2019 News
AAE's Qiao team applying jet ignition tech for cars
G.R.I.T.+ initiative launches at Welcome Picnic
Engineering announces PRIME grant recipients
ChE’s Pol named winner in Green Process Engineering
ECE's Bagchi featured in HPCwire
Computational scientists have long relied on high-precision arithmetic to accurately solve a wide range of problems, from modeling nuclear reactors to predicting supernova physics to measuring the forces within an atomic nucleus. However, changes to hardware, spurred by the demand for more computing capability and growth in machine learning, have us rethinking the balance between the number of digits needed to perform a given calculation and computational efficiency.
Researchers from Purdue University, Prof. Saurabh Bagchi, and the U.S. Department of Energy’s Lawrence Livermore National Laboratory, Dr. Ignacio Laguna, have taken an essential step toward enabling mixed precision calculations on GPUs through novel automatic tuning methods that can be applied to real-world CUDA programs. For portions of a calculation that do not require full 64-bit double-precision arithmetic, lower precision alternatives may provide enough accuracy. The tradeoff could enable us to solve complex problems faster and at lower energy budgets, thus enabling scientific discoveries that would otherwise remain hidden away from us.
CE/EEE's Whelton interviewed by NPR on "California's Worst Wildfire"
Tammy Waller thought she was one of the lucky ones after her home in Magalia survived California's most destructive wildfire ever, but her community remains a ghostly skeleton of its former self.
Hazmat crews are still clearing properties, and giant dump trucks haul away toxic debris. Signs on the water fountains in the town hall say, "Don't drink."
Waller remembers the day she came back home after the Camp Fire.