[
https://issues.apache.org/jira/browse/MAHOUT-1974?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15996884#comment-15996884
]
ASF GitHub Bot commented on MAHOUT-1974:
----------------------------------------
Github user nsakharnykh commented on the issue:
https://github.com/apache/mahout/pull/310
@andrewpalumbo regarding column-major: yes, this is the default mode for
CUBLAS, sorry I think I didn't mention it in my original email. There are a
couple options we can exercise here. 1. We can use transposed versions of
`gemm` routines if the input matrices are row-major. I think the output matrix
will be always column-major so we'll have to transpose it by using `geam` if we
want to keep it in a different format. 2. We can also keep the dense matrices
in column-major format on the GPU and move between `csc` and `csr` formats for
sparse matrices by using CUSPARSE conversion routines like `csr2csc`. There are
also existing API functions in CUSPARSE to convert sparse to dense `csr2dense`
and the other way around `dense2csr`. I think we should try to use the
available conversion APIs from CUSPARSE as much as possible to avoid writing
this on our own.
> CUDA support
> ------------
>
> Key: MAHOUT-1974
> URL: https://issues.apache.org/jira/browse/MAHOUT-1974
> Project: Mahout
> Issue Type: New Feature
> Reporter: Nikolay Sakharnykh
> Labels: features
>
> Implement native CUDA bindings using JCuda
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
