Okay, but what GPU calculations take long enough that this information is useful? The vast majority of things you run on the GPU complete in seconds or less. If you have some MATLAB code that is complex, takes a long time to run, and is using the GPU, then whatever instrumentation you...
there is a possibility that your GPU supports parallel.gpu.GPUArray.rand(), and it would be much faster if you construct random numbers within GPU, instead of generate and copy them into GPU
regularly defragment your disk to improve read/write efficiency. Use MATLAB's built-in profiler (profile on; and profile viewer) to get more detailed insights into where exactly the slowdown occurs. This might help pinpoint whether the issue is with file I/O, memory management, or something ...
Open in MATLAB Online In Windows system ThemeCopy system('systeminfo') For my case, matlab online was runing on linux, so here are some details (matlab commands and outputs): ThemeCopy system('lscpu') Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU...
See how to quantize, calibrate, and validate deep neural networks in MATLAB using a white-box approach to make tradeoffs between performance and accuracy, then deploy the quantized DNN to an embedded GPU and an FPGA hardware board.Confusion matrix of the classification rate of a scaled MNIST (...
Afaik it's because on GPU Matlab only supports 2^32 elements in one array. Whats the best way to circumvent this? I know I can disable the GPU completely by settingCUDA_VISIBLE_DEVICESto -1, but I still want to use it in other functions. ...
the biggest advantage of using an apu over a cpu is that it combines both a cpu and a gpu onto a single chip. this means that it can handle both general computing tasks and graphics-intensive tasks without the need for a separate graphics card. this can result in lower costs and power...
In addition to TensorFlow, many other deep learning frameworks rely on CUDA for their GPU support, including Caffe2, Chainer, Databricks, H2O.ai, Keras, MATLAB, MXNet, PyTorch, Theano, and Torch. In most cases they use the cuDNN library for the deep neural network computations. That library...
Julia growing. I think that Julia has great potential to replace C/C++/Python (and of course Fortran) in scientific and technical computing as it matures. The low level performance is excellent. It will be important for it to be adopted as a first-class target language by CPU/GPU vendors...
Using the Linux "top" command, I can see that MATLAB is using a great deal of the CPU, even when MATLAB is idle, i.e. not running any programs. After some time (sometimes 45 minutes, sometimes a couple of hours) of opening a session of MATLAB, MATLAB...