gpuinfo: Lightweight Hardware and GPU Compute Detection
Detects central processing unit topology, process-available cores,
host and container memory, graphics processing unit hardware, and reports
the apparent availability of 'CUDA', 'Metal', 'ROCm',
and 'OpenCL' compute backends, with a read-only shared-library inventory for
the 'CUDA' runtime, 'cuBLAS', 'cuFFT', 'cuSOLVER', 'cuRAND', and optional
'NCCL' libraries. Detection uses operating-system information,
documented platform interfaces, and optional command-line utilities,
without requiring a GPU framework, 'Python', or a vendor software
development kit. Backend interpretation follows the official 'CUDA'
<https://docs.nvidia.com/cuda/cuda-driver-api/>, 'Metal'
<https://developer.apple.com/documentation/metal>, 'ROCm'
<https://rocm.docs.amd.com/>, and 'OpenCL'
<https://registry.khronos.org/OpenCL/> documentation. 'NCCL' version
inspection follows its documented API
<https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/comms.html>.
Process-level GPU memory samples, multi-device inventories, and versioned
hardware manifests support reproducible benchmarks. Missing hardware,
drivers, libraries, and utilities are handled safely.
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