Con Kolivas wrote:
Interbench is a Linux Kernel Interactivity Benchmark.
Direct download:
http://ck.kolivas.org/apps/interbench/interbench-0.24.tar.bz2
Web:
http://interbench.kolivas.org
Changes:
3 new loads were added:
Gaming benchmark:
This simulates an unlocked frame rate cpu intensive 3d gaming environment. It
measures the latencies mean/sd/max and desired cpu percentage only. These
should give a marker of frame rate stability (latencies), and maximum frame
rates under different loads (desired cpu percentage). As this simulates an
unlocked frame rate the deadlines met is meaningless. This does not
accurately emulate a 3d game which is gpu bound, only a cpu bound one.
Hackbench:
Taken from Rusty's hackbench code as suggested by Ingo Molnar, this will run
'hackbench 50' repeatedly in the background when benchmarking real time
performance.
Custom:
Based on the periodic scheduling used for audio/video, custom will allow you
to specify a cpu percentage and frame rate of a custom workload, and this can
be used to benchmark this workload's performance under normal scheduling,
real time scheduling or it can be used as a background load.
Bugfixes:
Numerous floating point and overflow errors were tracked down and fixed. These
are responsible for results like 'nan' and 4294... which is basically 2^32.
Unfortunately the standard deviation reported in previous versions appears to
have been bogus, but fortunately little value was placed on this result.
Error handling was made _much_ more robust - for example it was found that
contrary to 'man sem_wait' but consistent with SUSv3, sem_wait can return -1
with -EINTR.
Lots of little tweaks.
I've just been perusing the code and noticed that there is a bug in
calculating the latency standard deviation caused by the fact that the
latency that is inserted into the samples array is not necessarily the
same as that added to total_latency and could be quite a bit larger. So
either the means are too small or the standard deviations are too large.
BTW, there's a method for calculating variances and means that avoids
the need to keep an array of samples. Basically, in addition to the sum
of the samples (sum_x, say) you keep a sum of the squares of the samples
(sum_x_sq, say) and the number of samples (n, say). Then:
mean = sum_x / n
variance = (sum_x_sq - (mean * mean) / n) / (n - 1)
standard deviation = sqrt(variance)
Without the need to keep the array of samples there's no need worry
about an arbitrary upper limit on the number of samples.
Peter
--
Peter Williams [EMAIL PROTECTED]
"Learning, n. The kind of ignorance distinguishing the studious."
-- Ambrose Bierce
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