Use numpy directly for matrix multiply. #687
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Using matrix multiply to compute XtX and XtY yields a 5-20x speedup depending on problem size. For example - the following takes 19s locally after this change vs. 5m21s before the change. (16x speedup). bin/pyspark examples/src/main/python/als.py local[8] 1000 1000 50 10 10
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examples/src/main/python/als.py
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@etrain Could you also update this line? We should only touch the diagonals.
This probably won't make a huge difference when K is small, but it's better style.
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LGTM. Thanks! |
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Thanks. Merged. |
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Using matrix multiply to compute XtX and XtY yields a 5-20x speedup depending on problem size. For example - the following takes 19s locally after this change vs. 5m21s before the change. (16x speedup). bin/pyspark examples/src/main/python/als.py local[8] 1000 1000 50 10 10 Author: Evan Sparks <evan.sparks@gmail.com> Closes #687 from etrain/patch-1 and squashes the following commits: e094dbc [Evan Sparks] Touching only diaganols on update. d1ab9b6 [Evan Sparks] Use numpy directly for matrix multiply. (cherry picked from commit 6ed7e2c) Signed-off-by: Reynold Xin <rxin@apache.org>
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Using matrix multiply to compute XtX and XtY yields a 5-20x speedup depending on problem size. For example - the following takes 19s locally after this change vs. 5m21s before the change. (16x speedup). bin/pyspark examples/src/main/python/als.py local[8] 1000 1000 50 10 10 Author: Evan Sparks <evan.sparks@gmail.com> Closes apache#687 from etrain/patch-1 and squashes the following commits: e094dbc [Evan Sparks] Touching only diaganols on update. d1ab9b6 [Evan Sparks] Use numpy directly for matrix multiply.
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Using matrix multiply to compute XtX and XtY yields a 5-20x speedup depending on problem size.
For example - the following takes 19s locally after this change vs. 5m21s before the change. (16x speedup).
bin/pyspark examples/src/main/python/als.py local[8] 1000 1000 50 10 10