Naive Matrix TransposeOur first transpose kernel looks very similar to the copy kernel. The only difference is that the indices for odata are swapped.__global__ void transposeNaive(float *odata, const float *idata) { int x = blockIdx.x * TILE_DIM + threadIdx.x; int y = blockIdx.y ...
An Efficient Matrix Transpose in CUDA C++ Finite Difference Methods in CUDA C++, Part 1 Finite Difference Methods in CUDA C++, Part 2 Accelerated Ray Tracing in One Weekend with CUDA There is also a series ofCUDA Fortran postsmirroring the above, starting withAn Easy Introduction to CUDA Fortra...
Continue the discussion atforums.developer.nvidia.com 136 more replies Participants Developing Accelerated Code with Standard Language Parallelism NEW DLI Online Courses for Hands-on Training in Accelerated Computing New DLI Training: Accelerating CUDA C++ Applications with Multiple GPUs ...
For the particular step given in the illustration, Levenberg–Marquardt is the only method that works. This, of course, is not always the case. We wanted to highlight that using the exact Hessian matrix with Newton's method does not at all guarantee an efficient step. Show moreView chapter...
EfficientRep Backbone 多分支的网络(ResNet,DenseNet,GoogLeNet)相比单分支(ImageNet,VGG)的通常能够有更好的分类性能。但是,它通常伴随着并行性的降低,并导致推理延迟的增加。相反,像VGG这样的普通单路径网络具有高并行性和较少内存占用的优点,从而导致更高的推理效率。
However, opaque arrays dictate the cost of copying data into it, which should be kept in mind. Thus, the most efficient way to specify a data array from the application is to created a shared data array, which is done with OSPData ospNewSharedData(const void *sharedData, OSPDataType, ...
Fill in the execution configuration parameters for the design. E. Analyze the pros and cons of each kernel design above. 2. A matrix–vector multiplication takes an input matrix B and a vector C and produces one output vector A. Each element of the output vector A is the dot product of...
Sparse and dense vectors are distributed along all processors. This is very space efficient and provides good load balance for SpMSV (sparse matrix-sparse vector multiplication). New since version 1.6: Connected components in distributed memory, found in Applications/CC.h [15,16], compile with "...
The fast Fourier transform is an efficient algorithm for computing discrete Fourier transforms of complex or real-valued data sets. The NVIDIA CUDA fast Fourier transform library (cuFFT) provides a simple interface for computing FFTs up to 10× faster. cuFFT provides familiar API similar to FFTW ...
First on the matrix form itself, the menus provide many standard matrix techniques including the ability to transpose, row reduce, set the ij entries by formula, and calculate terms like rank or determinant. In addition, the math library provides a Matrices button on the toolbar that can be ...