Hewlett-Packard x4000 Workstation
What I was most concerned with in replacing our tried-and-true SGI workstations with Linux-based desktops were the little glitches that lurk in the dark corners, unexpectedly ruining one's experience. Other UNIX-based workstations don't have nearly the price/performance ratio of PCs, but they are relatively bulletproof. The qualification and testing that SGI or Sun put their machines through guarantees some amount of freedom from problems.
HP did their homework here and built a very solid machine. There were none of the display glitches that we find in consumer-grade machines. The FireGL4 was absolutely problem-free, no speckling pixels during refreshes, no polygons where they shouldn't be, no problems of any kind. The OpenGL drivers seem to be every bit as mature as those on other workstations.
We did have minor problems that might be expected with machines of this scale. Maya refused to run at first, claiming to be out of memory (this turned out paradoxically to be a problem of having too much memory). The 4GB in the machine is too large for the 32-bit integer that Maya was using to store the amount of free memory, wrapping around to a negative value. Booting Linux with mem=2048k yielded perfect Maya performance.
There was a bit of a learning curve for us as well with the window manager. Maya depends on using Alt-mouse button chords to move the camera. It took us longer than it should have to realize that GNOME had already intercepted these events—once we disabled those combinations our camera moves worked as they should.
With RAYZ, there was a minor marking-menu problem, but fairly simple workarounds were suggested by Silicon Grail.
The x4000 has an array of four diagnostic LEDs on the front panel. These started flashing at one point, and the code was deciphered as indicating a low CMOS battery. Reseating the battery, as suggested by the manual, caused the problem to go away.
In general, I found the problems to be remarkably few and easy to deal with. The software packages came up cleanly and easily with no configuration headaches. Companies like HP, Alias|Wavefront, Nothing Real and Silicon Grail recognize the size and importance of the Linux desktop workstation community and devote the required resources to release tested, well-qualified solutions.
HP has made a formidable computer in the x4000. At least in the fully outfitted configuration that we tested, it was a serious competitor to anything from the traditional workstation vendors. All of the advanced commercial software that we loaded worked as expected, as did our in-house software. In every particular area the performance was as high as anything we've seen, and all aspects of the machine worked synergistically to provide a great workstation.

Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
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- New Products
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Enter to Win an Adafruit Pi Cobbler Breakout Kit for Raspberry Pi

It's Raspberry Pi month at Linux Journal. Each week in May, Adafruit will be giving away a Pi-related prize to a lucky, randomly drawn LJ reader. Winners will be announced weekly.
Fill out the fields below to enter to win this week's prize-- a Pi Cobbler Breakout Kit for Raspberry Pi.
Congratulations to our winners so far:
- 5-8-13, Pi Starter Pack: Jack Davis
- 5-15-13, Pi Model B 512MB RAM: Patrick Dunn
- 5-21-13, Prototyping Pi Plate Kit: Philip Kirby
- Next winner announced on 5-27-13!
Free Webinar: Hadoop
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Some of key questions to be discussed are:
- What is the “typical” Hadoop cluster and what should be installed on the different machine types?
- Why should you consider the typical workload patterns when making your hardware decisions?
- Are all microservers created equal for Hadoop deployments?
- How do I plan for expansion if I require more compute, memory, storage or networking?




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