cupla alternatives and similar libraries
Based on the "Concurrency" category.
Alternatively, view cupla alternatives based on common mentions on social networks and blogs.
-
Thrust
A parallel algorithms library which resembles the C++ Standard Template Library (STL). [Apache2] -
ck
Concurrency primitives, safe memory reclamation mechanisms and non-blocking data structures. [BSD] -
HPX
A general purpose C++ runtime system for parallel and distributed applications of any scale. [Boost] -
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] -
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 -
CUB
CUB provides state-of-the-art, reusable software components for every layer of the CUDA programming mode. [New BSD]
Get performance insights in less than 4 minutes
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest. Visit our partner's website for more details.
Do you think we are missing an alternative of cupla or a related project?
Popular Comparisons
|
SaaSHub - Software Alternatives and Reviews
Sponsored
www.saashub.com
|
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.