SCHEDULE: NOV 10-16, 2012
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Massively Parallel X-Ray Scattering Simulations
SESSION: Massively Parallel Simulations
EVENT TYPE: Papers
TIME: 11:00AM - 11:30AM
SESSION CHAIR: Kamesh Madduri
AUTHOR(S):Abhinav Sarje, Xiaoye S. Li, Slim Chourou, Elaine R. Chan, Alexander Hexemer
ROOM:355-EF
ABSTRACT:
Although present X-ray scattering techniques can provide tremendous information
on the nano-structural properties of materials that are valuable in the design
and fabrication of energy-relevant nano-devices, a primary challenge remains in
the analyses of such data. In this paper we describe a high-performance,
flexible, and scalable Grazing Incidence Small Angle X-ray Scattering simulation
algorithm and codes that we have developed on multi-core/CPU and many-core/GPU clusters. We discuss in detail our implementation, optimization and performance on these platforms. Our results show speedups of ~125x on a Fermi-GPU and ~20x on a Cray-XE6 24-core node, compared to a sequential CPU code, with near linear scaling on multi-node clusters. To our knowledge, this is the
first GISAXS simulation code that is flexible to compute scattered light
intensities in all spatial directions allowing full reconstruction of GISAXS
patterns for any complex structures and with high-resolutions while reducing
simulation times from months to minutes.
Chair/Author Details:
Kamesh Madduri (Chair) - Pennsylvania State University
Abhinav Sarje - Lawrence Berkeley National Laboratory
Xiaoye S. Li - Lawrence Berkeley National Laboratory
Slim Chourou - Lawrence Berkeley National Laboratory
Elaine R. Chan - Lawrence Berkeley National Laboratory
Alexander Hexemer - Lawrence Berkeley National Laboratory
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Massively Parallel X-Ray Scattering Simulations
SESSION: Massively Parallel Simulations
EVENT TYPE:
TIME: 11:00AM - 11:30AM
SESSION CHAIR: Kamesh Madduri
AUTHOR(S):Abhinav Sarje, Xiaoye S. Li, Slim Chourou, Elaine R. Chan, Alexander Hexemer
ROOM:355-EF
ABSTRACT:
Although present X-ray scattering techniques can provide tremendous information
on the nano-structural properties of materials that are valuable in the design
and fabrication of energy-relevant nano-devices, a primary challenge remains in
the analyses of such data. In this paper we describe a high-performance,
flexible, and scalable Grazing Incidence Small Angle X-ray Scattering simulation
algorithm and codes that we have developed on multi-core/CPU and many-core/GPU clusters. We discuss in detail our implementation, optimization and performance on these platforms. Our results show speedups of ~125x on a Fermi-GPU and ~20x on a Cray-XE6 24-core node, compared to a sequential CPU code, with near linear scaling on multi-node clusters. To our knowledge, this is the
first GISAXS simulation code that is flexible to compute scattered light
intensities in all spatial directions allowing full reconstruction of GISAXS
patterns for any complex structures and with high-resolutions while reducing
simulation times from months to minutes.
Chair/Author Details:
Kamesh Madduri (Chair) - Pennsylvania State University
Abhinav Sarje - Lawrence Berkeley National Laboratory
Xiaoye S. Li - Lawrence Berkeley National Laboratory
Slim Chourou - Lawrence Berkeley National Laboratory
Elaine R. Chan - Lawrence Berkeley National Laboratory
Alexander Hexemer - Lawrence Berkeley National Laboratory
Click here to download .ics calendar file