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From jcai <jonathon....@yale.edu>
Subject Verifying Empirically Number of Performance-Heavy Threads and Parallelism
Date Thu, 25 Jun 2015 16:26:57 GMT
Hello,

I am doing some performance testing on Spark and 

1. I would like to verify Spark's parallelism, empirically. For example, I
would like to determine if the number of "performance-heavy" threads is
equal to SPARK_WORKER_CORES in standalone mode at a given moment.
Essentially, I want to ensure that the right amount of parallelism is
achieved when fiddling around with certain parameters in Spark. 

a. I've skimmed what Ganglia does but it doesn't seem to have thread-level
information.
b. In addition, running jconsole on the worker (executor) process on a
worker node yields the number of active threads, but I found this kind of
difficult to work with. I've been using a McGyver-ish technique where I
observe the process number that is spawned when running an application, and
then running "jconsole procNum" to get a visualization of number of threads
across the application. The thing is this takes a few seconds and so by the
time jconsole starts running, I already see 40 active threads.

Here's an image describing the sort of thing I'm getting:

<http://apache-spark-developers-list.1001551.n3.nabble.com/file/n12898/proc.png> 

In addition, jconsole doesn't show the "performance" associated with
specific threads.

Ideally I would like to monitor the process from the time before it's
spawned (starting out with 0 active threads) and observe the thread growth /
utilization over time. Not sure if this is possible to do with
Spark/jconsole.

c. Maybe someone who is familiar with JVM's / OS has a suggestion about how
to go about this, or if this is feasible to do. I am able to do some
verification about the utilization of cores by checking "top" on Linux for
low task numbers, but my machines  only have 4 physical cores -i.e. if only
1 task is running, top shows only one core being utilized, 2 tasks shows two
cores being utilized, etc. I want to do some testing with larger numbers of
threads, for example 8 or 16, which is greater than 4.  

2. There is a listing called "active tasks" in the executor tab, which
yields an indication of parallelism at a certain moment. Is it safe to
assume one task is assigned to one (separate) thread on a worker node? For
example, if there are 3 tasks running on a node, are there three separate
threads running?

3. In addition, could you point me to where in the source cluster-wide /
slave-node-specific "parallelism" is actually implemented in the Spark
source code, or is threading simply deferred to details specified by JVM or
YARN? For example, in  in a comment by Sean Owen
<http://stackoverflow.com/questions/27606541/why-does-spark-streaming-need-a-certain-number-of-cpu-cores-to-run-correctly>

, it is stated that YARN needs to reserve entire physical cores and so it
seems that you can't set your SPARK_WORKER_CORES to a number greater than
the number of physical cores. Furthermore, it seems that "cores" is not a
very good name for specifying these parameters (I've been playing around
with local / standalone mode mostly), and perhaps, should possibly be
changed to just "threads" in the future?

Thanks,

Jonathon



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