[concurrency-interest] Some interesting (confusing?) benchmark results

thurston at nomagicsoftware.com thurston at nomagicsoftware.com
Tue Feb 12 04:55:09 EST 2013

Yes, I will push the code and my measurements some time this week.  I'm 
really interested in seeing what others' results would be.  What I can 
say is that the timings are surprisingly stable (range 30-40ms) and the 
input data is randomly generated for each test run which can certainly 
affect merge(int[], int[])'s performance

On 2013-02-12 01:50, Kirk Pepperdine wrote:
> Hi,
> On 2013-02-11, at 9:52 PM, thurston at nomagicsoftware.com wrote:
> Hi T,
> Can you pub the raw data from your runs? Average tends to hide
> effects and it's difficult to say anything with only that measure.
> Regards,
> Kirk
>> Hello,
>> I made some initial attempts at using ForkJoin framework for some 
>> rather obvious recursively parallel use-cases, namely summing the 
>> elements of an int[] and also sorting an int[] (randomly filled).
>> The problem-sizes I used ranged from 1M - 10M.
>> I really enjoy using the framework and find it relatively easy to 
>> reason about my programs (if not necessarily the internals of the 
>> framework).
>> But I was disappointed with the results: in both cases they were 
>> slower than the corresponding Java serial implementation.
>> I completely understand the principle of YMMV, and I wasn't 
>> expecting 2x speedup, but especially in the case of the sort, but I 
>> was expecting that ForkJoin would do at least a little better than the 
>> single-threaded version.  I guess what I'm asking is: are these 
>> results surprising?  Does it mean I'm doing something wrong?
>> I'll give a brief description of my implementation:
>> single-threaded:  Arrays.sort(int[])
>> ForkJoin: I'm using a merge-sort, with THRESHOLD = 250 (arrived at 
>> by trial-and-error)
>> int[] compute()
>> {
>> if int[].length < THRESHOLD
>>    return insertionSort(int[])
>> left = new MergeSort(int[] //split in half)
>> right = new MergeSort(int[] //other half)
>> return merge(right.compute(), left.join())
>> }
>> The insertionSort() and merge() methods are just standard 
>> implementations; there is a bit of apples to oranges comparison since 
>> Arrays.sort() uses an optimized quicksort, but we're still talking 
>> O(nlog(n))
>> Just ran it on my laptop:
>> Windows 7 64-bit
>> 1.7.0_03; Java HotSpot(TM) 64-Bit Server VM 22.1-b02
>> Core 2 Duo==> 2 cores 2GHz
>> 2M int[]:
>> single-threaded: ~330ms
>> ForkJoin (2 workers): ~390ms
>> Would appreciate any feedback and hopefully the #s are somewhat 
>> helpful (and please no scoffawing at my antiquated machine)
>> -T
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