
Will
I was trying to get a feeling for what those coloured squares actually
denoted - typically we examine this sort of performance information
as CDFs (cumulative distribution functions[1]) trying to pull apart the
issues that “mean” effecting (i.e typical path through code/system) and
those that are “tail” effecting (i.e exceptions - and GC running could
be seen as an “exception” - one that you can manage and time shift
in the relative timing).
I’m assuming that messages have a similar “cost” (i.e similar work
to complete) - so that a uniform arrival rate equates to a uniform
rate of work to be done arriving.
Neil
[1] We plot the CDF’s in two ways, the “usual” way for the major part
of the probability mass and then as a (1-CDF) on a log log scale to
expose the tail behaviour.
On 29 Sep 2015, at 10:35, Will Sewell
Thank you for the reply Neil.
The spikes are in response time. The graph I linked to shows the distribution of response times in a given window of time (darkness of the square is the number of messages in a particular window of response time). So the spikes are in the mean and also the max response time. Having said that I'm not exactly sure what you mean by "mean values".
I will have a look into -I0.
Yes the arrival of messages is constant. This graph shows the number of messages that have been published to the system: http://i.imgur.com/ADzMPIp.png
On 29 September 2015 at 10:16, Neil Davies
wrote: Will
is your issue with the spikes i response time, rather than the mean values?
If so, once you’ve reduced the amount of unnecessary mutation, you might want to take more control over when the GC is taking place. You might want to disable GC on timer (-I0) and force GC to occur at points you select - we found this useful.
Lastly, is the arrival pattern (and distribution pattern) of messages constant or variable? just making sure that you are not trying to fight basic queueing theory here.
Neil
On 29 Sep 2015, at 10:03, Will Sewell
wrote: Thanks for the reply Greg. I have already tried tweaking these values a bit, and this is what I found:
* I first tried -A256k because the L2 cache is that size (Simon Marlow mentioned this can lead to good performance http://stackoverflow.com/a/3172704/1018290) * I then tried a value of -A2048k because he also said "using a very large young generation size might outweigh the cache benefits". I don't exactly know what he meant by "a very large young generation size", so I guessed at this value. Is it in the right ballpark? * With -H, I tried values of -H8m, -H32m, -H128m, -H512m, -H1024m
But all lead to worse performance over the defaults (and -H didn't really have much affect at all).
I will try your suggestion of setting -A to the L3 cache size.
Are there any other values I should try setting these at?
90% of the memory is used for our message index, which is a temporary store of messages that have gone through the system. These messages are stored in aligned chunks in memory that are merged together. I initially though this was causing the spikes, but they were still
As for your final point, I have run space profiling, and it looks like there even after I removed the component. I will try and run space profiling in the build with the message index.
Thanks again.
On 28 September 2015 at 19:02, Gregory Collins
wrote: On Mon, Sep 28, 2015 at 9:08 AM, Will Sewell
wrote: If it is the GC, then is there anything that can be done about it?
Increase value of -A (the default is too small) -- best value for this is L3 cache size of the chip Increase value of -H (total heap size) -- this will use more ram but you'll run GC less often This will sound flip, but: generate less garbage. Frequency of GC runs is proportional to the amount of garbage being produced, so if you can lower mutator allocation rate then you will also increase net productivity. Built-up thunks can transparently hide a lot of allocation so fire up the profiler and tighten those up (there's an 80-20 rule here). Reuse output buffers if you aren't already, etc.
G
-- Gregory Collins
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