| Andrew Cooke | Contents | Latest | RSS | Twitter | Previous | Next

C[omp]ute

Welcome to my blog, which was once a mailing list of the same name and is still generated by mail. Please reply via the "comment" links.

Always interested in offers/projects/new ideas. Eclectic experience in fields like: numerical computing; Python web; Java enterprise; functional languages; GPGPU; SQL databases; etc. Based in Santiago, Chile; telecommute worldwide. CV; email.

Personal Projects

Lepl parser for Python.

Colorless Green.

Photography around Santiago.

SVG experiment.

Professional Portfolio

Calibration of seismometers.

Data access via web services.

Cache rewrite.

Extending OpenSSH.

Last 100 entries

Interesting (But Largely Illegible) Typeface; Retiring Essentialism; Poorest in UK, Poorest in N Europe; I Want To Be A Redneck!; Reverse Racism; The Lost Art Of Nomography; IBM Data Center (Photo); Interesting Account Of Gamma Hack; The Most Interesting Audiophile In The World; How did the first world war actually end?; Ky - Restaurant Santiago; The Black Dork Lives!; The UN Requires Unaninmous Decisions; LPIR - Steganography in Practice; How I Am 6; Clear Explanation of Verizon / Level 3 / Netflix; Teenage Girls; Formalising NSA Attacks; Switching Brakes (Tektro Hydraulic); Naim NAP 100 (Power Amp); AKG 550 First Impressions; Facebook manipulates emotions (no really); Map Reduce "No Longer Used" At Google; Removing RAID metadata; New Bike (Good Bike Shop, Santiago Chile); Removing APE Tags in Linux; Compiling Python 3.0 With GCC 4.8; Maven is Amazing; Generating Docs from a GitHub Wiki; Modular Shelves; Bash Best Practices; Good Emergency Gasfiter (Santiago, Chile); Readings in Recent Architecture; Roger Casement; Integrated Information Theory (Or Not); Possibly undefined macro AC_ENABLE_SHARED; Update on Charges; Sunburst Visualisation; Spectral Embeddings (Distances -> Coordinates); Introduction to Causality; Filtering To Help Colour-Blindness; ASUS 1015E-DS02 Too; Ready Player One; Writing Clear, Fast Julia Code; List of LatAm Novels; Running (for women); Building a Jenkins Plugin and a Jar (for Command Line use); Headphone Test Recordings; Causal Consistency; The Quest for Randomness; Chat Wars; Real-life Financial Co Without ACID Database...; Flexible Muscle-Based Locomotion for Bipedal Creatures; SQL Performance Explained; The Little Manual of API Design; Multiple Word Sizes; CRC - Next Steps; FizzBuzz; Update on CRCs; Decent Links / Discussion Community; Automated Reasoning About LLVM Optimizations and Undefined Behavior; A Painless Guide To CRC Error Detection Algorithms; Tests in Julia; Dave Eggers: what's so funny about peace, love and Starship?; Cello - High Level C Programming; autoreconf needs tar; Will Self Goes To Heathrow; Top 5 BioInformatics Papers; Vasovagal Response; Good Food in Vina; Chilean Drug Criminals Use Subsitution Cipher; Adrenaline; Stiglitz on the Impact of Technology; Why Not; How I Am 5; Lenovo X240 OpenSuse 13.1; NSA and GCHQ - Psychological Trolls; Finite Fields in Julia (Defining Your Own Number Type); Julian Assange; Starting Qemu on OpenSuse; Noisy GAs/TMs; Venezuela; Reinstalling GRUB with EFI; Instructions For Disabling KDE Indexing; Evolving Speakers; Changing Salt Size in Simple Crypt 3.0.0; Logarithmic Map (Moved); More Info; Words Found in Voynich Manuscript; An Inventory Of 3D Space-Filling Curves; Foxes Using Magnetic Fields To Hunt; 5 Rounds RC5 No Rotation; JP Morgan and Madoff; Ori - Secure, Distributed File System; Physical Unclonable Functions (PUFs); Prejudice on Reddit; Recursion OK; Optimizing Julia Code; Cash Handouts in Brazil; Couple Nice Music Videos; It Also Works!

© 2006-2013 Andrew Cooke (site) / post authors (content).

Image Processing with CUDA / Python (Dynamic Pipelines)

From: "andrew cooke" <andrew@...>

Date: Sun, 3 Aug 2008 13:20:21 -0400 (CLT)

I've been thinking about how best to apply CUDA to image processing
(particularly in astronomy, which is what I know).

Many image processing tasks are *very* suitable for parallelisation, since
the processing is "per-pixel" across several arrays (for example,
subtracting oen image from another).

So my idea is to write a parser (in Python) that accepts an expression in
a "language" that describes image processing and compiles that (literally)
to a CUDA process.

The motivation for this approach is to reduce the memory transfer overhead
compared to the alternative: a library of primitive operations.  If add,
multiple, etc are each separate primitives then data must be loaded for
each.  But in many cases it should be possible to combine these so that
all data are loaded just once, and the "per pixel" operation does several
steps.  Thie lowers the data transfer cost while at the same time grouping
the arithmetic processing into a single "chunk" (useful because it can run
while other data are loading).

It may not be clear that there is sufficient complexity, but my idea is to
support "error" and "uality" data too.

Andrew

Re: CUDA and astronomical image processing

From: andrew cooke <andrew@...>

Date: Sun, 8 Jul 2012 08:35:38 -0400

Hi,

I need to update my blog - it doesn't handle multipart mime messages like 
yours (which is why it wasn't displayed).

Anyway, no - I never got any further.  One reason was that I thought Theano
http://deeplearning.net/software/theano/ did a lot of what I was planning.  So
if you're looking for something like I described, you might want to consider
that.

Cheers,
Andrew


On Sun, Jul 08, 2012 at 11:47:42AM +0930, Andrew Cool wrote:
> Hi Andrew,
> 
> It's now 2012. Did you get anywhere with your CUDA processing?
> 
> Regards,
> 
> Andrew Cool
> 
> www.skippysky.com.au

CUDA and astronomical image processing

From: "Andrew Cool" <andrew@...>

Date: Sun, 8 Jul 2012 11:47:42 +0930

Hi Andrew,

It's now 2012. Did you get anywhere with your CUDA processing?

Regards,

Andrew Cool

www.skippysky.com.au

 

From: "andrew cooke" <andrew@...> 

Date: Sun, 3 Aug 2008 13:20:21 -0400 (CLT) 

I've been thinking about how best to apply CUDA to image processing

(particularly in astronomy, which is what I know).

 

Many image processing tasks are *very* suitable for parallelisation, since

the processing is "per-pixel" across several arrays (for example,

subtracting oen image from another).

 

So my idea is to write a parser (in Python) that accepts an expression in

a "language" that describes image processing and compiles that (literally)

to a CUDA process.

 

The motivation for this approach is to reduce the memory transfer overhead

compared to the alternative: a library of primitive operations.  If add,

multiple, etc are each separate primitives then data must be loaded for

each.  But in many cases it should be possible to combine these so that

all data are loaded just once, and the "per pixel" operation does several

steps.  Thie lowers the data transfer cost while at the same time grouping

the arithmetic processing into a single "chunk" (useful because it can run

while other data are loading).

 

It may not be clear that there is sufficient complexity, but my idea is to

support "error" and "uality" data too.

 

Andrew

 

 

All of us could take a lesson from the weather........ It pays no attention
to Criticism.

Comment on this post