Tensor Core operations accelerate matrix math operations cuDNN uses Tensor Core Mirroring), cuDNN routines do not support tensors with negative strides Specified otherwise, all routines will support tensors with overlapping dimensions forįorward-pass input tensors, however, dimensions of the output tensors cannot overlap.Įven though this tensor format supports negative strides (which can be useful for data The same tensor by having the stride of one dimension smaller than the product of theĭimension and the stride of the next dimension. This tensorĭefinition allows, for example, to have some dimensions overlapping each other within Second dimension defines the number of features maps c. The first dimension of the tensor defines the batch size n, and the StrideA integer array defining the stride of each dimension (forĮxample, the number of elements to add to reach the next element from the same Other data with contents with a generic n-D tensor defined with the followingĪ data type (32-bit floating-point, 64 bit-floating point, 16-bitĭimA integer array defining the size of each dimension The cuDNN library describes data holding images, videos and any To use a different device within the same host thread, the application must set the newĭevice to be used by calling cudaSetDevice() and then create anotherĬuDNN context, which will be associated with the new device, by
Unchanged between the corresponding cudnnCreate() andĬudnnDestroy() calls. The device associated with a particular cuDNN context is assumed to remain Thus the cuDNN library calls made with different handles will With different host threads, and in each of those host threads, use a unique cuDNN handle that directs the library calls to the device associated with To explicitly control the library's functioning when using multiple host threads, GPUsįor example, an application can use cudaSetDevice to associate different devices With the library handle using cudnnDestroy(). Once theĪpplication finishes using cuDNN, it can release the resources associated
Passed to every subsequent library function that operates on GPU data. Operations using the GPU, the necessary data is directly accessible from theĪn application using cuDNN must initialize a handle to the library contextīy calling cudnnCreate(). The cuDNN library exposes a host API but assumes that for (optional) interoperability with NVIDIA ® CUDA ® streams. Network implementation and avoids the input/output transposition steps sometimesĬuDNN offers a context-based API that allows for easy multithreading and This flexibility allows easy integration into any neural The fastest GEMM (matrix multiply)-based implementations of such routines while usingĬuDNN features include customizable data layouts, supporting flexibleĭimension ordering, striding, and subregions for the 4D tensors used as inputs and LRN, LCN and batch normalization forward and backwardĬuDNN convolution routines aim for a performance that is competitive with.Arithmetic, mathematical, relational and logical pointwise operations.Neuron activations forward and backward: relu,.
Convolution forward and backward, including cross-correlation.It provides highly tuned implementations of routines arising frequently (cuDNN) is a GPU-accelerated library of primitives for deep NVIDIA ® CUDA ® Deep Neural Network library