gpu

问题 I would like to know how to obtain the total number of CUDA Cores in my GPU using Python, Numba and cudatoolkit. 回答1: Most of what you need can be found by combining the information in this answer along with the information in this answer. We'll use the first answer to indicate how to get the device compute capability and also the number of streaming multiprocessors. We'll use the second answer (converted to python) to use the compute capability to get the "core" count per SM, then multiply

问题 I would like to know how to obtain the total number of CUDA Cores in my GPU using Python, Numba and cudatoolkit. 回答1: Most of what you need can be found by combining the information in this answer along with the information in this answer. We'll use the first answer to indicate how to get the device compute capability and also the number of streaming multiprocessors. We'll use the second answer (converted to python) to use the compute capability to get the "core" count per SM, then multiply

问题 I am trying to make a simple GPU offloading program using openMP. However, when I try to offload it still runs on the default device, i.e. my CPU. I have installed a compiler, g++ 7.2.0 that has CUDA support (is in on a cluster that I use). When I run the below code it shows me that it can see the 8 GPUs but when I try to offload it says that it is still on the CPU. #include <omp.h> #include <iostream> #include <stdio.h> #include <math.h> #include <algorithm> #define n 10000 #define m 10000

问题 I am trying to make a simple GPU offloading program using openMP. However, when I try to offload it still runs on the default device, i.e. my CPU. I have installed a compiler, g++ 7.2.0 that has CUDA support (is in on a cluster that I use). When I run the below code it shows me that it can see the 8 GPUs but when I try to offload it says that it is still on the CPU. #include <omp.h> #include <iostream> #include <stdio.h> #include <math.h> #include <algorithm> #define n 10000 #define m 10000

问题 Lately I've been trying to get into programming for GPUs in Python using the Numba library. I have been reading up on it on their website using the tutorial there and currently I'm stuck on their example, which can be found here: https://numba.pydata.org/numba-doc/latest/cuda/examples.html. I'm attempting to generalize the example for the fast matrix multiplication a bit (which is of the form A*B=C). When testing I noticed that matrices with dimensions that are not perfectly divisible by the

问题 I know I can access the current GPU using torch.cuda.current_device() , but how can I get a list of all the currently available GPUs? 回答1: You can list all the available GPUs by doing: >>> import torch >>> available_gpus = [torch.cuda.device(i) for i in range(torch.cuda.device_count())] >>> available_gpus [<torch.cuda.device object at 0x7f2585882b50>] 来源： https://stackoverflow.com/questions/64776822/how-do-i-list-all-currently-available-gpus-with-pytorch

问题 I know I can access the current GPU using torch.cuda.current_device() , but how can I get a list of all the currently available GPUs? 回答1: You can list all the available GPUs by doing: >>> import torch >>> available_gpus = [torch.cuda.device(i) for i in range(torch.cuda.device_count())] >>> available_gpus [<torch.cuda.device object at 0x7f2585882b50>] 来源： https://stackoverflow.com/questions/64776822/how-do-i-list-all-currently-available-gpus-with-pytorch

问题 Today I started working with CUDA and GPU processing. I found this tutorial: https://www.geeksforgeeks.org/running-python-script-on-gpu/ Unfortunately my first attempt to run gpu code failed: from numba import jit, cuda import numpy as np # to measure exec time from timeit import default_timer as timer # normal function to run on cpu def func(a): for i in range(10000000): a[i]+= 1 # function optimized to run on gpu @jit(target ="cuda") def func2(a): for i in range(10000000): a[i]+= 1 if _

问题 Is there a way to import the XGBoost with GPU support on Google Collab? At last, I tried yesterday and the loaded XGBoost does not have the GPU support. You could reach the notebook from this link: https://colab.research.google.com/drive/1YxCw7eVHZDrbVBFqSN0-t1mZ-fBJJI7h And the error is: XGBoostError: b'[19:15:28] src/learner.cc:151: XGBoost version not compiled with GPU support.\n\nStack trace returned 10 entries:\n[bt] (0) /usr/local/lib/python3.6/dist-packages/xgboost/./lib/libxgboost.so(

问题 I installed Anaconda-Navigatoron Windows 10 and all necessary Nvidia/Cuda packages, created a new environment called tensorflow-gpu-env, updated PATH information, etc. When I run a model (build by using tensorflow.keras ), I see that CPU utilization increases significantly, GPU utilization is 0%, and the model just does not train. I run a couple of tests to make sure how things look: print(tf.test.is_built_with_cuda()) True The above output ('True') looks correct. Another try: from tensorflow