Glimpses of Space-Time Beyond the Singularities Using Supercomputers

A fundamental problem of Einstein's theory of classical general relativity is the existence of singularities such as the big bang. All known laws of physics end at these boundaries of classical space-time. Thanks to recent developments in quantum gravity, supercomputers are now playing an important role in understanding the resolution of big bang and black hole singularities. Using supercomputers, explorations of the very genesis of space and time from quantum geometry are revealing a novel picture of what lies beyond classical singularities and the new physics of the birth of our universe.Comment: Invited semi-technical overview article appeared in IEEE publication Computing in Science and Engineering, special issue on "Supercomputing-Enabled Advances in Science and Engineering" edited by S. Gottlieb and G. Khanna. 8 pages, 3 figures. Uses IEEE style fil

NASA's supercomputing experience

A brief overview of NASA's recent experience in supercomputing is presented from two perspectives: early systems development and advanced supercomputing applications. NASA's role in supercomputing systems development is illustrated by discussion of activities carried out by the Numerical Aerodynamical Simulation Program. Current capabilities in advanced technology applications are illustrated with examples in turbulence physics, aerodynamics, aerothermodynamics, chemistry, and structural mechanics. Capabilities in science applications are illustrated by examples in astrophysics and atmospheric modeling. Future directions and NASA's new High Performance Computing Program are briefly discussed

Analyzing and Modeling the Performance of the HemeLB Lattice-Boltzmann Simulation Environment

We investigate the performance of the HemeLB lattice-Boltzmann simulator for cerebrovascular blood flow, aimed at providing timely and clinically relevant assistance to neurosurgeons. HemeLB is optimised for sparse geometries, supports interactive use, and scales well to 32,768 cores for problems with ~81 million lattice sites. We obtain a maximum performance of 29.5 billion site updates per second, with only an 11% slowdown for highly sparse problems (5% fluid fraction). We present steering and visualisation performance measurements and provide a model which allows users to predict the performance, thereby determining how to run simulations with maximum accuracy within time constraints.Comment: Accepted by the Journal of Computational Science. 33 pages, 16 figures, 7 table

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

The Ultralight project: the network as an integrated and managed resource for data-intensive science

Looks at the UltraLight project which treats the network interconnecting globally distributed data sets as a dynamic, configurable, and closely monitored resource to construct a next-generation system that can meet the high-energy physics community's data-processing, distribution, access, and analysis needs

Lessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers

For decades, the use of HPC systems was limited to those in the physical sciences who had mastered their domain in conjunction with a deep understanding of HPC architectures and algorithms. During these same decades, consumer computing device advances produced tablets and smartphones that allow millions of children to interactively develop and share code projects across the globe. As the HPC community faces the challenges associated with guiding researchers from disciplines using high productivity interactive tools to effective use of HPC systems, it seems appropriate to revisit the assumptions surrounding the necessary skills required for access to large computational systems. For over a decade, MIT Lincoln Laboratory has been supporting interactive, on-demand high performance computing by seamlessly integrating familiar high productivity tools to provide users with an increased number of design turns, rapid prototyping capability, and faster time to insight. In this paper, we discuss the lessons learned while supporting interactive, on-demand high performance computing from the perspectives of the users and the team supporting the users and the system. Building on these lessons, we present an overview of current needs and the technical solutions we are building to lower the barrier to entry for new users from the humanities, social, and biological sciences.Comment: 15 pages, 3 figures, First Workshop on Interactive High Performance Computing (WIHPC) 2018 held in conjunction with ISC High Performance 2018 in Frankfurt, German

Enhancing speed and scalability of the ParFlow simulation code

Regional hydrology studies are often supported by high resolution simulations of subsurface flow that require expensive and extensive computations. Efficient usage of the latest high performance parallel computing systems becomes a necessity. The simulation software ParFlow has been demonstrated to meet this requirement and shown to have excellent solver scalability for up to 16,384 processes. In the present work we show that the code requires further enhancements in order to fully take advantage of current petascale machines. We identify ParFlow's way of parallelization of the computational mesh as a central bottleneck. We propose to reorganize this subsystem using fast mesh partition algorithms provided by the parallel adaptive mesh refinement library p4est. We realize this in a minimally invasive manner by modifying selected parts of the code to reinterpret the existing mesh data structures. We evaluate the scaling performance of the modified version of ParFlow, demonstrating good weak and strong scaling up to 458k cores of the Juqueen supercomputer, and test an example application at large scale.Comment: The final publication is available at link.springer.co