device/cuda/kernel.cpp

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/* SPDX-FileCopyrightText: 2011-2022 Blender Foundation
 *
 * SPDX-License-Identifier: Apache-2.0 */
 
#ifdef WITH_CUDA
 
#  include "device/cuda/kernel.h"
#  include "device/cuda/device_impl.h"
 
CCL_NAMESPACE_BEGIN
 
void CUDADeviceKernels::load(CUDADevice *device)
{
  CUmodule cuModule = device->cuModule;
 
  for (int i = 0; i < (int)DEVICE_KERNEL_NUM; i++) {
    CUDADeviceKernel &kernel = kernels_[i];
 
    /* No mega-kernel used for GPU. */
    if (i == DEVICE_KERNEL_INTEGRATOR_MEGAKERNEL) {
      continue;
    }
 
    const std::string function_name = std::string("kernel_gpu_") +
                                      device_kernel_as_string((DeviceKernel)i);
    cuda_device_assert(device,
                       cuModuleGetFunction(&kernel.function, cuModule, function_name.c_str()));
 
    if (kernel.function) {
      cuda_device_assert(device, cuFuncSetCacheConfig(kernel.function, CU_FUNC_CACHE_PREFER_L1));
 
      cuda_device_assert(
          device,
          cuOccupancyMaxPotentialBlockSize(
              &kernel.min_blocks, &kernel.num_threads_per_block, kernel.function, NULL, 0, 0));
    }
    else {
      LOG(ERROR) << "Unable to load kernel " << function_name;
    }
  }
 
  loaded = true;
}
 
const CUDADeviceKernel &CUDADeviceKernels::get(DeviceKernel kernel) const
{
  return kernels_[(int)kernel];
}
 
bool CUDADeviceKernels::available(DeviceKernel kernel) const
{
  return kernels_[(int)kernel].function != nullptr;
}
 
CCL_NAMESPACE_END
 
#endif /* WITH_CUDA */