Looks interesting. One question: what support does the library provide to
orchestrate parallelism, i.e. doing useful work while the GPGPU is executing a kernel? Do you have something like:
int main() { // create data array on host int host_data[] = { 1, 3, 5, 7, 9 };
// create vector on device boost::compute::vector<int> device_vector(5);
// copy from host to device future<void> f = boost::compute::copy_async(host_data, host_data + 5, device_vector.begin());
// do other stuff
f.get(); // wait for transfer to be done
return 0; }
?
All libraries I have seen so far assume that the CPU has to idle while waiting for the GPU, is yours different?
Yes. The library allows for asynchronous computation and the API is almost exactly the same as your proposed example.
See this example: http://kylelutz.github.com/compute/boost_compute/advanced_topics.html# boos t_compute.advanced_topics.asynchronous_operations
That's excellent (sorry I have not seen this in the docs before). However I think it's not a good idea to create your own futures. This does not scale well nor does it compose with std::future or boost::future. Is there a way to use whatever futures (or more specifically, threading implementation) the user decides to use?
Also, do you have asynchronous versions for all algorithms? Regards Hartmut --------------- http://boost-spirit.com http://stellar.cct.lsu.edu