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:

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.