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Over the weekend it was announced IBM was buying Red Hat. A lot of gasps in the community. Personally, I'm not surprised and some other strategic moves may follow. My guess is Canonical's phone is ringing today. I sometimes think these acquisitions are forty percent about the technology/people and sixty percent about keeping it out of the hands of competitors. In this case, I believe it is about renting remote servers, sorry, cloud computing.

From the do-you-want-fries-with-that department

As processor frequencies continue to level off and mainstream processors keep sprouting cores, the end of the "frequency free lunch" has finally arrived. That is, in the good old days each new generation of processor would bring with it a faster system clock that would result in a "free" performance bump for many software applications -- no reprogramming needed. Can we ever get back to the good old days?

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.)

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.

From the cult of personality department

Image Ever since Watson became the Jeopardy Champ, I have been following his (its) developments. IBM and others seem to think deep learning AI's are the future. Recently, I happened upon this web page for IBM Watson Personality Insights where I can "Gain insight into how and why people think, act, and feel the way they do. This service applies linguistic analytics and personality theory to infer attributes from a person's unstructured text. The web page invites you to submit writing samples that are analyzed by Watson using some kind of linguistic API. Such a page is ripe for testing.


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