
On 22 March 2011 02:00, Jesper Louis Andersen
On Tue, Mar 22, 2011 at 00:59, David MacIver
wrote: It's for rank aggregation - taking a bunch of partial rankings of some items from users and turning them into an overall ranking (aka "That thing that Hammer Principle does").
Two questions immediately begs themselves:
* Can we go parallel? :P
Maybe. A lot of this is inherently sequential. Some bits are parallelisable, but my initial attempts at exploiting that made very little performance difference. I'd rather exhaust what I can from single-core performance first.
* What does +RTS -s -RTS say? Specifically, what is the current productivity?
./rank +RTS -s 3,466,696,368 bytes allocated in the heap 212,888,240 bytes copied during GC 51,949,568 bytes maximum residency (10 sample(s)) 5,477,016 bytes maximum slop 105 MB total memory in use (0 MB lost due to fragmentation) Generation 0: 6546 collections, 0 parallel, 0.93s, 0.93s elapsed Generation 1: 10 collections, 0 parallel, 0.32s, 0.32s elapsed INIT time 0.00s ( 0.00s elapsed) MUT time 7.11s ( 7.12s elapsed) GC time 1.25s ( 1.25s elapsed) EXIT time 0.00s ( 0.00s elapsed) Total time 8.37s ( 8.37s elapsed) %GC time 15.0% (15.0% elapsed) Alloc rate 487,319,292 bytes per MUT second Productivity 85.0% of total user, 85.0% of total elapsed So if I'm reading this right, my hypothesis that allocation was most of the cost seems to be wrong? I don't know how much of that MUT time is allocation, but I'd expect it to be < GC time.
Do we get an improvement with +RTS -A2m -H128m -RTS ? (Force the heap to be somewhat up there from day one, perhaps try -H256m.
This seems to consistently give about a 0.4s improvement, which isn't nothing but isn't a particularly interesting chunck of 8s (actually it's 8.4s -> 8s). Setting it to 256M doesn't make any difference.