We present unprecedented, high throughput simulations of cloud cavitation collapse on 1.6 million cores of Sequoia reaching 55% of its nominal peak performance, corresponding to 11 PFLOP/s. The destructive power of cavitation reduces the lifetime of energy critical systems such as internal combustion engines and hydraulic turbines, yet it has been harnessed for water purification and kidney lithotripsy. The present two-phase flow simulations enable the quantitative prediction of cavitation using 13 trillion grid points to resolve the collapse of 15'000 bubbles. We advance by one order of magnitude the current state-of-the-art in terms of time to solution, and by two orders the geometrical complexity of the flow. The software successfully addresses the challenges that hinder the effective solution of complex flows on contemporary supercomputers, such as limited memory bandwidth, I/O bandwidth and storage capacity. The present work redefines the frontier of high performance computing for fluid dynamics simulations.

We present an open source, object-oriented software for high throughput Direct Numerical Simulations of compressible, two-phase flows. The Navier-Stokes equations are discretized on uniform grids using high order finite volume methods. The software exploits recent CPU micro-architectures by explicit vectorization and adopts NUMA-aware techniques as well as data and computation reordering. We report a compressible flow solver with unprecedented fractions of peak performance: 45% of the peak for a single node (nominal performance of 840 GFLOP/s) and 30% for a cluster of 47'000 cores (nominal performance of 0.8 PFLOP/s). We suggest that the present work may serve as a performance upper bound, regarding achievable GFLOP/s, for two-phase flow solvers using adaptive mesh refinement. The software enables 3D simulations of shock-bubble interaction including, for the first time, effects of diffusion and surface tension, by efficiently employing two hundred billion computational elements.

Abstract We build wavelet‐based adaptive numerical methods for the simulation of advection‐dominated flows that develop multiple spatial scales, with an emphasis on fluid mechanics problems. Wavelet‐based adaptivity is inherently sequential and in this work we demonstrate that these numerical methods can be implemented in software that is capable of harnessing the capabilities of multi‐core architectures while maintaining their computational efficiency. Recent designs in frameworks for multi‐core software development allow us to rethink parallelism as task‐based, where parallel tasks are specified and automatically mapped onto physical threads. This way of exposing parallelism enables the parallelization of algorithms that were considered inherently sequential, such as wavelet‐based adaptive simulations. In this paper we present a framework that combines wavelet‐based adaptivity with the task‐based parallelism. We demonstrate the promising performance obtained by simulating various physical systems on different multi‐core architectures using up to 16 cores. Copyright © 2010 John Wiley & Sons, Ltd.