svm_vector(const backend::command_queue &q, size_t n) : n(n), q(q), p(NULL) {
q.context().set_current();
CUdeviceptr dptr;
cuda_check( cuMemAllocManaged(&dptr, n * sizeof(T), CU_MEM_ATTACH_GLOBAL) );
p = reinterpret_cast<T*>(static_cast<size_t>(dptr));
}
inline kernel_call transpose_kernel(
const backend::command_queue &queue, size_t width, size_t height,
const backend::device_vector<T2> &in,
const backend::device_vector<T2> &out
)
{
backend::source_generator o;
kernel_common<T>(o, queue);
// determine max block size to fit into local memory/workgroup
size_t block_size = 128;
{
#ifndef VEXCL_BACKEND_CUDA
cl_device_id dev = backend::get_device_id(queue);
cl_ulong local_size;
size_t workgroup;
clGetDeviceInfo(dev, CL_DEVICE_LOCAL_MEM_SIZE, sizeof(cl_ulong), &local_size, NULL);
clGetDeviceInfo(dev, CL_DEVICE_MAX_WORK_GROUP_SIZE, sizeof(size_t), &workgroup, NULL);
#else
const auto local_size = queue.device().max_shared_memory_per_block();
const auto workgroup = queue.device().max_threads_per_block();
#endif
while(block_size * block_size * sizeof(T) * 2 > local_size) block_size /= 2;
while(block_size * block_size > workgroup) block_size /= 2;
}
// from NVIDIA SDK.
o.kernel("transpose").open("(")
.template parameter< global_ptr<const T2> >("input")
.template parameter< global_ptr< T2> >("output")
.template parameter< cl_uint >("width")
.template parameter< cl_uint >("height")
.close(")").open("{");
o.new_line() << "const size_t global_x = " << o.global_id(0) << ";";
o.new_line() << "const size_t global_y = " << o.global_id(1) << ";";
o.new_line() << "const size_t local_x = " << o.local_id(0) << ";";
o.new_line() << "const size_t local_y = " << o.local_id(1) << ";";
o.new_line() << "const size_t group_x = " << o.group_id(0) << ";";
o.new_line() << "const size_t group_y = " << o.group_id(1) << ";";
o.new_line() << "const size_t target_x = local_y + group_y * " << block_size << ";";
o.new_line() << "const size_t target_y = local_x + group_x * " << block_size << ";";
o.new_line() << "const bool range = global_x < width && global_y < height;";
// local memory
{
std::ostringstream s;
s << "block[" << block_size * block_size << "]";
o.smem_static_var(type_name<T2>(), s.str());
}
// copy from input to local memory
o.new_line() << "if(range) "
<< "block[local_x + local_y * " << block_size << "] = input[global_x + global_y * width];";
// wait until the whole block is filled
o.new_line().barrier();
// transpose local block to target
o.new_line() << "if(range) "
<< "output[target_x + target_y * height] = block[local_x + local_y * " << block_size << "];";
o.close("}");
backend::kernel kernel(queue, o.str(), "transpose");
kernel.push_arg(in);
kernel.push_arg(out);
kernel.push_arg(static_cast<cl_uint>(width));
kernel.push_arg(static_cast<cl_uint>(height));
// range multiple of wg size, last block maybe not completely filled.
size_t r_w = (width + block_size - 1) / block_size;
size_t r_h = (height + block_size - 1) / block_size;
kernel.config(backend::ndrange(r_w, r_h), backend::ndrange(block_size, block_size));
std::ostringstream desc;
desc << "transpose{"
<< "w=" << width << "(" << r_w << "), "
<< "h=" << height << "(" << r_h << "), "
<< "bs=" << block_size << "}";
return kernel_call(false, desc.str(), kernel);
}
size_t preferred_work_group_size_multiple(const backend::command_queue &q) const {
return q.device().warp_size();
}
