Hi Marcus, Thanks for the detailed answer. After a long period, I managed to work on it (in several steps) during the last couple of months.
I managed to find the bottleneck using ControlPorts. It showed a couple of blocks that were bottlenecks (based on previous block's output buffers). Interestingly, they weren't IO-bound. I haven't investigated it further, but I guess that the facts that (i) they caused dropped samples; (ii) were CPU-bound; and (iii) did not reach anywhere near 100% in htop indicate that they might have high fluctuations in CPU usage, it might be that they do use 100% CPU for a very short time (then they cause dropped samples), shorter than the sampling and averaging periods of tools like htop. Optimizing those blocks solved the issue of dropped samples. I only gave kernelshark little attention. It didn't prove to be a simple tool to use out of the box, I don't think that the docs and level of polish are well enough for this general use case. Maybe, if someone takes it as a project and documents how to apply it to flowgraph inspection. On Tue, Nov 7, 2017 at 1:25 PM Marcus Müller <muel...@kit.edu> wrote: > Hi Gilad, > > part of this is for the future reader of this thread, so, please, bear > with me: > > On 07.11.2017 10:42, Gilad Beeri (ApolloShield) wrote: > > I have a flowgraph, that when run, no CPU core is ever close to 100% > > utilization. > > Indeed, dropped samples indicate a bottleneck narrower than your USRP's > sampling rate, but that bottleneck doesn't have to be CPU overutilization! > Simplest example: add a Throttle block to a flow graph that otherwise > wouldn't produce any problems with half the necessary sampling rate. > > Most often, I find that IO operations actually become the the bottleneck > – be it that sending samples to the USRP (or receiving them) is actually > pretty time-intense, or that you need to interact with storage. > > Depending on the tooling you choose, this fact might or might not be > hidden; time spent, for example "on behalf" of a thread in Kernel land, > searching for a contiguous piece of memory to give to that process, or > handling USB buffers or... might or might not be attributed to the process. > > Another very classical problem is memory bandwidth and latency; so, as > shown by SE at this year's GRCon, chances aren't that bad that you can > optimize quite a bit if you co-locate connected blocks on the same CPU, > you get a caching advantage (or, rather, not incur a disadvantage). > > That all being said, how do you proceed? > > First of all, this is one of the cases where having ControlPort is very > helpful. If you have it (with Thrift and PerfCounters enabled), you can > start the CtrlPort Performance Monitor, and see which output buffers > "stay full" all the time. Block after that is probably your bottleneck. > > If you don't, try running `perf top -ag` (as root might help here, you > want to also inspect kernel times, not quite sure about that, though). > You should be getting a listing of "when we sampled where the CPU(s) > were, in x % of the time, they were stuck in these functions". > > I really tried, but haven't had the time to work with kernelshark. That > might really be a tool of choice here. In fact, it looks so cool that I > could imagine that we one day supersede the perf counter concept with > that; who knows. > > If you do happen to look into that, I'd be very happy to get some > feedback about the process, and what the problems were. I think this is > definitely something we want to enable users to do – understand not only > the behaviour of their blocks in isolation, but how a system works. > After all, one of the major "let's dream about a GNU Radio in the > future" things we're considering is making it easy to distribute a flow > graph across computers, and for that, systemic insight pretty much is a > must. > > Best regards, > Marcus > > _______________________________________________ > Discuss-gnuradio mailing list > Discussemail@example.com > https://lists.gnu.org/mailman/listinfo/discuss-gnuradio >
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