On 3/2/2013 4:25 PM, Kyle Lutz wrote:
Hi everyone,
A while back I posted a message asking for interest in a GPGPU computing library and the response seemed positive. I've been slowly working on it for the last few months and it has finally reached a usable state. I've made an initial release on GitHub (details below) and would like to get feedback from the community.
The Boost Compute library provides a partial implementation of the C++ standard library for GPUs and multi-core CPUs. It includes common containers (vector<T>, flat_set<T>) and standard algorithms (transform, sort, accumulate). It also features a number of extensions including parallel-computing focused algorithms (exclusive_scan, scatter, reduce) along with a number of fancy iterators (transform_iterator, permutation_iterator). The library is built around the OpenCL framework which allows it to be portable across many types of devices (GPUs, CPUs, and accelerator cards) from many different vendors (NVIDIA, Intel, AMD).
The source code and documentation are available from the links below.
Code: https://github.com/kylelutz/compute Documentation: http://kylelutz.github.com/compute Bug Tracker: https://github.com/kylelutz/compute/issues
I've tested the library with GCC 4.7 and Clang 3.3 on both NVIDIA GPUs and Intel CPUs. However, I would not yet consider the library production-ready. Most of my time has been devoted to reaching a solid and well-tested API rather than on performance. Over time this will improve.
Feel free to send any questions, comments or feedback.
Thanks, Kyle
_______________________________________________ Unsubscribe & other changes: http://lists.boost.org/mailman/listinfo.cgi/boost
How does it compare to VexCL (https://github.com/ddemidov/vexcl), Bolt (http://developer.amd.com/tools/heterogeneous-computing/amd-accelerated-paral...) and Thrust (https://developer.nvidia.com/thrust)? A comparison would be nice. Moreover, why not piggy-back on the libraries that are already available (and they probably have better optimizations in place) and simply write a nice wrapper around them (and maybe, crazy idea, allow a single codebase to use both AMD and nVidia GPUs at the same time)?