Is it possible to use ordinary C++ functions/functors or C++11 lambdas with Boost.Compute?
Unfortunately no. OpenCL relies on having C99 source code available at run-time in order to execute code on the GPU. Thus compiled C++ functions or C++11 lambdas cannot simply be passed to the OpenCL environment to be executed on the GPU.
This is the reason why I wrote the Boost.Compute lambda library. Basically it takes C++ lambda expressions (e.g. _1 * sqrt(_1) + 4) and transforms them into C99 source code fragments (e.g. “input[i] * sqrt(input[i]) + 4)”) which are then passed to the Boost.Compute STL-style algorithms for execution. While not perfect, it allows the user to write code closer to C++ that still can be executed through OpenCL.
Also check out the BOOST_COMPUTE_FUNCTION() macro which allows OpenCL functions to be defined inline with C++ code. An example can be found in the monte_carlo example code.
I find this to be a serious (killer) downside.
Are there any examples/FAQ about launching kernels that call member functions or about using Boost.Compute within class hierarchies?
Even a minimal example where the data is a class member e.g. vector and the kernel uses one or two member functions would be very much appreciated.
The std proposal for a parallel algorithms library, TBB, C++AMP, and Thrust seem to be a better fit for a "C++ interface to multi-core CPU and GPGPU computing platforms" than any OpenCL based library I've seen (OpenCL, Bolt, VexCL, Boost.Compute). OpenCL and C++ seem to not be made for each other. OpenCL is just FUBAR without extra compiler support like C++AMP or OpenACC.
On Monday, March 17, 2014 1:03:57 AM UTC+1, Kyle Lutz wrote:I'm proud to announce the initial release (version 0.1) of
Boost.Compute! It is available on GitHub [1] and instructions for
using the library can be found in the documentation [2].
Boost.Compute is a GPGPU and parallel-programming library based on
OpenCL. It provides an STL-like API and implements many common
containers (e.g. vector<T>, array<T, N>) as well as many common
algorithms (e.g. sort(), accumulate(), transform()). A full list can
be found in the header reference [3].
I hope to propose Boost.Compute for review in the next few months but
for I'm looking for more wide-spread testing and feedback from the
Boost community (please note the FAQ [4] and design rationale [5]
where I hope to have answered some common questions).
Thanks,
Kyle
[1] https://github.com/kylelutz/compute
[2] http://kylelutz.github.io/compute/
[3] http://kylelutz.github.io/compute/compute/reference.html
[4] http://kylelutz.github.io/compute/boost_compute/faq.html
[5] http://kylelutz.github.io/compute/boost_compute/design.html
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