Recently I use 'imrotate' a lot, and I see a 25 times speed boost with single-precision data compared with double-precision.
I am quite astonished because I thought single-precision were usually 'only' twice as fast as double-precision.
I ran this code on Intel Core Duo E8400 @ 3Ghz, with Matlab 2012A 64 bit. Double precision takes 10.461 seconds, while single precision takes 0.456s.
s=randn(400,400); for i=1:1:180 imrotate(s,i); end
In profiler, I saw that "imrotatemex(MEX-file)" was called if input is single-precision but was not used if input is double-precision, which makes a huge difference in speed.
Is it possible that Mathworks update 'imrotate' so that "imrotatemex(MEX-file)" works with both single and double-precision data?
If you are able to upgrade to a newer version, performance improvements for IMROTATE were implemented in R2012b. Moreover, R2013a adds GPU support for IMROTATE. See the release notes for more information.
As Teja pointed out, IMROTATE was hardware optimized for double-precision inputs in R2012b. If you are able to update to a newer version, you will see a dramatic increase in performance for double inputs in IMROTATE.
This is a limitation imposed by MathWorks in imrotate.m (subfunction useIPP), purportedly because Intel's Performance Primitives Library (IPPL) does not support double precision (although it does). Modifying useIPP() to accept doubles will not help since imrotatemex itself rejects this data type...
The big performance boost in R2012b appears to result from simply removing this limitation in both useIPP and imrotatemex.
Play games and win prizes!Learn more