Sign Up to our social questions and Answers Engine to ask questions, answer people’s questions, and connect with other people.
Login to our social questions & Answers Engine to ask questions answer people’s questions & connect with other people.
Lost your password? Please enter your email address. You will receive a link and will create a new password via email.
Please briefly explain why you feel this question should be reported.
Please briefly explain why you feel this answer should be reported.
Please briefly explain why you feel this user should be reported.
I’m doing some calculations, and doing some analysis on the forces and weakness of different BLAS implementations. however I have come across a problem.
I’m testing cuBlas, doing linAlg on the GPU would seem like a good idea, but there is one problem.
The cuBlas implementation using column-major format, and since this is not what I need in the end, I’m curious if there is a way in with one can make BLAS do matrix-transpose?
BLAS doesn’t have a matrix transpose routine built in. The CUDA SDK includes a matrix transpose example with a paper which discusses optimal strategy for performing a transpose. Your best strategy is probably to use row major inputs to CUBLAS with the transpose input version of the calls, then perform the intermediate calculations in column major, and lastly perform a transpose operation afterwards using the SDK transpose kernel.
Edited to add that CUBLAS added a transpose routine in CUBLAS version 5,
geam, which can performed matrix transposition in GPU memory and should be regarded as optimal for whatever architecture you are using.