Abstract: The last half-decade ushered in a new era of vision research. Computer vision now works on real images, in natural environments, solving hard problems. But the technology is far from ubiquitous and many researchers are most concerned with getting the best performance on a handful of datasets. This hyper-focus on accuracy has largely turned vision into a numbers game and research tends toward complex, finely-tuned systems that are brittle and impractical in the real world.
Abstract: The research of computer vision was motivated by a dream of making an intelligent machine that is able to see like our human beings: to automatically analyze and understand massive visual inputs. With the explosive growing of computing power, this dream evolves to many exciting emerging applications, such as intelligent robots, autonomous vehicles, intelligent video surveillances, computer-aided doctors, etc. A core component in these applications is visual recognition (including object classification, detection and localization).
The USA Exascale Computing Project (ECP) is focused on accelerating the delivery of a capable exascale computing ecosystem that delivers 50 times more computational science and data analytic application power than possible with DOE HPC systems such as Titan (ORNL) and Sequoia (LLNL). As next generation applications and experiments grow in concurrency and in complexity, the data produced often grows to extreme levels, limiting scientific knowledge discovery.
This talk will highlight two topics.
Topic #1: in-situ visualization of a multi-physics simulation on LLNL's Sierra Supercomputer: