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: dynare-matlab Description-md5: 90e8fcabb135fcf60e09f0be51774946 Description-en: MATLAB support for Dynare Dynare is a software platform for handling a wide class of economic models, in particular dynamic stochastic general equilibrium (DSGE) and overlapping generations (OLG) models. The models solved by Dynare include those relying on the rational expectations hypothesis, wherein agents form their expectations about the future in a way consistent with the model. But Dynare is also able to handle models where expectations are formed differently: on one extreme, models where agents perfectly anticipate the future; on the other extreme, models where agents have limited rationality or imperfect knowledge of the state of the economy and, hence, form their expectations through a learning process. In terms of types of agents, models solved by Dynare can incorporate consumers, productive firms, governments, monetary authorities, investors and financial intermediaries. Some degree of heterogeneity can be achieved by including several distinct classes of agents in each of the aforementioned agent categories. . Dynare offers a user-friendly and intuitive way of describing these models. It is able to perform simulations of the model given a calibration of the model parameters and is also able to estimate these parameters given a dataset. In practice, the user will write a text file containing the list of model variables, the dynamic equations linking these variables together, the computing tasks to be performed and the desired graphical or numerical outputs. . This package is only useful to users having MATLAB installed on their machine. It contains the source of the MEX files and will recompile them using the existing MATLAB installation. 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: 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: matlab-jsonlab Description-md5: 2aeaa0d5e0504122421ec9a78a14a1a6 Description-en: native JSON/UBJSON/MassagePack encoder/decoder for MATLAB JSONLab is a free and open-source implementation of a JSON/UBJSON/MessagePack encoder and a decoder in the native MATLAB language. It can be used to convert a MATLAB data structure (array, struct, cell, struct array and cell array) into JSON/UBJSON formatted string, or decode a JSON/UBJSON/MessagePack file into MATLAB data. JSONLab supports both MATLAB and GNU Octave (a free MATLAB clone). JSONLab is now the official reference implementation for the JData Specification (Draft 3) - the foundation of the OpenJData Project (http://openjdata.org). . This package provides support for MATLAB. Package: matlab-zmat Description-md5: d2c1037544212de68d4fca45fdc1f59b Description-en: in-memory data compression for MATLAB ZMat is a portable mex function to enable zlib/gzip/lzma/lzip/lz4/lz4hc based data compression/decompression and base64 encoding/decoding support in MATLAB and GNU Octave. . This package builds MATLAB bindings for zmat at installation time. Note that this package depends on MATLAB -- a commercial software that needs to be obtain and installed separately. 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: 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.