709 research outputs found
From Solar Cells to Exascale Computing
Beginning with a brief overview of a boundary integral problem that led from an intractable computational problem to model an efficient solar cell but through a novel mathematical formulation became tractable, I will lay the foundation of the importance of mathematics in computation. This problem led to my career in High Performance Computing (HPC). Today, as we move from the Petascale era to Exascale era, HPC is a tool frequently used to understand complex problems in numerous areas such as aerospace, biology, climate modeling and energy. Scientists and engineers working on problems in these and other areas demand ever increasing compute power for their problems. In order to satisfy the demand for increase performance to achieve breakthrough science and engineering, we turn to parallelism through large systems with multi-core chips. For these systems to be useful massive parallelism at the chip level is not sufficient. I will describe some of the challenges that will need to be considered in designing Petascale and eventually Exascale systems. Through the combination of HPC hardware coupled with novel mathematical and algorithmic approaches, such as those described in the initial problem of this talk, some efforts toward breakthroughs in science and engineering are described. While progress is being made, there remain many challenges for the computational science and engineering community to apply ultra-scale, multi-core systems to “Big” science problems with impact on society. In conclusion, some discussion not only on the most obvious way to use ultra-scale, multicore HPC systems will be given but also some thoughts on how one might use such systems to tackle previously intractable problems
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
Astronomy and Computing: a New Journal for the Astronomical Computing Community
We introduce \emph{Astronomy and Computing}, a new journal for the growing
population of people working in the domain where astronomy overlaps with
computer science and information technology. The journal aims to provide a new
communication channel within that community, which is not well served by
current journals, and to help secure recognition of its true importance within
modern astronomy. In this inaugural editorial, we describe the rationale for
creating the journal, outline its scope and ambitions, and seek input from the
community in defining in detail how the journal should work towards its
high-level goals.Comment: 5 pages, no figures; editorial for first edition of journa
Progress Towards Petascale Applications in Biology: Status in 2006
Petascale computing is currently a common topic of discussion in the high performance computing community. Biological applications, particularly protein folding, are often given as examples of the need for petascale computing. There are at present biological applications that scale to execution rates of approximately 55 teraflops on a special-purpose supercomputer and 2.2 teraflops on a general-purpose supercomputer. In comparison, Qbox, a molecular dynamics code used to model metals, has an achieved performance of 207.3 teraflops. It may be useful to increase the extent to which operation rates and total calculations are reported in discussion of biological applications, and use total operations (integer and floating point combined) rather than (or in addition to) floating point operations as the unit of measure. Increased reporting of such metrics will enable better tracking of progress as the research community strives for the insights that will be enabled by petascale computing.This research was supported in part by the Indiana Genomics Initiative and the Indiana Metabolomics and Cytomics Initiative. The Indiana Genomics Initiative of Indiana University and the Indiana Metabolomics and Cytomics Initiative of Indiana University are supported in part by Lilly Endowment, Inc. The authors also wish to thank IBM, Inc. for support via Shared University Research Grants and partnerships via IU’s relationship as an IBM Life Sciences Institute of Innovation. Indiana University also thanks the TeraGrid partners; IU’s participation in the TeraGrid is funded by National Science Foundation grant numbers 0338618, 0504075, and 0451237. The early development of this paper was supported by a Fulbright Senior Scholars award from the Council for International Exchange of Scholars (CIES) and the United States Department of State to Dr. Craig A. Stewart; Matthias Mueller and the Technische Universität Dresden were hosts. Many reviewers contributed to the improvement of the ideas expressed in this paper and are gratefully appreciated; Thom Dunning, Robert Germain, Chris Mueller, Jim Phillips, Richard Repasky, Ralph Roskies, and Allan Snavely are thanked particularly for their insights
Preparing HPC Applications for the Exascale Era: A Decoupling Strategy
Production-quality parallel applications are often a mixture of diverse
operations, such as computation- and communication-intensive, regular and
irregular, tightly coupled and loosely linked operations. In conventional
construction of parallel applications, each process performs all the
operations, which might result inefficient and seriously limit scalability,
especially at large scale. We propose a decoupling strategy to improve the
scalability of applications running on large-scale systems.
Our strategy separates application operations onto groups of processes and
enables a dataflow processing paradigm among the groups. This mechanism is
effective in reducing the impact of load imbalance and increases the parallel
efficiency by pipelining multiple operations. We provide a proof-of-concept
implementation using MPI, the de-facto programming system on current
supercomputers. We demonstrate the effectiveness of this strategy by decoupling
the reduce, particle communication, halo exchange and I/O operations in a set
of scientific and data-analytics applications. A performance evaluation on
8,192 processes of a Cray XC40 supercomputer shows that the proposed approach
can achieve up to 4x performance improvement.Comment: The 46th International Conference on Parallel Processing (ICPP-2017
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