8,926 research outputs found
A Parallel Adaptive P3M code with Hierarchical Particle Reordering
We discuss the design and implementation of HYDRA_OMP a parallel
implementation of the Smoothed Particle Hydrodynamics-Adaptive P3M (SPH-AP3M)
code HYDRA. The code is designed primarily for conducting cosmological
hydrodynamic simulations and is written in Fortran77+OpenMP. A number of
optimizations for RISC processors and SMP-NUMA architectures have been
implemented, the most important optimization being hierarchical reordering of
particles within chaining cells, which greatly improves data locality thereby
removing the cache misses typically associated with linked lists. Parallel
scaling is good, with a minimum parallel scaling of 73% achieved on 32 nodes
for a variety of modern SMP architectures. We give performance data in terms of
the number of particle updates per second, which is a more useful performance
metric than raw MFlops. A basic version of the code will be made available to
the community in the near future.Comment: 34 pages, 12 figures, accepted for publication in Computer Physics
Communication
Distributed Training Large-Scale Deep Architectures
Scale of data and scale of computation infrastructures together enable the
current deep learning renaissance. However, training large-scale deep
architectures demands both algorithmic improvement and careful system
configuration. In this paper, we focus on employing the system approach to
speed up large-scale training. Via lessons learned from our routine
benchmarking effort, we first identify bottlenecks and overheads that hinter
data parallelism. We then devise guidelines that help practitioners to
configure an effective system and fine-tune parameters to achieve desired
speedup. Specifically, we develop a procedure for setting minibatch size and
choosing computation algorithms. We also derive lemmas for determining the
quantity of key components such as the number of GPUs and parameter servers.
Experiments and examples show that these guidelines help effectively speed up
large-scale deep learning training
Data Provenance and Management in Radio Astronomy: A Stream Computing Approach
New approaches for data provenance and data management (DPDM) are required
for mega science projects like the Square Kilometer Array, characterized by
extremely large data volume and intense data rates, therefore demanding
innovative and highly efficient computational paradigms. In this context, we
explore a stream-computing approach with the emphasis on the use of
accelerators. In particular, we make use of a new generation of high
performance stream-based parallelization middleware known as InfoSphere
Streams. Its viability for managing and ensuring interoperability and integrity
of signal processing data pipelines is demonstrated in radio astronomy. IBM
InfoSphere Streams embraces the stream-computing paradigm. It is a shift from
conventional data mining techniques (involving analysis of existing data from
databases) towards real-time analytic processing. We discuss using InfoSphere
Streams for effective DPDM in radio astronomy and propose a way in which
InfoSphere Streams can be utilized for large antennae arrays. We present a
case-study: the InfoSphere Streams implementation of an autocorrelating
spectrometer, and using this example we discuss the advantages of the
stream-computing approach and the utilization of hardware accelerators
GreeM : Massively Parallel TreePM Code for Large Cosmological N-body Simulations
In this paper, we describe the implementation and performance of GreeM, a
massively parallel TreePM code for large-scale cosmological N-body simulations.
GreeM uses a recursive multi-section algorithm for domain decomposition. The
size of the domains are adjusted so that the total calculation time of the
force becomes the same for all processes. The loss of performance due to
non-optimal load balancing is around 4%, even for more than 10^3 CPU cores.
GreeM runs efficiently on PC clusters and massively-parallel computers such as
a Cray XT4. The measured calculation speed on Cray XT4 is 5 \times 10^4
particles per second per CPU core, for the case of an opening angle of
\theta=0.5, if the number of particles per CPU core is larger than 10^6.Comment: 13 pages, 11 figures, accepted by PAS
Solving the Klein-Gordon equation using Fourier spectral methods: A benchmark test for computer performance
The cubic Klein-Gordon equation is a simple but non-trivial partial
differential equation whose numerical solution has the main building blocks
required for the solution of many other partial differential equations. In this
study, the library 2DECOMP&FFT is used in a Fourier spectral scheme to solve
the Klein-Gordon equation and strong scaling of the code is examined on
thirteen different machines for a problem size of 512^3. The results are useful
in assessing likely performance of other parallel fast Fourier transform based
programs for solving partial differential equations. The problem is chosen to
be large enough to solve on a workstation, yet also of interest to solve
quickly on a supercomputer, in particular for parametric studies. Unlike other
high performance computing benchmarks, for this problem size, the time to
solution will not be improved by simply building a bigger supercomputer.Comment: 10 page
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