http://stackoverflow.com/questions/12074281/why-opencv-gpu-codes-is-slower-than-cpu http://answers.opencv.org/question/1670/huge-time-to-upload-data-to-gpu/#1676 The first gpu function call is always takes more time, because CUDA initialize context for device. The following calls will be fa...
frame++; } Measuring only theapply()-calls, the GPU version is about 20x slower thancv::morphologyExon the CPU of the Jetson Nano (0.07svs.1.5sfor a single frame). nvprofshows, that most of the time is spent doingcudaDeviceSynchronize(this is for the whole program doing more things tha...
谢谢paleonix的建议。事实上，你拥有的数据越多，你就越能看到CPU和GPU之间的加速差异。此外，删除cuda...
ax.set(title="Performance relative to GPU version", ylabel="Times slower") ax.yaxis.set_major_formatter(ticker.StrMethodFormatter("{x:.0f}x")) 上图以GPU版本为基准,可以看到NumPy版本至少要慢40倍,而我们的CPU版本要慢数千倍。GPU可以在几毫秒内处理这570万个字符数据集,而CPU解决方案需要超过10秒。
ax.set(title="Performance relative to GPU version", ylabel="Times slower") ax.yaxis.set_major_formatter(ticker.StrMethodFormatter("x")) 上图以GPU版本为基准,可以看到NumPy版本至少要慢40倍,而我们的CPU版本要慢数千倍。GPU可以在几毫秒内处理这570万个字符数据集,而CPU解决方案需要超过10秒。这意味着...
I did an exercise which consists in displaying the prime numbers less than N. For each code I commented out the last display loop to compare only the calculation times. The Makefile : all: sum sum_cpu nothing: g++ -O3 -std=c++17 -o premier.exe premier.cpp -Wall cpu:...
Applications that constantly allocate and free memory too often may find that the allocationcalls tend to get slower over time up to a limit. This is typically expected due to the nature ofreleasing memory back to the operating system for its own use. For best performance in thisregard, we ...
Graphics processing unit (GPU) Parallel computing Neural networks are embarrassingly parallel Convolution example Nvidia hardware (GPU) and software (CUDA) PyTorch comes with CUDA Using CUDA with PyTorch GPU can be slower than CPU GPGPU computing Tensors are up nextCourse...
ax.set(title="Performance relative to GPU version", ylabel="Times slower") ax.yaxis.set_major_formatter(ticker.StrMethodFormatter("{x:.0f}x")) 上图以GPU版本为基准,可以看到NumPy版本至少要慢40倍,而我们的CPU版本要慢数千倍。GPU可以在几毫秒内处理这570万个字符数据集,而CPU解决方案需要超过10秒...
Use signed integers rather than unsigned integers as loop counters. Use the fast math library whenever speed trumps precision. Prefer faster, more specialized math functions over slower, more general ones when possible. Low Priority Use zero-copy operations on integrated GPUs for CUDA Toolkit version...