TACC Ranger Tackles Facial Recognition
August 11, 2008
Using the TACC Ranger Supercomputer, Rob Farber, senior scientist at the Pacific Northwest National Laboratory (PNNL), has demonstrated the ability to create searchable databases based on image recognition with massive amounts of data rather than text tagging. The research proved the potential of supercomputers to not only simulate virtual experiments, but also to organize, enrich, and improve our present, media-saturated world.
“Because Farber intends to utilize data sets thousands of times larger, he is particularly interested in getting the maximum performance out of the massively parallel supercomputers he uses. Utilizing compiler intrinsic operations that allow direct access to the processor’s SSE assembly instructions, he can coax four flops per clock cycle per core — the theoretical peak performance — from each AMD Opteron™ Barcelona core on the floating point intensive part of his code, while scaling in a near-linear fashion up to 32,768 cores. His approach involves optimizing his code for Ranger’s architecture and minimizing the communications among nodes.
“It takes people who are cognizant of both the algorithms plus the runtime and communications behavior of their algorithms, to scale successfully on massively parallel systems,” Farber said. “For Ranger to get to four flops per clock, I had to rewrite some of the code to use the compiler’s SSE intrinsic operations – basically using the assembly language instructions. That really lit Ranger on fire.” Full Story