YAML: Yet Another (Open) Machine Learner

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Written by Douglas Eadline

Published: 06 September 2016

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From the machine-overlord department

Previously, we highlighted several open (and GPU optional) machine learning packages. Recently Baidu has released a beta version of their ML tools. According to the git hub page,

"The software will be released on Sept. 30 with full documentation and installation support. A pre-release version is available now for those who are eager to take a look. PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.

One of the most interesting components of this release is a step-by-step quick start in Python!

You (Still) Can't Always Get What You Want

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Written by Douglas Eadline

Published: 25 February 2016

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You can't always get what you want. But that doesn't stop me from asking. Besides, Buddha's got my back. Here's my HPC wish list.

[Note: A recent article by Al Geist, How To Kill A Supercomputer: Dirty Power, Cosmic Rays, and Bad Solder reminds us that statistics at scale represents a big challenge for HPC. The following updated article was originally published in Linux Magazine in May 2006 and offers some further thoughts on this important issue. ]

Twenty five years ago I wrote a short article in a now defunct parallel computing magazine (Parallelogram) entitled "How Will You Program 1000 Processors?" Back then, it was a good question that had no easy answer. Today, it's still a good question with no easy answer, except now it seems a bit more urgent as we step into the "multi-core" era. Indeed, when I originally wrote the article, using 1,000 processors was a far off, but real possibility. Today, 1,000 processors are a reality for many practitioners of high-performance computing (HPC). And as dual-cores (and now 18-core processors) hit the server room, effectively doubling processor counts, many more people will be joining the 1,000P club very soon.

So let's get adventurous and ask, "How will you program 10,000 processors?" As I realized twenty five years ago, such a question may never really have a complete answer. In the history of computers, no one has ever answered such a question to my liking — even when considering ten processors. Of course, there are plenty of methods and ideas like threads, messages, barrier synchronization, and so on, but when I have to think more about the computer than about my problem, something is wrong.

The Ignorance is Bliss Approach To Parallel Computing

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Written by Douglas Eadline

Published: 09 June 2016

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from the random thoughts department

[Note: The following updated article was originally published in Linux Magazine in June 2006 and offers some further thoughts on the concept of dynamic execution.]

In a previous article, I talked about programming large numbers of cluster nodes. By large, I mean somewhere around 10,000. To recap quickly, I pointed out that dependence on large numbers of things increase the chance that one of them will fail. I then proposed that it would be cheaper to develop software that can live with failure than try to engineer hardware redundancy. Finally, I concluded that adapting to failure requires dynamic software. As opposed to statically scheduled programs, dynamic software adapts at run-time. The ultimate goal is to make cluster programming easier: focus more on the problem and less on the minutiae of message passing. (Not that there is anything wrong with message passing or MPI. At some level messages (memory) needs to be transferred between cluster nodes.)