Slow SVD
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 30/100
- Issue type
- Bug
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- performance
Research direction
Start by reproducing the reported 1000x1000 benchmark using af.svd and compare it with NumPy's svd on the Radeon HD 7950/FGLRX and Debian Jessie setup described in the issue. Check the existing SVD implementation and its backend path; done means identifying and addressing the cause of the timing gap, with the benchmark showing improved results.
Written by the indexing model from the issue text.
Description
I found AF's SVD implementation is quite slow comparing to DGEMM with Radeon HD 7950/FGLRX driver on Debian Jessie:
In [47]: from pylab import randn, svd
In [48]: x_0 = randn(1000, 1000)
In [49]: %time y_0 = svd(x_0)
CPU times: user 1.24 s, sys: 1.01 s, total: 2.24 s
Wall time: 287 ms
In [50]: x_1 = af.Array(x_0.ctypes.data, x_0.shape, 'd')
In [51]: %time y_1 = af.svd(x_1)
CPU times: user 3.64 s, sys: 3.97 s, total: 7.62 s
Wall time: 3.25 s
AF's SVD takes more than 9 times of Numpy's SVD to solve the same matrix, However, the in DGEMM, AF is faster (but not much) than Numpy:
In [75]: from pylab import dot
In [76]: %time z_0 = dot(x_0.transpose(), x_0)
CPU times: user 52 ms, sys: 20 ms, total: 72 ms
Wall time: 10.6 ms
In [77]: %time z_1 = af.matmul(x_1.T, x_1)
CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 8.38 ms
I am wondering if there are anything I should tune/adjust before proceeding.
- Dominant language
- Python
- Stars
- 422
- Forks
- 63
- PR merge metrics
- No merged PRs in 30d
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