of standards, such as MPI and OpenMP, and de facto-standard industry solutions such as NVIDIA CUDA and AMD HIP. SLATE also relies on high performance implementations of numerical kernels from vendor libraries, such as Intel MKL, IBM ESSL, NVIDIA cuBLAS, and AMD rocBLAS. SLATE interacts with these libraries through a layer of C++ APIs.


pkgId: 89b408c2610e2d35834e957b457ace29acd60455430e0e2602dad84af8b33119
name: scls-gcc-superlu
arch: src
version: 7.0.1
epoch: 0
release: 1.amzn2023
summary: Subroutines to directly solve sparse linear systems
url: https://github.com/xiaoyeli/superlu
rpm_license: BSD-3-Clause
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-superlu-7.0.1-1.amzn2023.src.rpm
checksum_type: sha256
description:
  SuperLU contains a set of subroutines to solve a sparse linear system
  A*X=B. It uses Gaussian elimination with partial pivoting (GEPP).
  The columns of A may be preordered before factorization; the
  preordering for sparsity is completely separate from the factorization.


pkgId: c1c24725c918309a8a7a4f75739198bcff00ae17e6dd74055cb2978df902feff
name: scls-gcc-testsweeper
arch: src
version: 2025.05.28
epoch: 0
release: 1.amzn2023
summary: C++ testing framework for parameter sweeps
url: https://github.com/icl-utk-edu/testsweeper
rpm_license: BSD-3-Clause
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-gcc-testsweeper-2025.05.28-1.amzn2023.src.rpm
checksum_type: sha256
description:
  TestSweeper is a C++ testing framework for parameter sweeps.
  It handles parsing command line options, iterating over the test space,
  and printing results. This simplifies test functions by allowing them to
  concentrate on setting up and solving one problem at a time.

  TestSweeper is part of the SLATE project
  (Software for Linear Algebra Targeting Exascale), which is funded by
  the Department of Energy as part of its Exascale Computing Initiative
  (ECP).


pkgId: a0d18dbe29d7fa2f43d9ddf0ab585f8d7684c40fca550b98eb6c82c28f22aeee
name: scls-mkl
arch: src
version: 2026
epoch: 0
release: 1.amzn2023
summary: Production build with GCC compilers and Intel MKL
rpm_license: BSD-3-Clause-LBNL
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-mkl-2026-1.amzn2023.src.rpm
checksum_type: sha256
description:
  Production build with GCC compilers and Intel MKL

  This is a meta-package that depends on every package in the SCLS mkl
  flavor. Installing it will pull in the complete scientific computing stack.


pkgId: 483f2c6d88b984574712725b1a3a1ed087d5dcacdf75faeefc5b190cc575cc9f
name: scls-mkl-examples
arch: src
version: 2026
epoch: 0
release: 1.amzn2023
summary: Example programs for the SCLS mkl flavor
rpm_license: BSD-3-Clause-LBNL
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-mkl-examples-2026-1.amzn2023.src.rpm
checksum_type: sha256
description:
  Example programs for the SCLS mkl flavor.

  This meta-package pulls in the example programs shipped by packages in the
  SCLS mkl flavor (e.g. PETSc, SLEPc, SUNDIALS). It is optional — install it
  only if you want the upstream example sources on disk.


pkgId: 77fe4ef7a29feb4da4925e338beeef223e40205b286c9ee79a220ddbd61ec352
name: scls-release
arch: src
version: 2026
epoch: 0
release: 1.amzn2023
summary: SCLS repository configuration and GPG key
url: https://belfem.lbl.gov/scls
rpm_license: BSD-3-Clause-LBNL
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/scls-release-2026-1.amzn2023.src.rpm
checksum_type: sha256
description:
  This package provides the repository configuration and GPG key for the
  Scientific Core Library Stack (SCLS).


