Large-Scale Data Analytics and Its Relationship to Simulation
Abstract not provided.
Publications
Performance Efficiency and Effectivness of Supercomputers
Our first purpose here is to offer to a general technical and policy audience a perspective on whether the supercomputing community should focus on improving the efficiency of supercomputing systems and their use rather than on building larger and ostensibly more capable systems that are used at low efficiency. After first summarizing our content and defining some necessary terms, we give a concise answer to this question. We then set this in context by characterizing performance of current supercomputing systems on a variety of benchmark problems and actual problems drawn from workloads in the national security, industrial, and scientific context. Along the way we answer some related questions, identify some important technological trends, and offer a perspective on the significance of these trends. Our second purpose is to give a reasonably broad and transparent overview of the related issue space and thereby to better equip the reader to evaluate commentary and controversy concerning supercomputing performance. For example, questions repeatedly arise concerning the Linpack benchmark and its predictive power, so we consider this in moderate depth as an example. We also characterize benchmark and application performance for scientific and engineering use of supercomputers and offer some guidance on how to think about these. Examples here are drawn from traditional scientific computing. Other problem domains, for example, data analytics, have different performance characteristics that are better captured by different benchmark problems or applications, but the story in those domains is similar in character and leads to similar conclusions with regard to the motivating question. For more on this topic, see Large-Scale Data Analytics and Its Relationship to Simulation. 1 Director, Computing Research Center, Sandia National Laboratories 2 Distinguished Member of the Technical Staff, Sandia National Laboratories 3 Distinguished Member of the Technical Staff, Sandia National Laboratories 4 Distinguished Member of the Technical Staff , Sandia National Laboratories
Advances in Domain Mapping of Massively Parallel Scientific Computations
One of the most important concerns in parallel computing is the proper distribution of workload across processors. For most scientific applications on massively parallel machines, the best approach to this distribution is to employ data parallelism; that is, to break the datastructures supporting a computation into pieces and then to assign those pieces to different processors. Collectively, these partitioning and assignment tasks comprise the domain mapping problem.
An Empirical Study of Static Load Balancing Algorithms
Abstract not provided.
Graphs and HPC in the '90s: A Distant Mirror or Merely Distant?
Abstract not provided.
Investigating the balance between capacity and capability workloads across large scale computing platforms
Abstract not provided.
Architectural specification for massively parallel computers: An experience and measurement-based approach
Concurrency and Computation: Practice and Experience
In this paper, we describe the hardware and software architecture of the Red Storm system developed at Sandia National Laboratories. We discuss the evolution of this architecture and provide reasons for the different choices that have been made. We contrast our approach of leveraging high-volume, mass-market commodity processors to that taken for the Earth Simulator. We present a comparison of benchmarks and application performance that support our approach. We also project the performance of Red Storm and the Earth Simulator. This projection indicates that the Red Storm architecture is a much more cost-effective approach to massively parallel computing. Published in 2005 by John Wiley & Sons, Ltd.
Validating DOE's Office of Science "capability" computing needs
A study was undertaken to validate the 'capability' computing needs of DOE's Office of Science. More than seventy members of the community provided information about algorithmic scaling laws, so that the impact of having access to Petascale capability computers could be assessed. We have concluded that the Office of Science community has described credible needs for Petascale capability computing.
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