blas

问题 I want to use the BLAS package. To do so, the meaning of the two first parameters of the gemm() function is not evident for me. What do the parameters 'N' and 'T' represent? BLAS.gemm!('N', 'T', lr, alpha, A, B, beta, C) What is the difference between BLAS.gemm and BLAS.gemm! ? 回答1: According to the documentation gemm!(tA, tB, alpha, A, B, beta, C) Update C as alpha * A * B + beta*C or the other three variants according to tA (transpose A) and tB. Returns the updated C. Note: here, alpha and

问题 I'm trying to install matplotlib with pip in a virtualenv on debian wheezy. And i get the following message: Running setup.py egg_info for package matplotlib GotoBLAS : Architecture Initialization failed. No initialization function found. ============================================================================ Edit setup.cfg to change the build options BUILDING MATPLOTLIB matplotlib: yes [1.3.1] python: yes [2.7.3 (default, Jan 2 2013, 13:56:14) [GCC 4.7.2]] platform: yes [linux2]

问题 i have been trying for hours and it drives me crazy. The last error I get is : demo_cblas.c:(.text+0x83): undefined reference to `clapack_sgetrf' demo_cblas.c:(.text+0xa3): undefined reference to `clapack_sgetri' I am compiling the code using /usr/bin/gcc -o demo_cblas demo_cblas.c -L /usr/lib64 -l :libgfortran.so.3 -L /usr/lib64 \ -llapack -L /usr/lib64 -lblas I try with and without libgfortran, with different compilers gcc-33, gcc-47, gcc-48. The test code is not from me but comes from this

问题 I am trying to use MKL Sparse BLAS for CSR matrices with number of rows/columns on the order of 100M. My source code that seems to work fine for 10M rows/columns fails with segfault when I increase it to 100M. I isolated the problem to the following code snippet: void TestSegfault1() { float values[1] = { 1.0f }; int col_indx[1] = { 0 }; int rows_start[1] = { 0 }; int rows_end[1] = { 1 }; // Step 1. Create 1 x 100M matrix // with single non-zero value at (0,0) sparse_matrix_t A; mkl_sparse_s

问题 See further update below I am observing a quiet high system CPU usage when running my Fortran code. The "user CPU usage" is taking about one core (system is an Intel i7 with 4 cores/ 8 threads, running Linux) whilst system CPU is eating up about 2 cores (hence overall CPU usage about 75%). Can anyone explain to me where this is coming from and if this is "normal" behaviour? I compile the code with gfortran (optimization turned off -O0, though that part doesn't seem to matter) and link against

问题 I'm on an Ubuntu 14.04. I would have expected doing: sudo apt-get install python-numpy would've worked but it isn't the case... The way I tried to check is by doing locate blas and found nothing that seemed relevant. I would like a solution that doesn't involve me compiling from source. The ideal solution is something that uses the ubuntu repos. 回答1: In Ubuntu 14.04 and later, blas and lapack are installed as part of python-scipy and python3-scipy, so you need to install python-scipy. Open

问题 In CMake I use find_package(BLAS REQUIRED) and I use the BLAS_FOUND , BLAS_LINKER_FLAGS , BLAS_LIBRARIES variables as appropriate. My question is, how do I, based on the BLAS implementation that has been selected, find the include directory that should be included in CMake? BLAS_INCLUDE_DIR is not being set on macOS for either the Accelerate framework nor OpenBLAS. Also it's not part of the documentation for FindBLAS. 回答1: If there isn't a script already provided, you can write one yourself

问题 I'm trying to install numpy==1.10.2 , scipy and matplotlib on server based on opensuse . I've installed numpy in virtualenv from source (I've tried by pip also - same result of course). Now when I'm trying to import numpy in python console I'm receiving following error: ImportError: /home/user/.virtualenvs/project/lib/python2.7/site-packages/numpy/core/multiarray.so: undefined symbol: cblas_sgemm Note: I'm not superuser on this server. Edit: ldd /home/user/.virtualenvs/project/lib/python2.7

问题 I am interested in using Julia SharedArray s for a scientific computing project. My current implementation appeals to BLAS for all matrix-vector operations, but I thought that perhaps a SharedArray would offer some speedup on multicore machines. My idea is to simply update an output vector index-by-index, farming the index updates to worker processes. Previous discussions here about SharedArray s and here about shared memory objects did not offer clear guidance on this issue. It seems

问题 i am trying to troubleshoot my non-working apache spark and netlib setup and i don't know what to do next. Here some info: Spark 1.3.1 (but also tried 1.5.1) Mesos Cluster with 3 Nodes Ubuntu Trusty on every node and installed following BLAS package $ dpkg -l | grep 'blas\|atlas\|lapack' ii libopenblas-base 0.2.8-6ubuntu1 amd64 Optimized BLAS (linear algebra) library based on GotoBLAS2 $ update-alternatives --get-selections | grep 'blas\|lapack' libblas.so.3 auto /usr/lib/openblas-base