[Haskell-cafe] Accelerating Automatic Differentiation
dominic at steinitz.org
dominic at steinitz.org
Sun Mar 25 07:17:03 UTC 2018
I didn’t volunteer to be a mentor for this. The project already lists:
> Mentor: Fritz Henglein, Gabriele Keller, Trevor McDonell, Edward Kmett, Sacha Sokoloski
I doubt there is much I could add to such an illustrious list.
dominic at steinitz.org
> On 25 Mar 2018, at 06:11, Michal J Gajda <mgajda at mimuw.edu.pl> wrote:
> Given that Marco did not confirm, I am just now confirming that we can get you mentored by Mikhail Baikov as the third mentor (beside Dominic and me).
> Both me (Michal) and Mikhail are performance optimization experts (I am in parsers, and data analytics, Mikhail is in real-time systems, he has his own top-notch serialization library - Beamable that outperforms Cereal in both data size and speed). Dominic is expert in numerical computing (ODEs and Julia, among other things).
> I believe that with these three excellent mentors you have very good chance to make outstanding contribution.
> We just make sure that you prep application by 27th deadline.
> On Sun, Mar 25, 2018 at 6:32 AM Michal J Gajda <mgajda at mimuw.edu.pl <mailto:mgajda at mimuw.edu.pl>> wrote:
> 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 <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 <mailto: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 <mailto:dominic at steinitz.org>
> http://idontgetoutmuch.wordpress.com <http://idontgetoutmuch.wordpress.com/>
> Twitter: @idontgetoutmuch
>> On 24 Mar 2018, at 17:20, Charles Blake <cb307 at st-andrews.ac.uk <mailto: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?
>> From: Michal J Gajda <mgajda at mimuw.edu.pl <mailto:mgajda at mimuw.edu.pl>>
>> Sent: 24 March 2018 16:56:35
>> To: Dominic Steinitz; Marco Zocca; accelerate-haskell at googlegroups.com <mailto:accelerate-haskell at googlegroups.com>; Charles Blake; haskell-cafe at haskell.org <mailto: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.
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