ppkMHD | Kokkos implementation of spectral difference method
kandi X-RAY | ppkMHD Summary
kandi X-RAY | ppkMHD Summary
ppkMHD is a C++ library. ppkMHD has no bugs, it has no vulnerabilities and it has low support. However ppkMHD has a Non-SPDX License. You can download it from GitHub.
ppkMHD stands for Performance Portable Kokkos for Magneto-HydroDynamics (MHD) solvers.
ppkMHD stands for Performance Portable Kokkos for Magneto-HydroDynamics (MHD) solvers.
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Quality
Security
License
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Support
ppkMHD has a low active ecosystem.
It has 12 star(s) with 2 fork(s). There are 2 watchers for this library.
It had no major release in the last 12 months.
There are 0 open issues and 1 have been closed. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of ppkMHD is v1.0.0
Quality
ppkMHD has 0 bugs and 0 code smells.
Security
ppkMHD has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
ppkMHD code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
ppkMHD has a Non-SPDX License.
Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
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ppkMHD releases are available to install and integrate.
Installation instructions, examples and code snippets are available.
It has 484 lines of code, 9 functions and 6 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of ppkMHD
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of ppkMHD
ppkMHD Key Features
No Key Features are available at this moment for ppkMHD.
ppkMHD Examples and Code Snippets
No Code Snippets are available at this moment for ppkMHD.
Community Discussions
No Community Discussions are available at this moment for ppkMHD.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install ppkMHD
A few example builds, with minimal configuration options.
Add variable CXX on the cmake command line to change the compiler (clang++, icpc, pgcc, ....).
Create a build directory, configure and make
Create a build directory, configure and make
To be able to build with CUDA backend, you need to use nvcc_wrapper located in kokkos source (external/kokkos/bin/nvcc_wrapper). nvcc_wrapper is a compiler wrapper arroud NVIDIA nvcc. It is available from Kokkos sources: external/kokkos/bin/nvcc_wrapper. Any Kokkos application target NVIDIA GPUs must be built with nvcc_wrapper.
Create a build directory, configure and make
Please make sure to use a CUDA-aware MPI implementation (OpenMPI or MVAPICH2) built with the proper flags for activating CUDA support. It may happen that eventhough your MPI implementation is actually cuda-aware, cmake find_package macro for MPI does not detect it to be cuda aware. In that case, you can enforce cuda awareness by turning option USE_MPI_CUDA_AWARE_ENFORCED to ON. You don't need to use mpi compiler wrapper mpicxx, cmake should be able to correctly populate MPI_CXX_INCLUDE_PATH, MPI_CXX_LIBRARIES which are passed to all final targets. Example command line to run the application (1 GPU used per MPI task).
Create a build directory, configure and make
Add variable CXX on the cmake command line to change the compiler (clang++, icpc, pgcc, ....).
Create a build directory, configure and make
Create a build directory, configure and make
To be able to build with CUDA backend, you need to use nvcc_wrapper located in kokkos source (external/kokkos/bin/nvcc_wrapper). nvcc_wrapper is a compiler wrapper arroud NVIDIA nvcc. It is available from Kokkos sources: external/kokkos/bin/nvcc_wrapper. Any Kokkos application target NVIDIA GPUs must be built with nvcc_wrapper.
Create a build directory, configure and make
Please make sure to use a CUDA-aware MPI implementation (OpenMPI or MVAPICH2) built with the proper flags for activating CUDA support. It may happen that eventhough your MPI implementation is actually cuda-aware, cmake find_package macro for MPI does not detect it to be cuda aware. In that case, you can enforce cuda awareness by turning option USE_MPI_CUDA_AWARE_ENFORCED to ON. You don't need to use mpi compiler wrapper mpicxx, cmake should be able to correctly populate MPI_CXX_INCLUDE_PATH, MPI_CXX_LIBRARIES which are passed to all final targets. Example command line to run the application (1 GPU used per MPI task).
Create a build directory, configure and make
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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