cupla alternatives and similar libraries
Based on the "Concurrency" category.
Alternatively, view cupla alternatives based on common mentions on social networks and blogs.
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Thrust
DISCONTINUED. [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl -
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ck
Concurrency primitives, safe memory reclamation mechanisms and non-blocking (including lock-free) data structures designed to aid in the research, design and implementation of high performance concurrent systems developed in C99+. -
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SPSCQueue.h
A bounded single-producer single-consumer wait-free and lock-free queue written in C++11 -
continuable
C++14 asynchronous allocation aware futures (supporting then, exception handling, coroutines and connections) -
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Bolt
Bolt is a C++ template library optimized for GPUs. Bolt provides high-performance library implementations for common algorithms such as scan, reduce, transform, and sort. -
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CUB
DISCONTINUED. THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE. -
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BlockingCollection
C++11 thread safe, multi-producer, multi-consumer blocking queue, stack & priority queue class -
Light Actor Framework
DISCONTINUED. Laughably simple yet effective Actor concurrency framework for C++20 -
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Easy Creation of GnuPlot Scripts from C++
A simple C++17 lib that helps you to quickly plot your data with GnuPlot -
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InfluxDB – Built for High-Performance Time Series Workloads
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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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.