Re: ndslice, using a slice in place of T[] in template parameters
On Monday, 11 January 2016 at 00:50:37 UTC, Ilya Yaroshenko wrote: I will add such function. But it is not safe to do so (Slice can have strides not equal to 1). So it is like a hack (&ret[0, 0, 0])[0 .. ret.elementsCount]). Have you made comparison between my and yours parallel versions? https://github.com/9il/examples/blob/parallel/image_processing/median-filter/source/app.d -- Ilya Thanks. No, I haven't studied it previously, but I see how you used the 'hack' in your code, and it works out to the statement below in my case. medians[i] = median(vec, (&slb[task,0])[0 .. bigd]); which compiled. It ran in the faster time without the .array copying. parallel time medians msec:87 That 'hack' seems to be related to the third from below. https://dlang.org/spec/arrays.html b = a; b = a[]; b = a[0 .. a.length];
Re: ndslice, using a slice in place of T[] in template parameters
On Monday, 11 January 2016 at 00:39:04 UTC, Jay Norwood wrote: On Sunday, 10 January 2016 at 23:31:47 UTC, Ilya Yaroshenko wrote: Just use normal arrays for buffer (median accepts array on second argument for optimisation reasons). ok, I think I see. I created a slice(numTasks, bigd) over an allocated double[] dbuf, but slb[task] will be returning some struct instead of the double[] that i need in this case. If I add .array to the Slice, it does compile, and executes, but slower than using the buffer directly. medians[i] = median(vec, slb[task].array); parallel time medians msec:113 original version using the computed slice of the original allocated dbuf. medians[i] = median(vec,dbuf[j .. k]); parallel time medians msec:85 The .array appears to make a copy. Is there some other call in ndslice to return the double[] slice of the original array? I will add such function. But it is not safe to do so (Slice can have strides not equal to 1). So it is like a hack (&ret[0, 0, 0])[0 .. ret.elementsCount]). Have you made comparison between my and yours parallel versions? https://github.com/9il/examples/blob/parallel/image_processing/median-filter/source/app.d -- Ilya
Re: ndslice, using a slice in place of T[] in template parameters
On Sunday, 10 January 2016 at 23:31:47 UTC, Ilya Yaroshenko wrote: Just use normal arrays for buffer (median accepts array on second argument for optimisation reasons). ok, I think I see. I created a slice(numTasks, bigd) over an allocated double[] dbuf, but slb[task] will be returning some struct instead of the double[] that i need in this case. If I add .array to the Slice, it does compile, and executes, but slower than using the buffer directly. medians[i] = median(vec, slb[task].array); parallel time medians msec:113 original version using the computed slice of the original allocated dbuf. medians[i] = median(vec,dbuf[j .. k]); parallel time medians msec:85 The .array appears to make a copy. Is there some other call in ndslice to return the double[] slice of the original array?
Re: ndslice, using a slice in place of T[] in template parameters
On Sunday, 10 January 2016 at 23:24:24 UTC, Jay Norwood wrote: On Sunday, 10 January 2016 at 22:23:18 UTC, Ilya Yaroshenko wrote: Could you please provide full code and error (git gists)? -- Ilya ok, thanks. I'm building with DMD32 D Compiler v2.069.2 on Win32. The dub.json is included. https://gist.github.com/jnorwood/affd05b69795c20989a3 I have create parallel test to (it requires mir v0.10.0-beta ) https://github.com/9il/examples/blob/parallel/image_processing/median-filter/source/app.d Could you please create a benchmark with default values of nc & nc for single thread app, your parallel version, and my. My version has some additional overhead and I am interesting if it is significant. -- Ilya
Re: ndslice, using a slice in place of T[] in template parameters
On Sunday, 10 January 2016 at 23:24:24 UTC, Jay Norwood wrote: On Sunday, 10 January 2016 at 22:23:18 UTC, Ilya Yaroshenko wrote: Could you please provide full code and error (git gists)? -- Ilya ok, thanks. I'm building with DMD32 D Compiler v2.069.2 on Win32. The dub.json is included. https://gist.github.com/jnorwood/affd05b69795c20989a3 Just use normal arrays for buffer (median accepts array on second argument for optimisation reasons). BTW, dip80-ndslice moved to http://code.dlang.org/packages/mir -- Ilya
Re: ndslice, using a slice in place of T[] in template parameters
On Sunday, 10 January 2016 at 22:23:18 UTC, Ilya Yaroshenko wrote: Could you please provide full code and error (git gists)? -- Ilya ok, thanks. I'm building with DMD32 D Compiler v2.069.2 on Win32. The dub.json is included. https://gist.github.com/jnorwood/affd05b69795c20989a3
Re: ndslice, using a slice in place of T[] in template parameters
On Sunday, 10 January 2016 at 22:00:20 UTC, Jay Norwood wrote: I cut this median template from Jack Stouffer's article and was attempting to use it in a parallel function. As shown, it builds and execute correctly, but it failed to compile if I attempting to use medians[i] = median(vec,slb[task]); [...] Could you please provide full code and error (git gists)? -- Ilya
ndslice, using a slice in place of T[] in template parameters
I cut this median template from Jack Stouffer's article and was
attempting to use it in a parallel function. As shown, it
builds and execute correctly, but it failed to compile if I
attempting to use
medians[i] = median(vec,slb[task]);
in place of the
medians[i] = median(vec,dbuf[j .. k]);
Is there a cast needed?
import std.array : array;
import std.algorithm;
import std.datetime;
import std.conv : to;
import std.stdio;
import std.experimental.ndslice;
shared double[] medians;
double[] data;
shared double[] dbuf;
int numTasks;
const int smalld = 1000;
const int bigd = 10_000;
const int fulld = bigd*smalld;
/**
Params:
r = input range
buf = buffer with length no less than the number of elements in
`r`
Returns:
median value over the range `r`
*/
T median(Range, T)(Range r, T[] buf)
{
import std.algorithm.sorting: sort;
size_t n;
foreach (e; r) {
buf[n++] = e;
}
buf[0 .. n].sort();
immutable m = n >> 1;
return n & 1 ? buf[m] : cast(T)((buf[m - 1] + buf[m]) / 2);
}
void f3() {
import std.parallelism;
auto sl = data.sliced(smalld,bigd);
auto slb = dbuf.sliced(numTasks,bigd);
foreach(i,vec; parallel(sl)){
int task = taskPool.workerIndex;
int j = task*bigd;
int k = j+bigd;
medians[i] = median(vec,dbuf[j .. k]);
}
}
void main() {
import std.parallelism;
numTasks = taskPool.size+1;
data = new double[fulld];
dbuf = new double[bigd*numTasks];
medians = new double[smalld];
for(int i=0;i
