18,341 research outputs found
4.45 Pflops Astrophysical N-Body Simulation on K computer -- The Gravitational Trillion-Body Problem
As an entry for the 2012 Gordon-Bell performance prize, we report performance
results of astrophysical N-body simulations of one trillion particles performed
on the full system of K computer. This is the first gravitational trillion-body
simulation in the world. We describe the scientific motivation, the numerical
algorithm, the parallelization strategy, and the performance analysis. Unlike
many previous Gordon-Bell prize winners that used the tree algorithm for
astrophysical N-body simulations, we used the hybrid TreePM method, for similar
level of accuracy in which the short-range force is calculated by the tree
algorithm, and the long-range force is solved by the particle-mesh algorithm.
We developed a highly-tuned gravity kernel for short-range forces, and a novel
communication algorithm for long-range forces. The average performance on 24576
and 82944 nodes of K computer are 1.53 and 4.45 Pflops, which correspond to 49%
and 42% of the peak speed.Comment: 10 pages, 6 figures, Proceedings of Supercomputing 2012
(http://sc12.supercomputing.org/), Gordon Bell Prize Winner. Additional
information is http://www.ccs.tsukuba.ac.jp/CCS/eng/gbp201
2HOT: An Improved Parallel Hashed Oct-Tree N-Body Algorithm for Cosmological Simulation
We report on improvements made over the past two decades to our adaptive
treecode N-body method (HOT). A mathematical and computational approach to the
cosmological N-body problem is described, with performance and scalability
measured up to 256k () processors. We present error analysis and
scientific application results from a series of more than ten 69 billion
() particle cosmological simulations, accounting for
floating point operations. These results include the first simulations using
the new constraints on the standard model of cosmology from the Planck
satellite. Our simulations set a new standard for accuracy and scientific
throughput, while meeting or exceeding the computational efficiency of the
latest generation of hybrid TreePM N-body methods.Comment: 12 pages, 8 figures, 77 references; To appear in Proceedings of SC
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From Bare Metal to Virtual: Lessons Learned when a Supercomputing Institute Deploys its First Cloud
As primary provider for research computing services at the University of
Minnesota, the Minnesota Supercomputing Institute (MSI) has long been
responsible for serving the needs of a user-base numbering in the thousands.
In recent years, MSI---like many other HPC centers---has observed a growing
need for self-service, on-demand, data-intensive research, as well as the
emergence of many new controlled-access datasets for research purposes. In
light of this, MSI constructed a new on-premise cloud service, named Stratus,
which is architected from the ground up to easily satisfy data-use agreements
and fill four gaps left by traditional HPC. The resulting OpenStack cloud,
constructed from HPC-specific compute nodes and backed by Ceph storage, is
designed to fully comply with controls set forth by the NIH Genomic Data
Sharing Policy.
Herein, we present twelve lessons learned during the ambitious sprint to take
Stratus from inception and into production in less than 18 months. Important,
and often overlooked, components of this timeline included the development of
new leadership roles, staff and user training, and user support documentation.
Along the way, the lessons learned extended well beyond the technical
challenges often associated with acquiring, configuring, and maintaining
large-scale systems.Comment: 8 pages, 5 figures, PEARC '18: Practice and Experience in Advanced
Research Computing, July 22--26, 2018, Pittsburgh, PA, US
Platform independent profiling of a QCD code
The supercomputing platforms available for high performance computing based
research evolve at a great rate. However, this rapid development of novel
technologies requires constant adaptations and optimizations of the existing
codes for each new machine architecture. In such context, minimizing time of
efficiently porting the code on a new platform is of crucial importance. A
possible solution for this common challenge is to use simulations of the
application that can assist in detecting performance bottlenecks. Due to
prohibitive costs of classical cycle-accurate simulators, coarse-grain
simulations are more suitable for large parallel and distributed systems. We
present a procedure of implementing the profiling for openQCD code [1] through
simulation, which will enable the global reduction of the cost of profiling and
optimizing this code commonly used in the lattice QCD community. Our approach
is based on well-known SimGrid simulator [2], which allows for fast and
accurate performance predictions of HPC codes. Additionally, accurate
estimations of the program behavior on some future machines, not yet accessible
to us, are anticipated
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