Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/131#issuecomment-37532494
I do mean dgemm, since it is in jblas, although dsyrk would be even better
as it is specialized for this case. gemm can treat its args as transposed and
apply scalars, so i
Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/131
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Github user rxin commented on the pull request:
https://github.com/apache/spark/pull/131#issuecomment-37507055
Thanks. I've merged this.
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Github user MLnick commented on the pull request:
https://github.com/apache/spark/pull/131#issuecomment-37506200
@mengxr nice work! Thanks for this.
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Github user mengxr commented on the pull request:
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@srowen , level 3 BLAS would certainly help improve the performance. DSYRK
is for computing C <- A^T A + C, but I don't know whether we have it in jblas.
However, for impli
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Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/131#issuecomment-37493745
Yes I like where this is going. I had started on a similar change, that
used gemm instead, since it can compute C <- A*B + C.
In fact I had wanted to add an extra
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Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/131#issuecomment-37490541
@srowen , this continues the work from
https://github.com/apache/incubator-spark/pull/629 . Would you please help
review the changes? Thanks!
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GitHub user mengxr opened a pull request:
https://github.com/apache/spark/pull/131
[SPARK-1237, 1238] Improve the computation of YtY for implicit ALS
Computing YtY can be implemented using BLAS's DSPR operations instead of
generating y_i y_i^T and then combining them. The latter gen
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