We have developed a highly scalable and efficient GPU-based finite-difference code (AWP) for earthquake simulation that implements high throughput, memory locality, communication reduction and communication/computation overlap and achieves linear scalability on Cray XK7 Titan at ORNL and NCSA's Blue Waters system. We simulate realistic 0-10 Hz earthquake ground motions relevant to building engineering design using high-performance AWP. Moreover, we show that AWP provides a speedup by a factor of 110 in key strain tensor calculations critical to probabilistic seismic hazard analysis (PSHA). These performance improvements to critical scientific application software, coupled with improved co-scheduling capabilities of our workflow-managed systems, make a statewide hazard model a goal reachable with existing supercomputers. The performance improvements of GPU-based AWP are expected to save millions of core-hours over the next few years as physics-based seismic hazard analysis is developed using heterogeneous petascale supercomputers.

Petascale simulations are needed to understand the rupture and wave dynamics of the largest earthquakes at shaking frequencies required to engineer safe structures (> 1 Hz). Toward this goal, we have developed a highly scalable, parallel application (AWP-ODC) that has achieved “M8”: a full dynamical simulation of a magnitude-8 earthquake on the southern San Andreas fault up to 2 Hz. M8 was calculated using a uniform mesh of 436 billion 40-m3cubes to represent the three-dimensional crustal structure of Southern California, in a 800 km by 400 km area, home to over 20 million people. This production run producing 360 sec of wave propagation sustained 220 Tflop/s for 24 hours on NCCS Jaguar using 223,074 cores. As the largest-ever earthquake simulation, M8 opens new territory for earthquake science and engineering - the physics-based modeling of the largest seismic hazards with the goal of reducing their potential for loss of life and property.

Efficient data management is a key component in achieving good performance for scientific workflows in distributed environments. Workflow applications typically communicate data between tasks using files. When tasks are distributed, these files are either transferred from one computational node to another, or accessed through a shared storage system. In grids and clusters, workflow data is often stored on network and parallel file systems. In this paper we investigate some of the ways in which data can be managed for workflows in the cloud. We ran experiments using three typical workflow applications on Amazon's EC2. We discuss the various storage and file systems we used, describe the issues and problems we encountered deploying them on EC2, and analyze the resulting performance and cost of the workflows.

Abstract The Collaboratory for the Study of Earthquake Predictability (CSEP) aims to advance earthquake research by rigorous testing of earthquake forecast hypotheses. As in other disciplines, such hypothesis testing requires carefully designed experiments that meet certain requirements: they should be reproducible, fully transparent, and conducted within a controlled environment. CSEP has begun building infrastructure for conducting such rigorous earthquake forecasting experiments. Because past earthquake prediction experiments often have been controversial, CSEP testing centers—the secure, controlled computational environments within which experiments are conducted—have been designed to address particular issues related to transparency and exact reproducibility. Moreover, CSEP fosters collaboration among scientists developing earthquake forecast models, and the testing center concept allows multiple concurrent predictability experiments. In this paper, we share our perspective on computational earthquake science by presenting the design principles, organizational structure, and implementation details of CSEP testing centers. We describe ongoing forecast experiments in different testing regions and some of the implementation challenges encountered. We also describe the collaboration tools used for multinational software development and regional presentation websites. The need for common data exchange formats is discussed, as are potential avenues of future research within CSEP testing centers. Copyright © 2009 John Wiley & Sons, Ltd.

Researchers at the Southern California Earthquake Center (SCEC) use large-scale grid-based scientific workflows to perform seismic hazard research as a part of SCEC's program of earthquake system science research. The scientific goal of the SCEC CyberShake project is to calculate probabilistic seismic hazard curves for sites in Southern California. For each site of interest, the CyberShake platform includes two large-scale MPI calculations and approximately 840,000 embarrassingly parallel post-processing jobs. In this paper, we describe the computational requirements of CyberShake and detail how we meet these requirements using grid-based, high-throughput, scientific workflow tools. We describe the specific challenges we encountered and we discuss workflow throughput optimizations we developed that reduced our time to solution by a factor of three and we present runtime statistics and propose further optimizations.

This paper discusses the process of building an environment where large-scale, complex, scientific analysis can be scheduled onto a heterogeneous collection of computational and storage resources. The example application is the Southern California Earthquake Center (SCEC) CyberShake project, an analysis designed to compute probabilistic seismic hazard curves for sites in the Los Angeles area. We explain which software tools were used to build to the system, describe their functionality and interactions. We show the results of running the CyberShake analysis that included over 250,000 jobs using resources available through SCEC and the TeraGrid.