ifbranching and comparison? ( i.e. if this 4-5 comparisons per elemet would be 10x slower than the same 4-5 comparisons on CPU it would be a bottleneck ) is there any optimization trick how to minizmizeifbranching and comparison slow down?
I made the current OpenCL version, but actually it is slower than CPU even for GPUs with many ALUs. The reason is that I used a suboptimal strategy: I reimplemented the stack-based recursion mechanism in OpenCL but creating a software stack, which means that all ALUs in a group would wa...
Cool, but who in their right mind would use DirectML if it is slower than existing ones? Speed in ML is the holy grail. Obviously speed is important, but it's a different problem to optimize for one network on a subset of hardware than to support arbitrary networks across a wide range ...
Full reduction is around 14 times slower on CUDA-on-CL than on CUDA. We think this may be because of the absence of the low-level hardware shfl operation. The asymptotic time for zero buffer sizes is double that of CUDA, possibly because of the overhead of additional kernel boilerplate ...
You can try OpenCL Caffe or Keras-PlaidML - it maybe slower and not as optimal as other solutions but have higher chances of making it work. Edit 2021-09-14: there is a new project dlprimitives: https://github.com/artyom-beilis/dlprimitives that has better performance tha...
‣ Known Issues ‣ Some T4 FFTs are slower than expected. ‣ cuFFT may produce incorrect results for real-to-complex and complex-to-real transforms when the total number of elements across all batches in a single execution exceeds 2147483647. ‣ Some cuFFT multi-GPU plans may exhibit ...
cudaMemcpy2DAsync a lot slower than cudaMemcpy normally 6 33 2024 年8 月 22 日 Using Shared Data resting in GPU across multiple programs cuda 4 33 2024 年8 月 8 日 Really slow nvidia-smi, cuda initialization or context creation (L40) 6 59 2024 年8 月 8 日 The interface ...
On future architectures however, mul24 will be slower than 32-bit integer multiplication, so we recommend to provide two kernels, one using mul24 and the other using generic 32-bit integer multiplication, to be called appropriately by the application. Integer division and modulo operation are ...
When using Numba, we have one detail we must pay attention to. Numba is a Just-In-Time compiler, meaning that the functions are only compiled when they are called. Therefore timing the first call of the functionwill also time the compilation stepwhich is in general much slower. We must ...
ZLUDA's GitHub also shows off some individual Geekbench compute scores and comparing OpenCL to this experimental CUDA implementation. While several benchmarks were significantly slower in ZLUDA, the Stereo Matching test was around 50% faster using ZLUDA than it was on OpenCL. That seems pretty pr...