Example: CUDA/GPU Information

julia> gpus()
8 devices:
  0: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
  1: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
  2: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
  3: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
  4: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
  5: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
  6: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
  7: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)

More specific information for each gpu device can be obtained with gpuinfo(deviceid::Integer).

julia> gpuinfo()
Device: NVIDIA A100-SXM4-40GB (CuDevice(0))
Total amount of global memory: 42.5 GB
Number of CUDA cores: 6912
Number of multiprocessors: 108 (64 CUDA cores each)
GPU max. clock rate: 1410 Mhz
Memory clock rate: 1215 Mhz
Memory bus width: 5120-bit
L2 cache size: 41.9 MB
Max. texture dimension sizes (1D): 131072
Max. texture dimension sizes (2D): 131072, 65536
Max. texture dimension sizes (3D): 16384, 16384, 16384
Max. layered 1D texture size: 32768 (2048 layers)
Max. layered 2D texture size: 32768, 32768 (2048 layers)
Total amount of constant memory: 65.5 kB
Total amount of shared memory per block: 49.2 kB
Total number of registers available per block: 65536
Warp size: 32
Max. number of threads per multiprocessor: 2048
Max. 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
Texture alignment: 512.0 B
Maximum memory pitch: 2.1 GB
Concurrent copy and kernel execution: Yes with 3 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing host memory: No
Support host page-locked memory mapping: Yes
Concurrent kernel execution: Yes
Alignment requirement for surfaces: Yes
Device has ECC support: Yes
Device supports Unified Addressing (UVA): Yes
Device supports managed memory: Yes
Device supports compute preemption: Yes
Supports cooperative kernel launch: Yes
Supports multi-device co-op kernel launch: Yes
Device PCI domain ID / bus ID / device ID: 0 / 7 / 0
Compute mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)

Peer-to-peer access information can be retrived via gpuinfo_p2p_access.

julia> gpuinfo_p2p_access()
P2P Access Supported:
8×8 Matrix{Bool}:
 0  1  1  1  1  1  1  1
 1  0  1  1  1  1  1  1
 1  1  0  1  1  1  1  1
 1  1  1  0  1  1  1  1
 1  1  1  1  0  1  1  1
 1  1  1  1  1  0  1  1
 1  1  1  1  1  1  0  1
 1  1  1  1  1  1  1  0

P2P Atomic Supported:
8×8 Matrix{Bool}:
 0  1  1  1  1  1  1  1
 1  0  1  1  1  1  1  1
 1  1  0  1  1  1  1  1
 1  1  1  0  1  1  1  1
 1  1  1  1  0  1  1  1
 1  1  1  1  1  0  1  1
 1  1  1  1  1  1  0  1
 1  1  1  1  1  1  1  0

Turns out that using CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED or cuDeviceCanAccessPeer to query p2p access support may lead to different results (see this stackoverflow thread). In gpuinfo_p2p_access() we use both methods and, if the results were to be different, we print both matrices (not shown above).