Hello Jeremy,
If this is sampling, not tracing, then how is it different from the
low-overhead memory profiling provided by JFR [1].
JFR samples per new TLAB allocation. It provides really very good
picture and I haven't seen overhead more than 2%.
Btw, JFR also does not have false positives reported by instrumented
approaches for the cases when JIT was able to eliminate heap allocation.
Thanks,
Vladimir.
[1] http://hirt.se/blog/?p=381
On 22.06.2015 11:48, Jeremy Manson wrote:
Hey folks,
(cc'ing Aleksey and John, because I mentioned this to them at the
JVMLS last year, but I never followed up.)
We have a patch at Google I've always wanted to contribute to OpenJDK,
but I always figured it would be unwanted. I've recently been
thinking that might not be as true, though. I thought I would ask if
there is any interest / if I should write a JEP / if I should just
forget it.
The basic problem is that there is no low-overhead supported way to
figure out where allocation hotspots are. That is, sets of stack
traces where lots of allocation / large allocations took place.
What I had originally done (this was in ~2007) was use bytecode
rewriting to instrument allocation sites. The instrumentation would
call a Java method, and the method would capture a stack trace. To
reduce overhead, there was a per-thread counter that only took a stack
trace once every N bytes allocated, where N is a randomly chosen point
in a probability distribution that centered around ~512K.
This was *way* too slow, and it didn't pick up allocations through
JNI, so I instrumented allocations at the VM level, and the overhead
went away. The sampling is always turned on in our internal VMs, and
a user can just query an interface for a list of sampled stack
traces. The allocated stack traces are held with weak refs, so you
only get live samples.
The additional overhead for allocations amounts to a subtraction, and
an occasional stack trace, which is usually a very, very small amount
of our CPU (although I had to do some jiggering in JDK8 to fix some
performance regressions).
There really isn't another good way to do this with low overhead. I
was wondering how the gruop would feel about our contributing it?
Thoughts?
Jeremy
