[Baypiggies] [ANN] Scientific Python at UC Berkeley talk for November 18

[Rami Chowdhury]

On Nov 14, 2009, at 02:35 , Stephen McInerney wrote:

> +1 definitely. Thanks Fernando.
> It's impossible to make it up to Berkeley during the workday otherwise I'd come.
>  
> Stephen

+1 here too. As Stephen said -- if I could make it, I'd definitely come.

>  
> > Date: Sat, 14 Nov 2009 02:18:32 -0800
> > From: fperez.net at gmail.com
> > To: baypiggies at python.org
> > Subject: [Baypiggies] [ANN] Scientific Python at UC Berkeley talk for November 18
> > 
> > Hi all,
> > 
> > I would like to know if in general people want to get these
> > announcements regarding the UC Berkeley py4science seminars on this
> > list or not, I don't want to spam people. But last week's event with
> > Guido drew a huge crowd, some from this list, so perhaps there is
> > interest (obviously Guido's visit was a special event, but still
> > there may be interest in these talks amongst this group). If there
> > isn't let me know and I won't post them again, as you can always see
> > them at the group page:
> > 
> > https://cirl.berkeley.edu/view/Py4Science/WebHome
> > 
> > But if you find these useful, I'm happy to send the announcements
> > here, it will be limited to just one email every 2 weeks at most.
> > 
> > So for now here's next week's, I'll refrain from posting these again
> > if so requested.
> > 
> > Next week, we will have a presentation by Bryan Catanzaro from EECS
> > on Copperhead, the system we got a glimpse of last week from his
> > lightning talk. Details below.
> > 
> > 
> > Thanks,
> > 
> > f
> > 
> > When: November 18, 2pm.
> > 
> > Where: UC Berkeley, Evans 508-20
> > 
> > Title: Copperhead: Data-Parallel Python
> > 
> > Abstract:
> > The need for productive programming languages which can avail
> > themselves of parallel hardware has never been more acute. The
> > Copperhead project attempts to address this problem by defining a
> > subset of Python which can be compiled and executed in a data-parallel
> > fashion. Copperhead procedures are expressed as standard, fully-legal
> > Python procedures operating on Numpy datatypes, which are intercepted,
> > specialized, and compiled to parallel C code at runtime, and then
> > executed on a high-performance parallel platform. Since the
> > Copperhead runtime supports only a subset of Python, the runtime will
> > revert to standard Python execution if specialization fails. The
> > current Copperhead runtime targets Nvidia Graphics Processors, which
> > are highly suited for data-parallel computation and provide high
> > performance. In this talk, I will be discussing the current state of
> > Copperhead, as well as plans for future development.
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