64-Bit JMP for Linux
One particular analytical strength of JMP sets it clearly apart from all other stats packages, open source or otherwise: its capabilities for design of experiment (DOE). The idea of experimental design goes beyond the traditional statistical concept of trying to learn from data that has already been collected, to an idea of planning how best to learn more by designing and running experiments.
Suppose you make tires by cooking various ingredients into rubber, combining that rubber with steel and other materials, then forming the rubber into tire-shaped molds of various dimensions and tread patterns. You might judge the success of those tires by measuring their traction in a variety of driving conditions, the range of operating temperatures and speeds they can endure, and the length of their usable lifetimes. You would be attempting to solve an impossibly complex problem. You would be trying to optimize at least four response measurements of varying importance and interactions that depend on an infinitely variable mixture of ingredients and countless combinations of cooking temperatures and times, molding pressures and times, tire dimensions and tread patterns. If you tried to optimize this manufacturing process using traditional methods, trying out every imaginable combination, you would be working for centuries and expending more natural resources than you could hope to collect.
Design of experiments offers a better way. By running a representative set of experiments that span the space of possibilities and using statistical modeling methods to interpolate and extrapolate those results, researchers can reduce the size of problems to something manageable.
The problem is that analysts always had to leaf through books searching for a design model that resembled the problem they were trying to solve. In practice, they would have to use a model that was sort of like their problem, that sort of handled their conditions and that sort of modeled the behaviors of their system. To make matters worse, most of these “canned designs” required huge numbers of runs. If a run costs a few cents and takes minutes, that's no problem, but if your experiment involves building or changing a multimillion-dollar semiconductor fabrication complex or shutting down a thousand-unit-per-minute assembly line, you cannot hope to do all the runs it would take to get meaningful results.
JMP takes DOE to a whole new level by providing unique, powerful custom design capabilities. Researchers can describe their problem precisely and fully, and JMP can determine smaller numbers of runs that will be sufficient. JMP's unique graphical factor profilers enable researchers to explore the result space over any combination of responses and factors and ultimately maximize desirabilities for their entire system in seconds. JMP's DOE has allowed blue-chip customers to discover million-dollar annual savings or profit opportunities in mere weeks. As Bradley Jones, Senior Manager of Statistical Development and chief architect of JMP's DOE capability, says, “Of all the statistical methods invented in the last 100 years, design of experiments is the most cost-beneficial.”
But JMP's unique power in areas, such as design of experiments and its newly introduced restricted maximum likelihood estimation of general linear models, are computationally intensive in the extreme. With JMP's new multithreaded architecture running on a 64-bit dual processor, experimental designs that once took several days to compute can now be calculated in minutes. And, problems that were impossible only last year can now be handled in minutes with 64-bit Linux JMP.
“When porting a 32-bit application to 64 bits, there are certain pitfalls you are likely to encounter”, says Potter. He continues:
The complexity is compounded if you support not only Linux but Windows and Macintosh as well, as we do with JMP. The key thing to remember is that in the 64-bit Linux architecture, pointers and longs are both 64-bits wide, while an int remains 32 bits. This breaks any code that assumes a pointer can be stored in an int.
If you have source code compiled on both Linux and Windows, you must be extra careful. Although it's perfectly legal to store a pointer in a long on 64-bit Linux, converting a pointer to a long won't work on Windows, where a long is still only 32-bits wide. On Windows any 64-bit pointers will have to be converted to long longs instead.
Until now floating-point operations on PowerPC for Macintosh used a 64-bit long double, but that may change in the future. PowerPC-based Macs use the “LP64” model in 64-bit executables, meaning that longs and pointers are 64-bit, just as they are on Linux. Apple has not yet announced a 64-bit strategy for Intel-based Macs.
Finally, although graphical user interface APIs are available to 64-bit applications on Linux and Windows, the Macintosh GUIs are not. Of course, you can separate your application into a 32-bit GUI that communicates with a 64-bit kernel, as Wolfram Research did with 64-bit Mathematica.
Nelson observed that porting JMP to 64 bits went smoothly. “The only issue was locating the few places where our code made assumptions about 32-bit word and pointer sizes”, says Nelson. “There were no surprises from the tools used for the port, either. Much of the ease in porting is due to the long heritage on 64-bit UNIX platforms of the open-source tools we used.”
“The GNU Compiler Collection, gdb, Emacs and the rest of the toolchain performed as expected on the x86_64 architecture”, says Nelson.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide