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LinearAlgebra./ and LinearAlgebra.\ breaks CuArray  #1568

@algorithmx

Description

@algorithmx

After using the operator / or \ on two CuArrays, some internal states oc CUDA seems to be broken.
The lines followng it causes strange error messages.
Removing the line using the operator / or \ will eliminate the error.

To reproduce

The Minimal Working Example (MWE) for this bug:

using LinearAlgebra
using CUDA

b = CUDA.rand(Float64,5,5)
A = CUDA.rand(Float64,5,5)
V = (A \ b)  # no command-line errors if this line is absent 

B = CUDA.rand(Float64,5,5)
w = CUDA.rand(Float64,5)
B * w
Manifest.toml

# This file is machine-generated - editing it directly is not advised

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[[IrrationalConstants]]
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Expected behavior

Run the code in MWE from command line or Julia REPL will generate the following error message:

(base) dabajabaza@XXXX:~$ julia jianguoyun/Nutstore/RigorousCoupledWaveAnalysis.jl-master/examples/test.ma2018.jl 
ERROR: LoadError: CUBLASError: the requested feature is not supported (code 15, CUBLAS_STATUS_NOT_SUPPORTED)
Stacktrace:
 [1] throw_api_error(res::CUDA.CUBLAS.cublasStatus_t)
   @ CUDA.CUBLAS ~/.julia/packages/CUDA/DfvRa/lib/cublas/error.jl:50
 [2] macro expansion
   @ ~/.julia/packages/CUDA/DfvRa/lib/cublas/error.jl:63 [inlined]
 [3] cublasDgemv_v2(handle::Ptr{Nothing}, trans::Char, m::Int64, n::Int64, alpha::Bool, A::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}, lda::Int64, x::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}, incx::Int64, beta::Bool, y::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}, incy::Int64)
   @ CUDA.CUBLAS ~/.julia/packages/CUDA/DfvRa/lib/utils/call.jl:26
 [4] gemv!
   @ ~/.julia/packages/CUDA/DfvRa/lib/cublas/wrappers.jl:331 [inlined]
 [5] gemv_dispatch!(Y::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}, A::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}, B::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}, alpha::Bool, beta::Bool)
   @ CUDA.CUBLAS ~/.julia/packages/CUDA/DfvRa/lib/cublas/linalg.jl:179
 [6] mul!
   @ ~/.julia/packages/CUDA/DfvRa/lib/cublas/linalg.jl:188 [inlined]
 [7] mul!
   @ ~/julia/share/julia/stdlib/v1.7/LinearAlgebra/src/matmul.jl:275 [inlined]
 [8] *(A::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}, x::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer})
   @ LinearAlgebra ~/julia/share/julia/stdlib/v1.7/LinearAlgebra/src/matmul.jl:47
 [9] top-level scope
   @ ~/jianguoyun/Nutstore/RigorousCoupledWaveAnalysis.jl-master/examples/test.ma2018.jl:9
in expression starting at /home/dabajabaza/jianguoyun/Nutstore/RigorousCoupledWaveAnalysis.jl-master/examples/test.ma2018.jl:9

Version info

Details on Julia:

Julia Version 1.7.2
Commit bf53498635 (2022-02-06 15:21 UTC)
Platform Info:
  OS: Linux (x86_64-pc-linux-gnu)
  CPU: Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-12.0.1 (ORCJIT, skylake)
Environment:
  JULIA_PKG_SERVER = https://mirrors.tuna.tsinghua.edu.cn/julia

Details on CUDA:

CUDA toolkit 11.7, artifact installation
NVIDIA driver 495.29.5, for CUDA 11.5
CUDA driver 11.5

Libraries: 
- CUBLAS: 11.10.1
- CURAND: 10.2.10
- CUFFT: 10.7.2
- CUSOLVER: 11.3.5
- CUSPARSE: 11.7.3
- CUPTI: 17.0.0
- NVML: 11.0.0+495.29.5
- CUDNN: 8.30.2 (for CUDA 11.5.0)
- CUTENSOR: 1.4.0 (for CUDA 11.5.0)

Toolchain:
- Julia: 1.7.2
- LLVM: 12.0.1
- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0
- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80

1 device:
  0: NVIDIA GeForce GTX 1060 (sm_61, 5.387 GiB / 5.936 GiB available)

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