NVIDIA : CUDA 12.0 : स्थापित करें2024/06/21 |
NVIDIA द्वारा प्रदान किया गया GPU कंप्यूटिंग प्लेटफ़ॉर्म (GPGPU (General-Purpose computing on Graphics Processing Units)), CUDA (Compute Unified Device Architecture) स्थापित करें।
CUDA का उपयोग करने के लिए, आपके कंप्यूटर में NVIDIA ग्राफ़िक कार्ड होना आवश्यक है और वे CUDA-सक्षम उत्पाद भी हैं। |
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[1] |
अपने ग्राफ़िक कार्ड के लिए NVIDIA ग्राफ़िक ड्राइवर स्थापित करें, यहां देखें। |
[2] | CUDA 12.0 स्थापित करें जो Ubuntu 24.04 आधिकारिक रिपॉजिटरी से प्रदान किया गया है। |
root@dlp:~#
root@dlp:~# apt -y install nvidia-cuda-toolkit nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2023 NVIDIA Corporation Built on Fri_Jan__6_16:45:21_PST_2023 Cuda compilation tools, release 12.0, V12.0.140 Build cuda_12.0.r12.0/compiler.32267302_0 |
[3] | नमूना प्रोग्राम चलाने के लिए किसी सामान्य उपयोगकर्ता के साथ इंस्टॉलेशन सत्यापित करें। |
# नमूने कॉपी करें ubuntu@dlp:~$ git clone https://github.com/NVIDIA/cuda-samples.git
ubuntu@dlp:~$
cd ./cuda-samples/Samples/1_Utilities/deviceQuery
ubuntu@dlp:~/cuda-sample/1_Utilities/deviceQuery$
vi Makefile # पंक्ति 35 : परिवर्तन CUDA_PATH?= /usr
# deviceQuery नमूना संकलित करें ubuntu@dlp:~/cuda-sample/1_Utilities/deviceQuery$ make
# deviceQuery नमूना चलाएँ ubuntu@dlp:~/cuda-sample/1_Utilities/deviceQuery$ ./deviceQuery ./deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "NVIDIA GeForce RTX 3060" CUDA Driver Version / Runtime Version 12.4 / 12.0 CUDA Capability Major/Minor version number: 8.6 Total amount of global memory: 12037 MBytes (12622168064 bytes) (028) Multiprocessors, (128) CUDA Cores/MP: 3584 CUDA Cores GPU Max Clock rate: 1777 MHz (1.78 GHz) Memory Clock rate: 7501 Mhz Memory Bus Width: 192-bit L2 Cache Size: 2359296 bytes Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total shared memory per multiprocessor: 102400 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per multiprocessor: 1536 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 2 copy engine(s) Run time limit on kernels: No Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Disabled Device supports Unified Addressing (UVA): Yes Device supports Managed Memory: Yes Device supports Compute Preemption: Yes Supports Cooperative Kernel Launch: Yes Supports MultiDevice Co-op Kernel Launch: Yes Device PCI Domain ID / Bus ID / location ID: 0 / 5 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 12.4, CUDA Runtime Version = 12.0, NumDevs = 1 Result = PASS # bandwidthTest नमूना चलाने का प्रयास करें
ubuntu@dlp:~$
cd ~/cuda-samples/Samples/1_Utilities/bandwidthTest
ubuntu@dlp:~/cuda-sample/1_Utilities/bandwidthTest$
vi Makefile # पंक्ति 35 : परिवर्तन CUDA_PATH?= /usr
make ubuntu@dlp:~/cuda-sample/1_Utilities/bandwidthTest$ ./bandwidthTest [CUDA Bandwidth Test] - Starting... [CUDA Bandwidth Test] - Starting... Running on... Device 0: NVIDIA GeForce RTX 3060 Quick Mode Host to Device Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(GB/s) 32000000 11.0 Device to Host Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(GB/s) 32000000 10.3 Device to Device Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(GB/s) 32000000 318.6 Result = PASS NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
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