cameron <[email protected]> writes:
> There does seem to be something unusual about conj and
> clojure.lang.PersistentList in this parallel test case and I don't
> think it's related to the JVMs memory allocation.
I’ve got a few more data-points, but still no handle on what exactly is
going on.
My last benchmark showing the `conj*` speedup for `Cons` objects
degrading as soon as it was used on a `PersistantList` was incomplete.
In fact, the speedup degrades after it is used on objects of more than
one type. The effect just appears immediately when used with
`PersistantList` because '() is in fact a different a
`PersistantList$EmptyList`. Using `conj*` first in vector
implementation then results in the same inverse speedup on `Cons`s.
Even without your near-optimal speedup using Java standard library
types, I think your earlier benchmarks are enough to demonstrate that
this isn’t an issue with allocation alone. All of the implementations
based on `reduce` with `conj` must allocate and return a new object for
each iteration. If parallel allocation were the sole issue, I’d expect
all of the implementations to demonstrate the same behavior.
Unfortunately I have no idea what to connect from these facts:
- Parallel allocation of `Cons` and `PersistentList` instances through
a Clojure `conj` function remains fast as long as the function only
ever returns objects of a single concrete type
- Parallel allocation speed for `PersistentVector` instances is
unaffected by `conj` returning multiple types, and does not
demonstrate the inverse speedup seen for the previous types.
At this point I believe the symptoms point to cache contention, but I
don’t know where or why. Using OpenJDK 7 with -XX:+UseCondMark didn’t
appear to produce any improvement. Creating a private copy of
`PersistentList` which contained additional padding fields likewise
didn’t appear to produce any improvement.
So, Lee Spector: I think it’s possible to work around this though by
just not using `conj` on lists. It’s suboptimal, but at least solves
the problem in your original benchmark. Further improvements are
obviously possible, but that’s a start.
-Marshall
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