Would like to mention that the non-reducer version of @parallel is
asynchronous. Before you can use Ans1 and Ans2, you should wait for
completion.
For example, if you need to time it, you can wrap it in a @sync block like
this:
@time @sync begin
@parallel .....
....
end
end
On Mon, Feb 3, 2014 at 10:25 PM, David Salamon <[email protected]> wrote:
> I have no experience with it, but it looks like you could also just do:
>
> Ans1 = SharedArray(Float64, (limit, int64(limit/2))
> Ans2 = SharedArray(Float64, (limit, int64(limit/2))
>
> @parallel for sample=1:samples, i=1:limit, j=1:int64(limit/2)
> Sx = S[i, sample]
> Sy = S[j, sample]
> Sxy = S[i+j, sample]
> ...
>
> Ans1[i,j] = Aix * Bix / samples / samples
> Ans2[i,j] = Cix / samples
> end
>
> return (Ans1, Ans2)
>
>
> On Mon, Feb 3, 2014 at 8:48 AM, David Salamon <[email protected]> wrote:
>
>> Also S[:,1] is allocating. it should look something like:
>>
>> for sample=1:samples, i=1:limit, j=1:int64(limit/2)
>> Sx = S[i, sample]
>> Sy = S[j, sample]
>> Sxy = S[i+j, sample]
>> ...
>> end
>>
>>
>> On Mon, Feb 3, 2014 at 8:45 AM, David Salamon <[email protected]> wrote:
>>
>>> You're not out of the no-slicing woods yet. Looks like you can get rid
>>> of `mx` and `my`
>>>
>>> for i=1:limit, j=1:int64(limit/2)
>>> end
>>>
>>>
>>>
>>> As far as parallelizing, you could define:
>>> three_tup_add(a, b, c) = (a[1] + b[1] + c[1], a[2] + b[2] + c[2], a[3] +
>>> b[3] + c[3])
>>>
>>> and then do a @parallel (three_tup_add) over your sample index?
>>>
>>> for that matter, why not compute the two parts of the answer directly
>>> rather than going via A, B, and C?
>>>
>>>
>>>
>>>
>>>
>>>
>>> On Mon, Feb 3, 2014 at 8:11 AM, Alex C <[email protected]> wrote:
>>>
>>>> Thanks. I've re-written the function to minimize the amount of copying
>>>> (i.e. slicing) that is required. But now, I'm befuddled as to how to
>>>> parallelize this function using Julia. Any suggestions?
>>>>
>>>> Alex
>>>>
>>>> function expensive_hat(S::Array{Complex{Float64},2},
>>>> mx::Array{Int64,2}, my::Array{Int64,2})
>>>>
>>>> samples = 64
>>>> A = zeros(size(mx));
>>>> B = zeros(size(mx));
>>>> C = zeros(size(mx));
>>>>
>>>> for i = 1:samples
>>>> Si = S[:,i];
>>>> Sx = Si[mx];
>>>> Sy = Si[my];
>>>> Sxy = Si[mx+my];
>>>> Sxyc = conj(Sxy);
>>>>
>>>> A += abs2(Sy .* Sx);
>>>> B += abs2(sqrt(Sxyc .* Sxy));
>>>> C += Sxyc .* Sy .* Sx;
>>>> end
>>>>
>>>> ans = (A .* B ./ samples ./ samples, C./samples)
>>>> return ans
>>>>
>>>> end
>>>>
>>>> data = rand(24000,64);
>>>> limit = 2000;
>>>>
>>>> ix = int64([1:limit/2]);
>>>> iy = ix[1:end/2];
>>>> mg = zeros(Int64,length(iy),length(ix));
>>>> mx = broadcast(+,ix',mg);
>>>> my = broadcast(+,iy,mg);
>>>> S = rfft(data,1)./24000;
>>>>
>>>> @time (AB, C) = expensive_hat(S,mx,my);
>>>>
>>>>
>>>
>>>
>>
>
