Vladimir Belsky, Ph.D.

Director, Solver Development, Simulia Implicit Analysis

Accelerating Commercial Linear Dynamic and Nonlinear Implicit FEA Software through High Performance Computing

In the last decade, significant R&D resources have been invested to deliver commercially available technologies that meet current and future mechanical engineering industry requirements both in terms of mechanics and performance. While significant focus has been given to developing robust nonlinear finite element analysis technology, there has also been continued investment in developing advancements for linear dynamic analyses. The research and development efforts have focused on combining advanced linear and nonlinear technology to provide accurate, yet fast modelling of noise and vibration engineering problems. This effort has enabled high-fidelity models to run in a reasonable time which is vital for virtual prototyping within shortened product design cycles.

While it is very true that model sizes (degrees of freedom) have grown significantly during this period, the complexity of the models has also increased, which has led to a larger number of total iterations within nonlinear implicit analyses and to a large number of eigenmodes within linear dynamic simulations. An innovative approach has been developed to leverage High Performance Computing (HPC) resources to yield reasonable turn-around times for such analyses by taking advantage of massive parallelism without sacrificing any mechanical formulation quality.

The accessibility and affordability of high-performance computing (HPC) hardware in the past few years has changed the landscape of commercial finite element analysis software usage and applications. This change has come in response to expressed desire from engineers and designers to run their existing simulations faster, or in many cases to run more realistic jobs. Due to their computational cost and lack of high-performance commercial software, such "high-end" simulations were until recently thought to be only available to academic institutions or government research laboratories which typically developed their own HPC applications. Today, with the advent of affordable multi-core SMP workstations and compute clusters with multi-core nodes and high-speed interconnects, equipped with GPGPU accelerators, HPC is now used or sought to be used by many engineers for routine FEA. This presents a challenge for commercial FEA software vendors which have to adapt their decades old legacy code to take advantage of state-of-the-art HPC platforms.

Given this background, this paper focuses on how recent developments in HPC affect performance of linear dynamic and implicit nonlinear analyses. Two main HPC developments are studied. First, we look into performance and scalability of the commercially available in Abaqus AMS eigenvalue solver and of the entire frequency response simulation running on multi-core SMP workstations. Advances in the AMS eigenvalue solution procedure and linear dynamic capabilities make the realistic simulation solution suitable for a wide range of vehicle-level noise and vibration simulations.

Next, we will present performance and scalability data for a direct sparse solver in addition to the entire application running on up to 256 cores. Our case-studies showcase the impact of HPC on our customers' industry applications. We also discuss the progress made in relatively new but very active area of high performance commercial FE software development, which is based on taken advantage of high performance GPGPU accelerators. Efficient adoption of GPGPU in such products is very challenging task which requires significant re-architecture of the existent code. We describe the experience in integrating GPGPU acceleration into complex commercial engineering software. In particular we discuss the trade-off we had to make and the benefits we obtained from this technology.

Biography

Vladimir Belsky has a Ph.D. degree in Structural Engineering from the University of Civil Engineering in Moscow, Russia. He started his career at SIMULIA in 1996 as a Development Engineer. Currently he is a Director of Solver Development. He and his group are responsible for development of highly efficient scalable linear equation solvers, eigensolvers and other capabilities heavily influenced by the solver technology.

Workshop Program
updated: 2011-10-19