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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "gpuinfo" in publications use:'
type: software
license: MIT
title: 'gpuinfo: Lightweight Hardware and GPU Compute Detection'
version: 0.2.0
doi: 10.32614/CRAN.package.gpuinfo
abstract: 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.
authors:
- family-names: Cacciatore
  given-names: Stefano
  email: tkcaccia@gmail.com
  orcid: https://orcid.org/0000-0001-7052-7156
repository: https://tkcaccia.r-universe.dev
repository-code: https://github.com/tkcaccia/gpuinfo
commit: a1122acf1cdf59c2fb2e19283353f3231d85bde3
url: https://github.com/tkcaccia/gpuinfo
date-released: '2026-10-14'
contact:
- family-names: Cacciatore
  given-names: Stefano
  email: tkcaccia@gmail.com
  orcid: https://orcid.org/0000-0001-7052-7156