importpycuda.driverascudaimportpycuda.autoinit# 获取CUDA设备数量device_count=cuda.Device.count()print(f"Number of CUDA devices:{device_count}")# 遍历每个CUDA设备并打印其ID和名称foriinrange(device_count):device=cuda.Device(i)print(f"Device{i}:{device.name()}, ID:{device.pci_bus_id()}")...
取上述计算结果中的最小值,可以知道每个SM的active block数量为8 NVIDIA在CUDA SDK中提供的CUDA occupancy calculator也可以完成上面的计算。 CUDA occupancy calculator是一个Excel文件,存储在 SDK的tools目录下。只要在这个Excel表格中输入目标硬件的架构,以及每个block中的线程数量、每个block使用的shared memory数量和每...
threadId = threadIdx.x (一维 block 中 thread 的 id) Id=(gridDim.x∗gridDim.y∗blockIdx.z+gridDim.x∗blockIdx.y+blockIdx.x)∗blockDim.x+threadIdx.xId=(gridDim.x∗gridDim.y∗blockIdx.z+gridDim.x∗blockIdx.y+blockIdx.x)∗blockDim.x+threadIdx.x 1D grid, 2D block b...
CUDA_VISIBLE_DEVICES变量是根据设备ID(CUDA)来选择设备，但是选择的设备不一定是我们想要使用的。可以通过CUDA_DEVICE_ORDER=PCI_BUS_ID来更改索引方式，使得CUDA按照PCI Bus ID来分配索引序号。参考资料：https://www.jianshu.com/p/d10bfee104cc https://www.cnblogs.com/wmr95/articles/8846749.htm...
"CMakeCUDACompilerId.cu" failed. Compiler: /opt/cuda/bin/nvcc Build flags: Id flags: --keep;--keep-dir;tmp -v The output was: 2 #$ _NVVM_BRANCH_=nvvm #$ _SPACE_= #$ _CUDART_=cudart #$ _HERE_=/opt/cuda/bin #$ _THERE_=/opt/cuda/bin ...
[0]); 56 57 // This will pick the best possible CUDA capable device 58 devID = findCudaDevice(argc, (const char **)argv); 59 60 // get device name 61 checkCudaErrors(cudaGetDeviceProperties(&deviceProps, devID)); 62 printf("CUDA device [%s]\n", deviceProps.name); 63 64 int ...
在CUDA中, host和device是两个重要概念, host是指CPU及其内存, device是指GPU及其内存. 典型的CUDA程序...
cudaMalloc((void**)&gpu_block, ARRAY_SIZE_IN_BYTES); cudaMalloc((void**)&gpu_thread, ARRAY_SIZE_IN_BYTES); cudaMalloc((void**)&gpu_warp, ARRAY_SIZE_IN_BYTES); cudaMalloc((void**)&gpu_calc_thread, ARRAY_SIZE_IN_BYTES); what_is_my_id<warp_size><<<num_blocks, num_threads>>>...
TypeError: cuda() got an unexpected keyword argument 'device_id' ` my basic software versions are as follows: ` cudatoolkit 9.0 h13b8566_0 cudnn 7.1.2 cuda9.0_0 python 3.6.7 h0371630_0 pytorch 0.4.1 py36_cuda9.0.176_cudnn7.1.2_1 soumith ...
The CUDA platform is the foundation of the GPU computing ecosystem. Every application and framework that uses the GPU does so through CUDA's libraries, compilers, runtimes and language — which means CUDA is growing as fast as its ecosystem is evolving. At this engineering-focu...