[Baypiggies] [ANN] Scientific Python at UC Berkeley talk for November 18
spmcinerney at hotmail.com
Sat Nov 14 11:35:24 CET 2009
+1 definitely. Thanks Fernando.
It's impossible to make it up to Berkeley during the workday otherwise I'd 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:
> 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.
> When: November 18, 2pm.
> Where: UC Berkeley, Evans 508-20
> Title: Copperhead: Data-Parallel Python
> 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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