high performance computing on graphics processing units: hgpu.org

We present a scalable approach that computes in operationally-compatible time the energy dispatch under uncertainty for complex energy systems of realistic size. Complex energy systems, such as the US power grid, are affected by increased uncertainty of its target power sources, due for example to increasing penetration of wind power coupled with the physical impossibility of very precise wind forecast. The leading optimization under uncertainty paradigm for such problems, stochastic programming, requires thousands of simultaneous scenarios, giving problems with billions of variables that need to be solved within an operationally defined time interval. To address this challenge, we propose several algorithmic and implementation advances inside our hybrid parallel optimization solver PIPS-IPM. The new developments include a novel incomplete augmented multicore sparse factorization implemented within PARDISO linear solver and new multicore- and GPUbased dense matrix implementations. We also adapt and improve the interprocess communication strategy. Numerical experiments on "Titan" (Cray XK7) and "Piz Daint" (Cray XC30) show that 24-hour horizon problems with thousands of scenarios can be efficiently solved in parallel in times compatible with the operational practices. To our knowledge, "real-time" compatible performance on a broad range of architectures for this class of problems has not been possible prior to present work.