Package: blimps-examples Description-md5: 18ddc9ff8aab78bc1efd53cccc24389d Description-en: blocks database improved searcher (example data) BLIMPS (BLocks IMProved Searcher) is a searching tool that scores a protein sequence against blocks or a block against sequences. . This package contains example data. Package: blimps-utils Description-md5: 1afe4567b883b39e7050a4f604a91e17 Description-en: blocks database improved searcher BLIMPS (BLocks IMProved Searcher) is a searching tool that scores a protein sequence against blocks or a block against sequences. . This package contains the binaries. Package: cuda-drivers-fabricmanager-580 Description-md5: 6715a1a6e39ab2cdf04d9fe216556c20 Description-en: Meta-package for FM and Driver Convenience meta-package for installing fabricmanager and the cuda-drivers meta-package simultaneously while keeping version equivalence. This meta- package is branch-specific. Package: distroav Description-md5: b85b6b7dc58a9f51e7afd9325eace389 Description-en: Network Audio/Video in OBS-Studio using NDI technology Formerly obs-ndi, distroav is an NDI plugin for obs-studio. NDI is a Network Audio/Video Protocol allowing high quality, low latency and multi track audio local streaming. . Features: - Receive NDI video and audio in OBS - Transmit OBS video and audio to NDI - Transmit a single OBS source or scene audio to NDI. . Note: This package does NOT include the NDI runtime, which must be downloaded and installed separately. This package provides instructions on how to do that. . NDI® is a registered trademark of Vizrt NDI AB Package: festlex-oald Description-md5: ce5877e81c377e82a4e6185ed2605182 Description-en: Festival lexicon from Oxford Advanced Learners' Dictionary A festival lexicon derived from the Oxford Advanced Learners' Dictionary. . This is a pronunciation dictionary that helps festival pronounce English text, with a British accent. Package: festvox-en1 Description-md5: 73113a75020377d4b66a5e0015a9b80f Description-en: mbrola-en1 voice support for festival This package lets festival make use of the non-free mbrola-en1 sample-based voice instead of its own synthesis voices. Package: festvox-us1 Description-md5: 4a88a1475767423d39383d867ca65c2e Description-en: mbrola-us1 voice support for festival This package lets festival make use of the non-free mbrola-us1 sample-based voice instead of its own synthesis voices. Package: festvox-us2 Description-md5: 0d83ae1061b439701a1d7bd8443b18ca Description-en: mbrola-us2 voice support for festival This package lets festival make use of the non-free mbrola-us2 sample-based voice instead of its own synthesis voices. Package: festvox-us3 Description-md5: 9f9fed2c3cca5ca1d4d49b1a99c0e678 Description-en: mbrola-us3 voice support for festival This package lets festival make use of the non-free mbrola-us3 sample-based voice instead of its own synthesis voices. Package: firmware-qcom-dragonboard845c Description-md5: 3507088ed94312d44b4d5f5207d7374f Description-en: Binary firmware for various Qualcomm drivers used on Dragonboard 845c This package contains the binary firmware for GPU, USB, Venus, DSP hardware coprocessors found on SDM845, which is the main SoC on the Dragonboard 845c. Package: firmware-qcom-rb5 Description-md5: 0abf8cbedf59bff7af0b4696550319c7 Description-en: Binary firmware for various Qualcomm drivers used on Robotics RB5 This package contains the binary firmware for the SM8250, which is the main SoC on the Robotics RB5. Package: gitaly-installer Description-md5: a0db51942a7c98ec821bffaf2b5a8789 Description-en: Git RPC service for handling all the git calls made by GitLab Gitaly makes the git data storage tier of large GitLab instances fast. This is achieved by moving git operations as close to the data as possible and Optimizing git services using caching. Gitaly is a core service of gitlab. This package installs Gitaly from pre-built binaries from Gitlab artifacts. Package: gitlab-common Description-md5: ccb3bd8dfffd14a6e75d53315368861a Description-en: git powered software platform to collaborate on code (common) gitlab provides web based interface to host source code and track issues. It allows anyone for fork a repository and send merge requests. Code review is possible using merge request workflow. Using groups and roles project access can be controlled. . This package includes configurations common to gitlab and gitaly. Package: libblimps3 Description-md5: a1ad50ab461eca726e0a4e957f1a2ffa Description-en: blocks database improved searcher library BLIMPS (BLocks IMProved Searcher) is a searching tool that scores a protein sequence against blocks or a block against sequences. . This package provides the shared library. Package: libblimps3-dev Description-md5: 4d04b155f279229d68533b0ec59a8e4f Description-en: blocks database improved searcher library (development) BLIMPS (BLocks IMProved Searcher) is a searching tool that scores a protein sequence against blocks or a block against sequences. . This package provides the library development headers and the static library. Package: libgloo-cuda-0 Description-md5: 11fe5b4bb9197845522ecac87176ae15 Description-en: Collective communications library (shared object) 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. . Transport of data between participating machines is abstracted so that IP can be used at all times, or InifiniBand (or RoCE) when available. In the latter case, if the InfiniBand transport is used, GPUDirect can be used to accelerate cross machine GPU-to-GPU memory transfers. . Where applicable, algorithms have an implementation that works with system memory buffers, and one that works with NVIDIA GPU memory buffers. In the latter case, it is not necessary to copy memory between host and device; this is taken care of by the algorithm implementations. . This package ships the shared object for Gloo. Package: libgloo-cuda-dev Description-md5: 1eb5d67397fdb9a8162c5c082d1e6d78 Description-en: Collective communications library (development files) 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. . Transport of data between participating machines is abstracted so that IP can be used at all times, or InifiniBand (or RoCE) when available. In the latter case, if the InfiniBand transport is used, GPUDirect can be used to accelerate cross machine GPU-to-GPU memory transfers. . Where applicable, algorithms have an implementation that works with system memory buffers, and one that works with NVIDIA GPU memory buffers. In the latter case, it is not necessary to copy memory between host and device; this is taken care of by the algorithm implementations. . This package ships the development files. Package: libnvidia-nscq-580 Description-md5: 06d90fb68ae67655c00815a4db03a64e Description-en: NVSwitch Configuration and Query library NVIDIA NVSwitch Configuration and Query (NSCQ) library provides a stable driver API used by DCGM for monitoring NVSwitch devices. Package: libparmetis-dev Description-md5: 839c770f477cb92f6af09275d807c484 Description-en: Parallel Graph Partitioning and Sparse Matrix Ordering Libs: Devel ParMetis computes minimal-cut partitions of graphs and meshes in parallel, and orders variables for minimal fill when using direct solvers for sparse matrices. It does all this in parallel, and also can efficiently re-partition a graph or mesh whose connectivity has changed. . This package contains files needed to develop programs using ParMetis. Package: libparmetis4.0 Description-md5: 17a6686f47a3b63f4328881bffab697b Description-en: Parallel Graph Partitioning and Sparse Matrix Ordering Shared Libs ParMetis computes minimal-cut partitions of graphs and meshes in parallel, and orders variables for minimal fill when using direct solvers for sparse matrices. It does all this in parallel, and also can efficiently re-partition a graph or mesh whose connectivity has changed. . This package contains the ParMetis shared libraries. Package: libsocl-contrib-1.4-1t64 Description-md5: 4a69ce3f02f2cf01466ed8b9a22d8758 Description-en: Task scheduler for heterogeneous multicore machines StarPU is a runtime system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs or on Cell's SPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible. . This package contains an OpenCL-compatible library interface to StarPU. This "contrib" version is built against CUDA. Package: libstarpu-contrib-1.4-8 Description-md5: f0ca811d02d6bbb6c830781bf9811bff Description-en: Task scheduler for heterogeneous multicore machines StarPU is a runtime system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs or on Cell's SPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible. . This package contains the main StarPU library This "contrib" version is built against CUDA. Package: libstarpu-contrib-dev Description-md5: e7f42763fbe9c087a6defa203190f56e Description-en: Task scheduler for heterogeneous multicore machines - dev StarPU is a runtime system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs or on Cell's SPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible. . This package contains development headers and libraries. This "contrib" version is built against CUDA. Package: libstarpu-contrib-openmp-llvm-1.4-1t64 Description-md5: 0c4cf9967f5b66c184481dbb23c8faa6 Description-en: Task scheduler for heterogeneous multicore machines StarPU is a runtime system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs or on Cell's SPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible. . This package contains the runtime support for LLVM OpenMP. This "contrib" version is built against CUDA. Package: libstarpu-contribfft-1.4-1t64 Description-md5: 1079179920c93a735ab89566a3855d36 Description-en: Task scheduler for heterogeneous multicore machines StarPU is a runtime system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs or on Cell's SPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible. . This package contains a hybrid CPU+GPU FFT library. This "contrib" version is built against CUDA. Package: libstarpu-contribmpi-1.4-3t64 Description-md5: 4d3ce5602db3c9fbd91b7e19dcc14e2a Description-en: Task scheduler for heterogeneous multicore machines StarPU is a runtime system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs or on Cell's SPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible. . This package contains MPI extensions for StarPU. This "contrib" version is built against CUDA. Package: libstarpu-contribrm-1.4-1t64 Description-md5: 3008682b6502a417f1d5f1b0170499a0 Description-en: Task scheduler for heterogeneous multicore machines StarPU is a runtime system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs or on Cell's SPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible. . This package contains the resource management library. This "contrib" version is built against CUDA. Package: meshroom Description-md5: c666ef103ddfbb5c9ed2a6f7d6514c91 Description-en: open-source, node-based visual programming framework Meshroom is a flexible toolbox for creating, managing, and executing complex data processing pipelines. . Meshroom uses a nodal system where each node represents a specific operation, and output attributes can seamlessly feed into subsequent steps. When a node’s attribute is modified, only the affected downstream nodes are invalidated, while cached intermediate results are reused to minimize unnecessary computation. . Meshroom supports both local and distributed execution, enabling efficient parallel processing on render farms. It also includes interactive widgets for visualizing images and 3D data. Package: mumax3 Description-md5: 31b32b8b874fff7a3226df456e85a895 Description-en: GPU-accelerated micromagnetic simulator mumax3 is a GPU-accelerated micromagnetic simulation program developed at the DyNaMat group of Prof. Van Waeyenberge at Ghent University. . A speed-up of the order of 100x compared to CPU-based simulations can easily be reached, even with relatively inexpensive gaming GPUs. Additionally, the software is optimized for low memory use and can handle about 16 million FD cells with 2GB of GPU RAM. . Features: - Landau-Lifshitz micromagnetic formalism - Magnetostatic field - Heisenberg exchange - Arbitrary inter-region exchange like RKKY coupling - Dzyaloshinskii-Moriya interaction - Spin-transfer torque (Zhang-Li and Slonczewski) - Uniaxial and cubic magnetocrystalline anisotropy - Thermal fluctuations (Brown) - Voronoi tessellation - Time- and space dependent material parameters - Arbitrary complex excitation (field, current) - Simulation window can automatically follow a moving domain wall - Edge charges can be removed to simulate an infinitely long geometry - Optional 1D, 2D or 3D periodic boundary conditions Package: nvidia-fabricmanager-580 Description-md5: 3d49e1346de8fc10beaca066b0eb5df0 Description-en: Fabric Manager for NVSwitch based systems. Fabric Manager for NVIDIA NVSwitch based systems. Package: nvidia-fabricmanager-dev-580 Description-md5: 565605cc6a127d423c88c38e5c2f9a26 Description-en: Fabric Manager API headers and associated library Fabric Manager API headers and associated library Package: nvidia-imex-580 Description-md5: f7a765b2a95d4cabcc9695466d5f6cc8 Description-en: IMEX Manager for NVIDIA based systems. IMEX Manager for NVIDIA systems. Package: parmetis-doc Description-md5: 55cc39b179c0b5b2dedead6fc6a8c34f Description-en: Parallel Graph Partitioning and Sparse Matrix Ordering Lib - Docs ParMetis computes minimal-cut partitions of graphs and meshes in parallel, and orders variables for minimal fill when using direct solvers for sparse matrices. It does all this in parallel, and also can efficiently re-partition a graph or mesh whose connectivity has changed. . This package contains the documentation and example files. Package: parmetis-test Description-md5: b9b53f52a3b7e53d03b5260911e600a9 Description-en: Parallel Graph Partitioning and Sparse Matrix Ordering Tests ParMetis computes minimal-cut partitions of graphs and meshes in parallel, and orders variables for minimal fill when using direct solvers for sparse matrices. It does all this in parallel, and also can efficiently re-partition a graph or mesh whose connectivity has changed. . This package contains programs which test the ParMetis libraries using files in the parmetis-doc package's examples directory. Package: python-pycuda-doc Description-md5: 4b4f2b1e8b32879eefe98c99f3a598ba Description-en: module to access Nvidia‘s CUDA computation API (documentation) PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains HTML documentation and example scripts. Package: python3-pycuda Description-md5: 4f446cb70e3ba6723eaae62a94efb36c Description-en: Python 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules. Package: sabnzbdplus Description-md5: 9af99c00c3efdc0801f9fda555ddbd78 Description-en: web-based binary newsreader with nzb support SABnzbd+ is a web-based binary newsgrabber written in Python, with support for the popular nzb file format. It greatly simplifies the process of downloading from Usenet, thanks to a friendly web-based, fully customizable user interface and advanced built-in post-processing options including the ability to automatically verify, repair, extract and clean up downloaded posts. . Multiple servers are supported, as well as secure (ssl) connections, ipv6, scheduling, pausing and resuming downloads, queue manipulation, rss feeds, automatic sorting, a download history, email notifications, and custom post-processing scripts. An extensive API allows third party applications to interact with the program. . This package contains the program itself and all interface templates. Package: sift Description-md5: 7788bf12148938f8dc8e4675657ce605 Description-en: predicts if a substitution in a protein has a phenotypic effect SIFT is a sequence homology-based tool that sorts intolerant from tolerant amino acid substitutions and predicts whether an amino acid substitution in a protein will have a phenotypic effect. SIFT is based on the premise that protein evolution is correlated with protein function. Positions important for function should be conserved in an alignment of the protein family, whereas unimportant positions should appear diverse in an alignment. Package: starpu-contrib-examples Description-md5: 11b8c6811b08d3b761c4e2f5fd0e0890 Description-en: Task scheduler for heterogeneous multicore machines - exs StarPU is a runtime system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs or on Cell's SPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible. . This package contains application examples. This "contrib" version is built against CUDA. Package: starpu-contrib-tools Description-md5: 21e204c0915cd3972a9cf41ad059d147 Description-en: Task scheduler for heterogeneous multicore machines - tools StarPU is a runtime system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs or on Cell's SPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible. . This package contains StarPU tools. This "contrib" version is built against CUDA. Package: python3-stravalib Description-md5: 34b256e53c48b4d20eee4d58cf73fb4e Description-en: tool for accessing and downloading Strava data from the Strava V3 web service. Stravalib provides a Client class that supports: * Authenticating with stravalib * Accessing and downloading strava activity, club and profile data * Making changes to account activities