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ftq.yaml
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ftq.yaml
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short_description: >
The FTQ benchmarks measure hardware and software interference or 'noise' on a
node from the applications perspective.
description: |
FTQ is a simple benchmark that probes interference within a system. It does so
as follows. For a given sampling period (say, 1ms), we would like to know how
many units of work can be achieved by a user application. The unit of work is
defined below. We can iteratively sample the system, checking for a series of
1ms sample intervals, how much work is actually achieved in each. For a high
interference system, one will see a great deal of variability in they output
data. For a low interference system, one will see less variability. Of
course, there is the distinct possibility that one may be experiencing
constant interference, which would be identical to the low interference case,
except with a lower than ideal mean value. A method for investigating whether
or not this is the case is discussed below.
Traditionally, a simple benchmark to quantify this effect was what we call the
Fixed Work Quantum benchmark. That method basically fixes a work quantum
(say, 10000 integer operations), and samples the amount of time it takes to
execute over a series of samples. The problem with this method is that
although interference is revealed, the lack of a firm time axis (think about
it for a second -- instead of sampling intervals being defined in terms of
time, they're defined in terms of work!) makes sophisticated analysis in
things like the frequency domain difficult and at times, impossible.
FTQ fixes this with two subtle tweaks to FWQ. First off, instead of
performing a single work quantum that requires on the order of the time we
wish the sampling period to be, FTQ uses many samples of work quanta that are
significantly smaller than the desired sampling period. Second, due to the
fact that FTQ is a self sampling system, it is entirely possible (actually,
it's almost guaranteed) that a sample may exceed the sampling interval by some
amount. FTQ is constructed to compensate for that by shrinking subsequent
samples to get the time series back on track.
In the end, FTQ provides data that both reveals interference and is in a form
where sophisticated analysis is possible depending on the desires of the user.
homepage: https://github.com/rminnich/ftq
parameters:
nr_task:
samples:
freq:
results:
ftq.max:
ftq.mean:
ftq.noise: