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If today’s Wall Street Journal story is correct, and the ouster of Hewlett-Packard CEO Leo Apotheker has been discussed in HP’s boardrooms, then he is finished. You can’t head an organization that large if employees believe there’s any sort of…

NOAA put the cart before the horse to some degree when purchasing a new supercomputer to track hurricanes.

Because they were packed to the rafters at their existing data centers, NOAA built a new center to house this new 383 TF box. However, since they hadn’t awarded the contract for the system yet, they didn’t know exactly what to design into the new building. This article outlines a few of the choices they made and provides some interesting details. (See below…)

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The “Fastest Supercomputer” title may move 6,940 miles (11,167 KM) eastward in 2012 from Kobe, Japan to a small Tennessee town. That’s if the folks at the Oak Ridge National Labs (ORNL), along with Cray and AMD, can pull off a massive upgrade of the existing Jaguar system. They’ll be replacing existing nodes with the new Cray XK6 nodes, which will be running the new 16-core AMD Interlagos chips.

In the second half of 2012, these will be augmented by dual NVIDIA Kepler GPUs. Interlagos CPUs should be around 3x faster than their current hex core processors and the addition of Kepler GPUs (and lots of ‘em) will really crank up the performance potential. Overall, they’re expecting a 9x increase in speed, which will  put the system in the 10-20 PF range when it’s completed near the end of 2012. (See below…)

Cornell University’s Center for Advanced Computing (CAC) announced a new initiative to test GPU-optimized MATLAB for use on various research projects. They’ve partnered up with Dell, NVIDIA, and Mathworks to see what GPUs bring to the table in terms of the university’s research.

Cornell cites a couple of interesting data points in their press release (here link1). The first is a 15x speed-up (well, 14.7 to be tediously accurate) in processing images used to diagnose cancer cells. Pre-GPU, it took 86.9 seconds to process a single image. Post-GPU, that time plummets to 5.9 seconds. The benefit is obvious – this increases the theoretical maximum number of images they can process from 994 per day to 14,644.

But that’s not all… (see below…)

Baseball is perhaps the most boring thing in the world to watch. The leisurely rate of play, the lack of constant action, and the pauses players take for impromptu meetings, spitting, and crotch-grabbing are torture for my ADD-riddled brain.

Reading about baseball is every bit as bad, and reading about baseball-stats geeks who painstakingly ‘score’ every move on the field makes me want to beat myself with a bat.

On the other hand, I’m a big fan of money, and innovative ways to make more of it. I found a very interesting article in my ever-growing pile of Businessweek magazines about how automation and deep analytics are playing an increasingly large role in the game. The “Baseball: Running the New Numbers” story outlines, in highly readable form, how Major League Baseball, individual teams, and savvy techies are building out systems that will log pretty much everything that happens on a baseball field.

In a recent HPCwire report, Nicole Hemsoth discloses the back story behind a major storage purchase by Utah’s Center for HPC (CHPC). This story isn’t noteworthy because it’s a particularly large deal or because of their use case. It’s interesting because of its insider perspective on the process, taking the reader from the problem they’re trying to solve to the eventual solution.

It also shows how these deals aren’t simply based on vendors throwing the cheapest and fastest gear at the problem, and the customer picking the lowest bidder. So many situations that seem typical on the outside are actually quite complicated underneath the covers, with considerations other than cost per unit of raw performance becoming the decisive factors.