Flip Phillips found an “insightful” way to accelerate his research this summer at Skidmore College. Phillips and his colleagues are studying human vision, primarily 3D shape perception, or “how we see what we see.”

Phillips’s focus is on empirical studies and actual laboratory testing. When these empirical studies do not match up to existing theoretical models, the onus is on Phillips to prove or disprove their validity, and he is using gridMathematica as one of his primary tools for doing so.

Testing the existing theoretical models requires computations–and lots of them. Phillips, who has an arguably fast 2 GHz dual-processor PowerMac G5, was faced with a daunting task when he realized that his machine would take 1.5 to 2 months for each individual test of a popular theoretical model against his current empirical data. So he took advantage of Skidmore’s Unlimited site program for all Wolfram Research products and sought out the resources of the gridMathematica computing cluster in the Skidmore Computer Science department.

Skidmore’s computer science lab includes a rack of dual-processor Apple Xserve G4 machines. Even though using this setup significantly reduced his computing time to only 2 to 2.5 weeks per test, Phillips still wanted a faster way to get results. This past summer, when computer usage on campus was low, it occurred to Phillips to extend his grid by “scavenging” for all the Macintosh machines sitting idle throughout the campus network.

Phillips wrote a series of programs, run on the main grid, to go out and find Macs with free time to use. When a free machine is found, the program loads gridMathematica and any needed data, and starts to calculate. If the machine is needed before the current process is completed, Mathematica finishes its calculations at a slowed pace in the background until the machine is free again.

Using gridMathematica, Phillips has been able to reduce computation times for the exact same processes from 1.5 to 2 months down to only 4 to 6 hours. With some adjustments, such as reviewing intermediate results and “pruning off” undesirable branches, the process could be made even faster.

Phillips was inspired by an experience from when he worked at Pixar in the 1980s. After a friend won the student Academy Award for best animated short film, part of the prize winnings were used to send the sketches overseas for colorization. When the thousands of original drawings were lost in transit, they found themselves trying to transfer the images from film to computer data in order to recreate them. To help accelerate the process–which still took about a year–they wrote a program that performed a similar “scavenging” process on all of the Pixar laser printers.

Having finally found a way to perform the necessary calculations in much less time than ever before, Phillips and his colleagues are preparing a paper for publication. Interestingly enough, what they have found is that the model they set out to disprove is not entirely incorrect. The theoretical model predicts the empirical results for a constrained set of circumstances. However, for a whole range of other conditions the model failed to accurately predict the empirical results.

Using gridMathematica has allowed Phillips to take his vision research to the next level. Rather than rejecting or accepting a theory as a whole due to computational constraints, he is now able to find out what parts of the theory may be usable, and under what conditions.