cupla alternatives and similar libraries
Based on the "Concurrency" category
-
Thrust
A parallel algorithms library which resembles the C++ Standard Template Library (STL). [Apache2] -
HPX
A general purpose C++ runtime system for parallel and distributed applications of any scale. [Boost] -
ck
Concurrency primitives, safe memory reclamation mechanisms and non-blocking data structures. [BSD] -
moderngpu
moderngpu is a productivity library for general-purpose computing on GPUs. It is a header-only C++ library written for CUDA. The unique value of the library is in its accelerated primitives for solving irregularly parallel problems. [FreeBSD & Copyright, Sean Baxter] -
CUB
CUB provides state-of-the-art, reusable software components for every layer of the CUDA programming mode. [New BSD] -
continuable
Async C++14 platform independent continuation chainer providing light and allocation aware futures -
SPSCQueue.h
A bounded single-producer single-consumer wait-free and lock-free queue written in C++11 -
Easy Creation of GnuPlot Scripts from C++
A simple C++17 library to quickly plot your data with GnuPlot
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README
cupla - C++ User interface for the Platform independent Library Alpaka
[cupla Release](doc/logo/cupla_logo_320x210.png)
cupla [qχɑpˈlɑʔ] is a simple user interface for the platform independent parallel kernel acceleration library alpaka. It follows a similar concept as the NVIDIA® CUDA® API by providing a software layer to manage accelerator devices. alpaka is used as backend for cupla.
Please keep in mind that a first, ["find & replace"](doc/PortingGuide.md) port from CUDA to cupla(x86) will result in rather bad performance. In order to reach decent performance on x86 systems you just need to add the alpaka [element level](doc/TuningGuide.md) to your kernels.
(Read as: add some tiling to your CUDA kernels by letting the same thread compute a fixed number of elements (N=4..16) instead of just computing one element per thread. Also, make the number of elements in your tiling a compile-time constant and your CUDA code (N=1) will just stay with the very same performance while adding single-source performance portability for, e.g., x86 targets).
Software License
cupla is licensed under LGPLv3 or later.
For more information see [LICENSE.md](LICENSE.md).
Dependencies
- cmake 3.11.4
- alpaka 0.4.0
- alpaka is loaded as
git subtreewithin cupla, see [INSTALL.md](INSTALL.md)
- alpaka is loaded as
Usage
- See our notes in [INSTALL.md](INSTALL.md).
- Checkout the [guide](doc/PortingGuide.md) how to port your project.
- Checkout the [tuning guide](doc/TuningGuide.md) for a step further to performance portable code.
[cupla can be used as a header-only library and without the CMake build system](doc/ConfigurationHeader.md)
Authors
Maintainers* and Core Developers
Former Members, Contributions and Thanks
Trademarks Disclaimer
All product names and trademarks are the property of their respective owners. CUDA® is a trademark of the NVIDIA Corporation.
*Note that all licence references and agreements mentioned in the cupla README section above
are relevant to that project's source code only.