156,149 research outputs found
Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters
The classical method of determining the atomic structure of complex molecules
by analyzing diffraction patterns is currently undergoing drastic developments.
Modern techniques for producing extremely bright and coherent X-ray lasers
allow a beam of streaming particles to be intercepted and hit by an ultrashort
high energy X-ray beam. Through machine learning methods the data thus
collected can be transformed into a three-dimensional volumetric intensity map
of the particle itself. The computational complexity associated with this
problem is very high such that clusters of data parallel accelerators are
required.
We have implemented a distributed and highly efficient algorithm for
inversion of large collections of diffraction patterns targeting clusters of
hundreds of GPUs. With the expected enormous amount of diffraction data to be
produced in the foreseeable future, this is the required scale to approach real
time processing of data at the beam site. Using both real and synthetic data we
look at the scaling properties of the application and discuss the overall
computational viability of this exciting and novel imaging technique
BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations
Objective: The advent of High-Performance Computing (HPC) in recent years has
led to its increasing use in brain study through computational models. The
scale and complexity of such models are constantly increasing, leading to
challenging computational requirements. Even though modern HPC platforms can
often deal with such challenges, the vast diversity of the modeling field does
not permit for a single acceleration (or homogeneous) platform to effectively
address the complete array of modeling requirements. Approach: In this paper we
propose and build BrainFrame, a heterogeneous acceleration platform,
incorporating three distinct acceleration technologies, a Dataflow Engine, a
Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform.
As a challenging proof of concept, we analyze the performance of BrainFrame on
different instances of a state-of-the-art neuron model, modeling the Inferior-
Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley
representation. The model instances take into account not only the neuronal-
network dimensions but also different network-connectivity circumstances that
can drastically change application workload characteristics. Main results: The
synthetic approach of three HPC technologies demonstrated that BrainFrame is
better able to cope with the modeling diversity encountered. Our performance
analysis shows clearly that the model directly affect performance and all three
technologies are required to cope with all the model use cases.Comment: 16 pages, 18 figures, 5 table
About multi-resolution techniques for large eddy simulation of reactive multi-phase flows
A numerical technique for mesh refinement in the HeaRT (Heat Release and Transfer) numerical code is presented. In the CFD
framework, Large Eddy Simulation (LES) approach is gaining in importance as a tool for simulating turbulent combustion pro-
cesses, also if this approach has an high computational cost due to the complexity of the turbulent modeling and the high number of
grid points necessary to obtain a good numerical solution. In particular, when a numerical simulation of a big domain is performed
with a structured grid, the number of grid points can increase so much that the simulation becomes impossible: this problem can
be overcomed with a mesh refinement technique. Mesh refinement technique developed for HeaRT numerical code (a staggered
finite difference code) is based on an high order reconstruction of the variables at the grid interfaces by means of a least square
quasi-eno interpolation: numerical code is written in modern Fortran (2003 standard of newer) and is parallelized using domain
decomposition and message passing interface (MPI) standard
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