use specialized axpy in RowMatrix for SVD #1378
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copy ARPACK dsaupd/dseupd code from latest breeze change RowMatrix to use sparse SVD change tests for sparse SVD
change the computation mode to local-svd, local-eigs, and dist-eigs update tests and docs
Some updates to SVD impl
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Can one of the admins verify this patch? |
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hmmm, I did sync with the upstream branch before committing the last change, it seems that the whole commit history is still there... |
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Jenkins, add to whitelist. |
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Jenkins, test this please. |
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QA tests have started for PR 1378. This patch merges cleanly. |
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QA results for PR 1378: |
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@vrilleup We squash commits before merging a PR. The commit history show up since you used your master branch for this PR but apache/master doesn't have those commits. The change looks good to me. Thanks for testing the performance! |
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Merged. |
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@mengxr thank you for merging the change! |
After running some more tests on large matrix, found that the BV axpy (breeze/linalg/Vector.scala, axpy) is slower than the BSV axpy (breeze/linalg/operators/SparseVectorOps.scala, sv_dv_axpy), 8s v.s. 2s for each multiplication. The BV axpy operates on an iterator while BSV axpy directly operates on the underlying array. I think the overhead comes from creating the iterator (with a zip) and advancing the pointers. Author: Li Pu <lpu@twitter.com> Author: Xiangrui Meng <meng@databricks.com> Author: Li Pu <li.pu@outlook.com> Closes apache#1378 from vrilleup/master and squashes the following commits: 6fb01a3 [Li Pu] use specialized axpy in RowMatrix 5255f2a [Li Pu] Merge remote-tracking branch 'upstream/master' 7312ec1 [Li Pu] very minor comment fix 4c618e9 [Li Pu] Merge pull request apache#1 from mengxr/vrilleup-master a461082 [Xiangrui Meng] make superscript show up correctly in doc 861ec48 [Xiangrui Meng] simplify axpy 62969fa [Xiangrui Meng] use BDV directly in symmetricEigs change the computation mode to local-svd, local-eigs, and dist-eigs update tests and docs c273771 [Li Pu] automatically determine SVD compute mode and parameters 7148426 [Li Pu] improve RowMatrix multiply 5543cce [Li Pu] improve svd api 819824b [Li Pu] add flag for dense svd or sparse svd eb15100 [Li Pu] fix binary compatibility 4c7aec3 [Li Pu] improve comments e7850ed [Li Pu] use aggregate and axpy 827411b [Li Pu] fix EOF new line 9c80515 [Li Pu] use non-sparse implementation when k = n fe983b0 [Li Pu] improve scala style 96d2ecb [Li Pu] improve eigenvalue sorting e1db950 [Li Pu] SPARK-1782: svd for sparse matrix using ARPACK
After running some more tests on large matrix, found that the BV axpy (breeze/linalg/Vector.scala, axpy) is slower than the BSV axpy (breeze/linalg/operators/SparseVectorOps.scala, sv_dv_axpy), 8s v.s. 2s for each multiplication. The BV axpy operates on an iterator while BSV axpy directly operates on the underlying array. I think the overhead comes from creating the iterator (with a zip) and advancing the pointers.