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07:51
<
ShubhamAgrawal[m >
> <@shubhamag:matrix.org> I think it will not hurt to implement this as a separate internal method in mlpack. But first try to run test with replaced implentation.
07:51
<
ShubhamAgrawal[m >
And can you link the reproducible examples you created
07:51
<
ShubhamAgrawal[m >
> Anyone with different thoughts?
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11:09
<
GopiMTatiraju[m] >
* zoq: rcurtin when we do `mat.get_dev_mem()` the memory which we get is that contiguous?
11:09
<
GopiMTatiraju[m] >
when I am doing
11:09
<
GopiMTatiraju[m] >
`cudaMemcpy(dest, source1, size1, cudaMemcpyDeviceToDevice); cudaMemcpy(dest+size1, source2, size2, cudaMemcpyDeviceToDevice);`
11:09
<
GopiMTatiraju[m] >
Although I am not getting any error but values seem random.
11:10
<
GopiMTatiraju[m] >
When I tried it with a basic c style 1D array the above worked.
11:11
<
GopiMTatiraju[m] >
* zoq: rcurtin when we do `mat.get_dev_mem()` the memory which we get is that contiguous?
11:11
<
GopiMTatiraju[m] >
Although I am not getting any error but values seem random.
11:11
<
GopiMTatiraju[m] >
when I am doing
11:11
<
GopiMTatiraju[m] >
```cudaMemcpy(dest, source1, size1, cudaMemcpyDeviceToDevice); cudaMemcpy(dest+size1, source2, size2, cudaMemcpyDeviceToDevice);`
11:22
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<
EshaanAgarwal[m] >
s/://, s///, s///
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14:05
<
rcurtin[m] >
yeah, all memory for `mat` objects is contiguous (whether it's OpenCL or CUDA memory)
14:08
<
jonpsy[m] >
hey guys, I have a generic question.
14:08
<
jonpsy[m] >
So, I have a complex logic where I take every combination of every length of a list and run it through an optimizer.
14:08
<
jonpsy[m] >
For ex: if my list is ['man', 'ban', 'can'], I'll run ['man'], ['can'], ['ban'], ['man', 'ban'], ['man','can'] ... ['man','ban','can']. aka this would grow exponentially with `len(list)`.
14:08
<
jonpsy[m] >
The catch is, I know the max len is 8, so at max it could grow to 2^8 combinations. I was wondering can I parallelize this since so that each thread takes 1 of the 256 tasks (in the worst case).
14:13
<
rcurtin[m] >
certainly you could parallelize it, but is it worth it? that will depend on how much time each individual optimization takes; if it's a trivial operation, the overhead of parallelization may not be worthwhile
14:14
<
rcurtin[m] >
you can use things like OpenMP `task`s; that may make the implementation easier
14:14
<
jonpsy[m] >
We're using python
14:14
<
rcurtin[m] >
(if you're going to do it in C++ with OpenMP, that is :))
14:14
<
jonpsy[m] >
rcurtin[m]: hehe
14:14
<
rcurtin[m] >
I don't know anything about Python parallelism
14:15
<
jonpsy[m] >
rcurtin[m]: I'd say yeah. The optimization process is
**very** costly like about 50 seconds or so, if we run 2^8 * 50 it would take forever
14:16
<
jonpsy[m] >
rcurtin[m]: nw you can just tell me if its a valid thing to do. Since my knowledge about parallelism is very limited
14:19
<
Liu[m] >
hey Curtin, would u mind take a look at my question above? Thanks
14:21
<
rcurtin[m] >
Liu: I saw it, I'll do that when I have a chance. 👍️ like Eshaan Agarwal pointed out, I think this has been discussed before
14:26
<
Liu[m] >
Is the solution mentioned in StackOverflow acceptable in mlpack? If so, I’d love to have a try 🙂
14:28
<
rcurtin[m] >
I have no idea, I haven't had a chance to look at the issue and formulate a response---I can't do that for you right now, I will get to it when I get to it 👍️
14:29
<
Liu[m] >
Sure, I’ll look at some other simple issues and have a try. Thanks~
14:29
<
GopiMTatiraju[m] >
<zoq[m]> "> <@gopitatiraju:matrix.org> zoq..." <- dest and source1 are matrix.
14:29
<
GopiMTatiraju[m] >
And I used sizeof(eT)*size_t(n_elem)
14:33
<
GopiMTatiraju[m] >
When I am filling the `dest` all at once it's working, but when I am trying to fill it in parts(like I mentioned above) the values are incorrect....
16:33
<
zoq[m] >
zoq[m]: Returns the expected results.
16:34
<
zoq[m] >
zoq[m]: As I mentioned keep in mind the size you like to copy is in bytes.
16:35
<
zoq[m] >
zoq[m]: `cudaMemcpy(dest+size1, source2, size2, cudaMemcpyDeviceToDevice);` isn't correct because you used `size1` for two different things.
16:36
<
zoq[m] >
zoq[m]: To iterate the pointer and to specify the number of bytes to copy.
16:39
<
GopiMTatiraju[m] >
Oh okay...
16:39
<
GopiMTatiraju[m] >
I will make the changes accordingly and update...
18:39
<
TarekNasser[m] >
Hey
18:39
<
TarekNasser[m] >
I am trying to contribute to example zoo project in GSoC this year
18:39
<
TarekNasser[m] >
What can I do next to enhance my chances?
18:39
<
TarekNasser[m] >
I did an example using Python and made a pull request in the example repository
18:53
<
zoq[m] >
zoq[m]: Also, if you are interested, you can take a look at some of the open issues or PR, having more eyes on code is always a good thing.
18:53
<
zoq[m] >
<GopiMTatiraju[m]> "Hey zoq , I commented on the..." <- Looks like, you figured it out, column major :)
21:16
<
GopiMTatiraju[m] >
zoq: rcurtin done with the implementation, also added tests.
21:16
<
GopiMTatiraju[m] >
Pushed the code...
22:21
<
rcurtin[m] >
zoq: do you think we should do a new ensmallen release now that the Adam fix is merged? or do you want to merge #211 first too?
22:21
<
rcurtin[m] >
(and #231)
22:22
<
zoq[m] >
Ideally we can include #211 as well, it's ready from my side.
22:23
<
zoq[m] >
I think #231 needs some extra work, so we can just include it in the next one.
22:35
<
rcurtin[m] >
ahh, ok, I'll try to review that today and then we can finally get it merged. hopefully before the two year mark 😄