[Haskell-cafe] Accelerating Automatic Differentiation

[dominic at steinitz.org]

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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