Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Exploring Application Performance on Emerging Hybrid-Memory Supercomputers

Next-generation supercomputers will feature more hierarchical and heterogeneous memory systems with different memory technologies working side-by-side. A critical question is whether at large scale existing HPC applications and emerging data-analytics workloads will have performance improvement or degradation on these systems. We propose a systematic and fair methodology to identify the trend of application performance on emerging hybrid-memory systems. We model the memory system of next-generation supercomputers as a combination of "fast" and "slow" memories. We then analyze performance and dynamic execution characteristics of a variety of workloads, from traditional scientific applications to emerging data analytics to compare traditional and hybrid-memory systems. Our results show that data analytics applications can clearly benefit from the new system design, especially at large scale. Moreover, hybrid-memory systems do not penalize traditional scientific applications, which may also show performance improvement.Comment: 18th International Conference on High Performance Computing and Communications, IEEE, 201

A visual Analytics System for Optimizing Communications in Massively Parallel Applications

Current and future supercomputers have tens of thousands of compute nodes interconnected with high-dimensional networks and complex network topologies for improved performance. Application developers are required to write scalable parallel programs in order to achieve high throughput on these machines. Application performance is largely determined by efficient inter-process communication. A common way to analyze and optimize performance is through profiling parallel codes to identify communication bottlenecks. However, understanding gigabytes of profile data is not a trivial task. In this paper, we present a visual analytics system for identifying the scalability bottlenecks and improving the communication efficiency of massively parallel applications. Visualization methods used in this system are designed to comprehend large-scale and varied communication patterns on thousands of nodes in complex networks such as the 5D torus and the dragonfly. We also present efficient rerouting and remapping algorithms that can be coupled with our interactive visual analytics design for performance optimization. We demonstrate the utility of our system with several case studies using three benchmark applications on two leading supercomputers. The mapping suggestion from our system led to 38% improvement in hop-bytes for MiniAMR application on 4,096 MPI processes.This research has been sponsored in part by the U.S. National Science Foundation through grant IIS-1320229, and the U.S. Department of Energy through grants DE-SC0012610 and DE-SC0014917. This research has been funded in part and used resources of the Argonne Leadership Computing Facility at Argonne National Lab- oratory, which is supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC02-06CH11357. This work was supported in part by the DOE Office of Science, ASCR, under award numbers 57L38, 57L32, 57L11, 57K50, and 508050

An extreme-scale implicit solver for complex PDEs: highly heterogeneous flow in earth's mantle

Mantle convection is the fundamental physical process within earth's interior responsible for the thermal and geological evolution of the planet, including plate tectonics. The mantle is modeled as a viscous, incompressible, non-Newtonian fluid. The wide range of spatial scales, extreme variability and anisotropy in material properties, and severely nonlinear rheology have made global mantle convection modeling with realistic parameters prohibitive. Here we present a new implicit solver that exhibits optimal algorithmic performance and is capable of extreme scaling for hard PDE problems, such as mantle convection. To maximize accuracy and minimize runtime, the solver incorporates a number of advances, including aggressive multi-octree adaptivity, mixed continuous-discontinuous discretization, arbitrarily-high-order accuracy, hybrid spectral/geometric/algebraic multigrid, and novel Schur-complement preconditioning. These features present enormous challenges for extreme scalability. We demonstrate that---contrary to conventional wisdom---algorithmically optimal implicit solvers can be designed that scale out to 1.5 million cores for severely nonlinear, ill-conditioned, heterogeneous, and anisotropic PDEs

Exploration into Properties of Molybdenum Disulfide using Atomistic Simulation

Molybdenum disulfide (MoS2) has a lamellar crystal structure, which makes it ideal for use as a solid lubricant. Transmission electron microscope (TEM) images have shown that line defects exist within the lattice of mechanically deformed MoS2, but the physical mechanisms which lead to the formation of these defects are unknown. The two central objectives of this research are to use molecular dynamics simulations to study the effects of tensile deformation on both single layer and bulk MoS2 and explore the properties of line defects in an otherwise perfect lattice of MoS2. Under tensile loading, molecular dynamics simulations show a multi-stage stress versus strain diagram. Atomistic visualization shows a distinct change in the structure of the lattice during tensile stretching. This new structure is likely the result of a phase transformation. For the second objective, a series of computational approaches are used to create a single line defect in a perfect MoS2 lattice. Shearing both parallel and perpendicular to the basal plane of MoS2 and compression leading to buckling were unsuccessful in moving the line defect

What is Cyberinfrastructure?

Cyberinfrastructure is a word commonly used but lacking a single, precise definition. One recognizes intuitively the analogy with infrastructure, and the use of cyber to refer to thinking or computing – but what exactly is cyberinfrastructure as opposed to information technology infrastructure? Indiana University has developed one of the more widely cited definitions of cyberinfrastructure: "Cyberinfrastructure consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high performance networks to improve research productivity and enable breakthroughs not otherwise possible." A second definition, more inclusive of scholarship generally and educational activities, has also been published and is useful in describing cyberinfrastructure: "Cyberinfrastructure consists of systems, data and information management, advanced instruments, visualization environments, and people, all linked together by software and advanced networks to improve scholarly productivity and enable knowledge breakthroughs and discoveries not otherwise possible." In this paper, we describe the origin of the term cyberinfrastructure based on the history of the root word infrastructure, discuss several terms related to cyberinfrastructure, and provide several examples of cyberinfrastructure