diff options
| author |  Benoit Steiner <benoit.steiner.goog@gmail.com> | 2016-02-01 12:46:32 -0800 |
|---|---|---|
| committer | Benoit Steiner <benoit.steiner.goog@gmail.com> | 2016-02-01 12:46:32 -0800 |
| commit | 6b5dff875e4ba2235f255b7cf0a86b7abed21df0 (patch) | |
| tree | 9dbf00da5c4e1b0a9689422b8faf5448af750416 /unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h | |
| parent | 264f8141f86e84312f0eea9e741d2260ed839890 (diff) | |
Made it possible to limit the number of blocks that will be used to evaluate a tensor expression on a CUDA device. This makesit possible to set aside streaming multiprocessors for other computations.
Diffstat (limited to 'unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h')
| -rw-r--r-- | unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h index d2ab70f2b..df15c6204 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h @@ -220,7 +220,7 @@ EIGEN_DEVICE_FUNC inline void TensorExecutor<Expression, GpuDevice, false>::run( if (needs_assign) { const int block_size = device.maxCudaThreadsPerBlock(); - const int max_blocks = device.getNumCudaMultiProcessors() * device.maxCudaThreadsPerMultiProcessor() / block_size; + const int max_blocks = numext::maxi<int>(device.maxBlocks(), device.getNumCudaMultiProcessors() * device.maxCudaThreadsPerMultiProcessor() / block_size); const Index size = array_prod(evaluator.dimensions()); // Create a least one block to ensure we won't crash if we're called with tensors of size 0. const int num_blocks = numext::maxi<int>(numext::mini<int>(max_blocks, (size + block_size - 1) / block_size), 1); @@ -239,7 +239,7 @@ EIGEN_DEVICE_FUNC inline void TensorExecutor<Expression, GpuDevice, true>::run(c if (needs_assign) { const int block_size = device.maxCudaThreadsPerBlock(); - const int max_blocks = device.getNumCudaMultiProcessors() * device.maxCudaThreadsPerMultiProcessor() / block_size; + const int max_blocks = numext::maxi<int>(device.maxBlocks(), device.getNumCudaMultiProcessors() * device.maxCudaThreadsPerMultiProcessor() / block_size); const Index size = array_prod(evaluator.dimensions()); // Create a least one block to ensure we won't crash if we're called with tensors of size 0. const int num_blocks = numext::maxi<int>(numext::mini<int>(max_blocks, (size + block_size - 1) / block_size), 1); |