)]}'
{
  "commit": "7d2e4152ec3da4a7f866f6030e1a2ae6a9bb5a04",
  "tree": "a242128f10529a8d64e500fb98eea450873a9bb1",
  "parents": [
    "f90833f5fac832b39ccb22a20567a9ad035cb71a"
  ],
  "author": {
    "name": "Joydeep Biswas",
    "email": "joydeepb@cs.utexas.edu",
    "time": "Sat Feb 12 12:09:26 2022 -0600"
  },
  "committer": {
    "name": "Joydeep Biswas",
    "email": "joydeepb@cs.utexas.edu",
    "time": "Mon Feb 14 15:22:39 2022 -0600"
  },
  "message": "Add support for dense CUDA solvers #2\n\n1. Add CUDADenseQR \u0026 tests.\n   CUDADenseQR uses the cuSolverDN LAPACK implementation\n   of QR factorization. A key limitation, however, is that\n   this solver does not perform singularity checking --\n   this is because cuSolverDN does not have a trtrs\n   implementation; we instead use cuBLAS\u0027 trsv for\n   backsubstitution.\n2. All CPU -\u003e GPU memory transfers are now async, and both\n   CUDADenseQR and CUDADenseCholesky explicitly manage their\n   own streams for async operations.\n3. Simplified CUDADenseCholesky to only use the legacy 32-bit\n   cuSolverDN API.\n\nChange-Id: I2a9b7b65469658ddfe33b5b2a3892c8744d6e437\n",
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