[Haskell-cafe] Accelerating Automatic Differentiation

Michal J Gajda mgajda at mimuw.edu.pl
Sat Mar 24 22:32:28 UTC 2018

As a mentor I would say it is certainly possible to outperform exsting
mega-solutions in some narrow domain.
Just as I did with hPDB https://hackage.haskell.org/package/hPDB
But it requires a lot of skill and patiece.

Please proceed with this project with the current list of mentors.
I think me and Dominic have already declared committment.

You might also start by making a table of best competing solutions in
other. languages, their respective strengths, and ways that we can possibly
improve on them!

Where do You keep Your application draft? Ideally it should be a shared
space where you can add mentors as co-editors.
On Sun, 25 Mar 2018 at 02:32, <dominic at steinitz.org> wrote:

> The list of mentors for this project looks great to me. I am not sure if I
> can add much other than I think this is a nice project. Perhaps it would be
> best to get the advice of some of the mentors?
> For some very simple tests with an ODE solver, I concluded that accelerate
> can perform at least as well as Julia. It would certainly be very helpful
> to be able to get Jacobians for ODE solving and for other applications.
> Dominic Steinitz
> dominic at steinitz.org
> http://idontgetoutmuch.wordpress.com
> Twitter: @idontgetoutmuch
> On 24 Mar 2018, at 17:20, Charles Blake <cb307 at st-andrews.ac.uk> wrote:
> Thanks for the response Michal,
> Yes, this did cross my mind - and I wouldn't be expecting to outperform
> those frameworks in the timeframe available! I assumed that the reason that
> this project was suggested was perhaps:
> a) there is some intrinsic value in implementing these algorithms natively
> in haskell (hence why the 'ad' library was developed in the first place),
> so that those who want to use parallel automatic differentiation / the
> machine learning algorithms built on top of it can do so without leaving
> the haskell ecosystem,
> and b) because the challenges involved in implementing parallel ad in a
> purely functional language are a little different to those involved in
> doing so in OO/imperative languages - so it might be interesting from that
> angle as well?
> So perhaps my aim would no be to do something unique, but rather to do
> something that has already done well in other languages, but has not yet
> been provided as a haskell library. Does this sound like a reasonable
> approach or do I need to find a slightly more unique angle?
> Thanks,
> Charlie
> ------------------------------
> *From:* Michal J Gajda <mgajda at mimuw.edu.pl>
> *Sent:* 24 March 2018 16:56:35
> *To:* Dominic Steinitz; Marco Zocca; accelerate-haskell at googlegroups.com;
> Charles Blake; haskell-cafe at haskell.org
> *Subject:* Re: Accelerating Automatic Differentiation
> Hi Charlie
> It certainly looks like exciting project, but the bar is currently placed
> very high.
> TensorFlow package not only provides automatic differentiation for whole
> programs, but also optimizes data processing both on GPU, and reading to
> achieve large batches.
> This field has a lot of hot developments, so You would either need to
> propose something unique to Haskell, or You risk being outclassed by
> PyTorch and TensorFlow bindings
>   Maybe Dominic suggests something too.
>  Cheers
>       Michal
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