cublas

问题 I am trying to solve about 1200000 linear systems (3x3, Ax=B) with CUDA 10.1, in particular using the CUBLAS library. I took a cue from this (useful!) post and re-wrote the suggested code in a Unified Memory version. The algorithm firstly performs a LU factorization using cublasgetrfBatched() followed by two consecutive invocations of cublastrsm() which solves upper or lower triangular linear systems. The code is attached below. It works correctly up to about 10000 matrixes and, in this case,

问题 I am trying to solve about 1200000 linear systems (3x3, Ax=B) with CUDA 10.1, in particular using the CUBLAS library. I took a cue from this (useful!) post and re-wrote the suggested code in a Unified Memory version. The algorithm firstly performs a LU factorization using cublasgetrfBatched() followed by two consecutive invocations of cublastrsm() which solves upper or lower triangular linear systems. The code is attached below. It works correctly up to about 10000 matrixes and, in this case,

问题 How do I correctly link to CUBLAS in CMake 3.11 ? In particular, I'm trying to create a CMakeLists file for this code. CMakeLists file so far: cmake_minimum_required(VERSION 3.8 FATAL_ERROR) project(cmake_and_cuda LANGUAGES CXX CUDA) add_executable(mmul_2 mmul_2.cu) This gives multiple "undefined reference errors" to cublas and curand. 回答1: Found the solution which is to add this line in the end of the CMakeLists file: target_link_libraries(mmul_2 -lcublas -lcurand) 来源： https://stackoverflow

问题 Concerning CUDA 10.1 I'm doing some calculations on geometric meshes with a large amount of independent calculations done per face of the mesh. I run a CUDA kernel which does the calculation for each face. The calculations involve some matrix multiplication, so I'd like to use cuBLAS or cuBLASLt to speed things up. Since I need to do many matrix multiplications (at least a couple per face) I'd like to do it directly in the kernel. Is this possible? It doesn't seem like cuBLAS or cuBLASLt

问题 Concerning CUDA 10.1 I'm doing some calculations on geometric meshes with a large amount of independent calculations done per face of the mesh. I run a CUDA kernel which does the calculation for each face. The calculations involve some matrix multiplication, so I'd like to use cuBLAS or cuBLASLt to speed things up. Since I need to do many matrix multiplications (at least a couple per face) I'd like to do it directly in the kernel. Is this possible? It doesn't seem like cuBLAS or cuBLASLt

问题 I have a sample cuBLAS Fortran binding routine provided from a previous question here. I'm running Ubuntu 13.10, IFORT 14.0.1, and Cuda 5.5. The code is below: cublas.f program cublas_fortran_example implicit none integer i, j c helper functions integer cublas_init integer cublas_shutdown integer cublas_alloc integer cublas_free integer cublas_set_vector integer cublas_get_vector c selected blas functions double precision cublas_ddot external cublas_daxpy external cublas_dscal external cublas

问题 I'd like to convert Octave to use CuBLAS for matrix multiplication. This video seems to indicate this is as simple as typing 28 characters: Using CUDA Library to Accelerate Applications In practice it's a bit more complex than this. Does anyone know what additional work must be done to make the modifications made in this video compile? UPDATE Here's the method I'm trying in dMatrix.cc add #include <cublas.h> in dMatrix.cc change all occurences of (preserving case) dgemm to cublas_dgemm in my

问题 I'd like to convert Octave to use CuBLAS for matrix multiplication. This video seems to indicate this is as simple as typing 28 characters: Using CUDA Library to Accelerate Applications In practice it's a bit more complex than this. Does anyone know what additional work must be done to make the modifications made in this video compile? UPDATE Here's the method I'm trying in dMatrix.cc add #include <cublas.h> in dMatrix.cc change all occurences of (preserving case) dgemm to cublas_dgemm in my

问题 I am trying to run a matrix inversion from the device. This logic works fine if called from the host. Compilation line is as follows (Linux): nvcc -ccbin g++ -arch=sm_35 -rdc=true simple-inv.cu -o simple-inv -lcublas_device -lcudadevrt I get the following warning that I cannot seem to resolve. (My GPU is Kepler. I don't know why it is trying to link to Maxwell routines. I have Cuda 6.5-14): nvlink warning : SM Arch ('sm_35') not found in '/usr/local/cuda/bin/../targets/x86_64-linux/lib

问题 I have an input matrix A of size 10x20 , I want to permute its columns as follows: p=[1 4 2 3 5 11 7 13 6 12 8 14 17 9 15 18 10 16 19 20] ;%rearrange the columns of A A=A(:,p); To do so, I constructed a permutation matrix I corresponding to the permutation vector p and permuted A can be obtained by performing the following multiplication: A=A*I I tested the permutation in Matlab and everything is ok. Now, I want to test it in cuda using cublas. The input matrix A is entered in column major.