Hello folks,
Last June, I gave an introduction to CUDA programming at the HPC summer school, available here. I have developed a library, named cuda-battery that can help you to program more easily in CUDA. The main idea is to get the C++ standard library accessible inside kernels, so you can use something like vector<int> in the kernel instead of a C-like array int*.
I also wrote a tutorial to this library which should be considered as a follow-up to the HPC school tutorial. It is available here.
Not exactly a challenge, but can be useful to solve some challenges maybe 
Best,
Pierre Talbot
Example
Quick example to transfer a std::vector on CPU to a battery::vector on GPU (notice you don’t need to do any manual memory allocation or deallocation):
#include <vector>
#include "battery/vector.hpp"
#include "battery/unique_ptr.hpp"
#include "battery/allocator.hpp"
using mvector = battery::vector<int, battery::managed_allocator>;
__global__ void kernel(mvector* v_ptr) {
mvector& v = *v_ptr;
// ... Compute on `v` in parallel.
}
int main(int argc, char** argv) {
std::vector<int> v(10000, 42);
// Transfer from CPU vector to GPU vector.
auto gpu_v = battery::make_unique<mvector, battery::managed_allocator>(v);
kernel<<<256, 256>>>(gpu_v.get());
CUDAEX(cudaDeviceSynchronize());
// Transfering the new data to the initial vector.
for(int i = 0; i < v.size(); ++i) {
v[i] = (*gpu_v)[i];
}
return 0;
}