New question #703335 on Yade: https://answers.launchpad.net/yade/+question/703335
Hi there, I'm trying to set up GPU following https://yade-dev.gitlab.io/trunk/GPUacceleration.html#install-suitesparse. I have encountered doing so. Two main issues are: 1- After installation of Cuda, the samples folder is not generated within the Cuda directory (/usr/local/Cuda/Samples). Therefore, I have to download the Cuda Samples by git clone https://github.com/NVIDIA/cuda-samples.git and then compiling within Samples folder. Running ./deviceQuery inside'/Samples/1_Utilities/deviceQuery' seems satisfactory giving: ./deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "Quadro P2200" CUDA Driver Version / Runtime Version 11.7 / 11.7 CUDA Capability Major/Minor version number: 6.1 Total amount of global memory: 5051 MBytes (5296029696 bytes) (010) Multiprocessors, (128) CUDA Cores/MP: 1280 CUDA Cores GPU Max Clock rate: 1493 MHz (1.49 GHz) Memory Clock rate: 5005 Mhz Memory Bus Width: 160-bit L2 Cache Size: 1310720 bytes Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total shared memory per multiprocessor: 98304 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per multiprocessor: 2048 Maximum 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) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 2 copy engine(s) Run time limit on kernels: Yes Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Disabled Device supports Unified Addressing (UVA): Yes Device supports Managed Memory: Yes Device supports Compute Preemption: Yes Supports Cooperative Kernel Launch: Yes Supports MultiDevice Co-op Kernel Launch: Yes Device PCI Domain ID / Bus ID / location ID: 0 / 101 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.7, CUDA Runtime Version = 11.7, NumDevs = 1 Result = PASS Is this the right approach for testing the Cuda installation for the most recent version? I'm not aware if the Samples folder had been generated for eralier versions inside the Cuda directory itself. I, then, run sudo apt-get install libopenblas-dev liblapack-dev 2- Following the instruction, I download the latest version of SuiteSparse package (5.13.0) and extracted it to usr/local folder using sudo (Question: Is extracting to usr/local mandatory?). Therefore, there is a /usr/local/SuiteSparse-5.13.0 directory from which I run make config within this directory gives: ---------------------------------------------------------------- SuiteSparse package compilation options: ---------------------------------------------------------------- SuiteSparse Version: 5.13.0 SuiteSparse top folder: /usr/local/SuiteSparse-5.13.0 Package: LIBRARY= PackageNameWillGoHere Version: VERSION= x.y.z SO version: SO_VERSION= x System: UNAME= Linux Install directory: INSTALL= /usr/local/SuiteSparse-5.13.0 Install libraries in: INSTALL_LIB= /usr/local/SuiteSparse-5.13.0/lib Install include files in: INSTALL_INCLUDE= /usr/local/SuiteSparse-5.13.0/include Install documentation in: INSTALL_DOC= /usr/local/SuiteSparse-5.13.0/share/doc/suitesparse-5.13.0 Optimization level: OPTIMIZATION= -O3 parallel make jobs: JOBS= 8 BLAS library: BLAS= -lblas LAPACK library: LAPACK= -llapack Other libraries: LDLIBS= -lm -lrt static library: AR_TARGET= PackageNameWillGoHere.a shared library (full): SO_TARGET= PackageNameWillGoHere.so.x.y.z shared library (main): SO_MAIN= PackageNameWillGoHere.so.x shared library (short): SO_PLAIN= PackageNameWillGoHere.so shared library options: SO_OPTS= -L/usr/local/SuiteSparse-5.13.0/lib -Wl,-rpath=/usr/local/SuiteSparse-5.13.0/lib -shared -Wl,-soname -Wl,PackageNameWillGoHere.so.x -Wl,--no-undefined -Wl,-rpath, -Wl,-z,origin shared library name tool: SO_INSTALL_NAME= echo ranlib, for static libs: RANLIB= ranlib static library command: ARCHIVE= ar rv copy file: CP= cp -f move file: MV= mv -f remove file: RM= rm -f pretty (for Tcov tests): PRETTY= grep -v "^#" | indent -bl -nce -bli0 -i4 -sob -l120 C compiler: CC= cc C++ compiler: CXX= g++ CUDA enabled: CUDA= auto CUDA compiler: NVCC= echo CUDA root directory: CUDA_PATH= OpenMP flags: CFOPENMP= -fopenmp C/C++ compiler flags: CF= -O3 -fexceptions -fPIC -fopenmp LD flags: LDFLAGS= -L/usr/local/SuiteSparse-5.13.0/lib -Wl,-rpath=/usr/local/SuiteSparse-5.13.0/lib Fortran compiler: F77= f77 Fortran flags: F77FLAGS= Intel MKL root: MKLROOT= Auto detect Intel icc: AUTOCC= no UMFPACK config: UMFPACK_CONFIG= CHOLMOD config: CHOLMOD_CONFIG= SuiteSparseQR config: SPQR_CONFIG= CUDA library: CUDART_LIB= CUBLAS library: CUBLAS_LIB= METIS and CHOLMOD/Partition configuration: Your METIS library: MY_METIS_LIB= Your metis.h is in: MY_METIS_INC= METIS is used via the CHOLMOD/Partition module, configured as follows. If the next line has -DNPARTITION then METIS will not be used: CHOLMOD Partition config: CHOLMOD Partition libs: -lccolamd -lcamd -lmetis CHOLMOD Partition include: -I/usr/local/SuiteSparse-5.13.0/CCOLAMD/Include -I/usr/local/SuiteSparse-5.13.0/CAMD/Include -I/usr/local/SuiteSparse-5.13.0/metis-5.1.0/include MAKE: make CMake options: -DCMAKE_INSTALL_PREFIX=/usr/local/SuiteSparse-5.13.0 -DCMAKE_CXX_COMPILER=g++ -DCMAKE_C_COMPILER=cc As observed, the paths are blank for CUDART_LIB= and CUBLAS_LIB= which is not a good sign. My effort for manual pointing to the Cuda directory in /usr/local/SuiteSparse/SuiteSparse_config/SuiteSparse_config.mk were not successful. What is wrong with my implementation? 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