16 THIS ARTICLE HAS BEEN PEER-REVIEWED. COMPUTING IN SCIENCE & ENGINEERING D IGITAL M ANUFACTURING Although there’s a widespread belief that the effective application of high-performance computing will dramatically increase industrial innovation, progress in this area has been slow and limited because of a combination of technical and economic impediments. Here, such impediments are outlined, along with efforts to address them. Bringing HPC to Engineering Innovation I t’s well recognized that US industry must focus on innovation. A review of the current Council on Competitiveness publication list (see www.compete.org/publications) clearly indicates that the application of simulation using high-performance computing (HPC) is critical to industrial innovation. Case studies demonstrate the importance of HPC across all industrial sec- tors. It’s also well recognized that taking advan- tage of advances in nanotechnology is at the core of many of the innovations possible in product development and healthcare. However, the ability to translate those advances into new products and industries requires the transformation of exist- ing modeling, analysis, and design methodologies into ones that explicitly account for the interac- tions of phenomena across the atomic, molecular, microscopic, and macroscopic scales. The compu- tational needs of such simulations are dramatically higher than those of single-scale analyses, and the software infrastructure needed is also much more complex. Some companies make extensive use of mas- sively parallel simulation. What isn’t as obvious is that in areas where computer-aided engineer- ing (CAE) has been used for many years, the level of computation being used for the majority of simulations is far from what’s needed, and it’s far below what current HPC systems can provide. Closer examination of the engineering problems being addressed indicates that, in most cases, the resolution of the models and discretizations ap- plied isn’t high enough for engineers to ensure the simulation results’ reliability, and the simulations being applied are at a single scale, ignoring the in- novations made possible by performing multiscale simulations. For example, in an April 2009 case study, 1 a 168-processor system was applied to sup- port a major manufacturer’s HPC needs. Although this case study does demonstrate impressive gains, 168 cores is less than 1/1,000th of the 294,912 pro- cessors used for a single simulation with tools 2 that we’re applying to industrial problems. Addition- ally, these massively parallel machines can support concurrent execution of multiple simulations. This capability for high throughput when applied to de- sign optimization and parameter studies can result in a dramatic reduction in time to completion. You could argue that machines with hundreds of thousands of processing cores are well beyond what industry would obtain—however, industry will easily be able to justify next-generation massively parallel machines with more than 10,000 cores due to the continued dramatic decreases in machine costs and power requirements over that of cur- rent systems. In addition, through opportunities such as the US Department of Energy’s (DOE’s) Innovative and Novel Computational Impact on Theory and Experiment (INCITE) and the National Science Foundation’s (NSF’s) Extreme Mark S. Shephard, Cameron Smith, and John E. Kolb Rensselaer Polytechnic Institute 1521-9615/13/$31.00 © 2013 IEEE COPUBLISHED BY THE IEEE CS AND THE AIP CISE-15-1-Smith.indd 16 12/12/12 11:54 AM