🚀 go-pugleaf

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The method I use for performance analysis depends heavily on the problem
being investigated.  Most of my work is in measurement and tuning of
operating systems, so I usually start by instrumenting the system of
interest and then performing statistical analysis on the results.

[Could you explain how you "instrument" the system?  -DL]

This usually provides only a rough sketch of the system's behavior, because
measurement interfers with behavior, and because instrumentation doesn't
uncover all of the behavior.  Once I have this rough sketch, I propose a
series of experiments aimed at highlighting the behavior I'm interested in.
I do this using the scientific method: propose a hypothesis explaining
some behavior of the system, formulate an experiment which can disprove
the hypothesis, perform the experiment, and evaluate the results.

The experiments can consist of modifications to the measurements being made,
applications of analytic models, stochastic or event driven model, and
performance of workload benchmarks; as appropriate.  In a recently completed
analysis of the UniCos scheduler on the Cray 2, I have used all but stochastic
models.

My personal preferences for analysis tools are SPSS and SLAMII, not because
they are inherently better than other tools, but because I am familiar with
them and they are (usually) readily available.

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