Hey Brad,
Thank you so much for the clear explanation. I was guessing there
could be a race condition. I will further read more on chapel as you said.
On Mon, Mar 17, 2014 at 11:12 AM, Brad Chamberlain <[email protected]> wrote:
>
> Hi Bibek --
>
> The following code:
>
>
> forall i in arow do
>> forall j in bcol do
>> forall k in acol do
>> c(i,j) += a(i,k)*b(k,j);
>>
>
> is problematic in a few ways: Because there are multiple tasks executing
> for the same (i,j) indices, you could have a read-read-write-write race on
> c(i,j). In particular, say that you're the task executing (i=1, j=1, k=1)
> and I'm the task executing (i=1, j=1, k=2). If we hit the inner loop at
> approximately the same time, we may both read the same value of c(i,j),
> each do our accumulation into our local copy of that read, and then each
> write our results back to c(i,j), in which case one of our two writes would
> get lost. This suggests you either shouldn't parallelize the k loop, or
> that you should use some sort of synchronization to ensure that this race
> doesn't occur (e.g., sync variables, atomics, or reductions).
>
>
> Correcting something you wrote:
>
> 2nd is a parallel code .. it creates i*j*k number of thread internally,
>>
>
> If by i*j*k threads, you mean |arow| * |bcol| * |acol| then this isn't
> correct -- forall loops generally create a number of tasks proportional
> to the machine's hardware parallelism, not to the number of iterations
> in the loop. E.g., running:
>
> forall i in 1..1000000
>
> will create here.numCores() tasks (a task per processor core), not
> 1,000,000 tasks. For more information on how tasks are created for forall
> loops by default, refer to "Controlling Degree of Data Parallelism" in
> README.executing in the doc/ directory of the release.
>
> -Brad
>
>
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