Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/SC.2012.76 | SC |
Keywords | Field | DocType |
complex structure,simulation time,gisaxs pattern,cray-xe6 24-core node,gpu cluster,gisaxs simulation code,sequential cpu code,massively parallel x-ray,angle x-ray,simulation algorithm,present x-ray,lattice qcd,data analysis,nanofabrication,simd | Cluster (physics),Linear scale,Instruction set,Massively parallel,Computer science,Parallel computing,Grazing-incidence small-angle scattering,SIMD,Computational science,Scattering,Scalability | Conference |
ISSN | ISBN | Citations |
2167-4329 | 978-1-4673-0804-5 | 3 |
PageRank | References | Authors |
0.90 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abhinav Sarje | 1 | 35 | 5.71 |
| Xiaoye S. Li | 2 | 1042 | 98.22 |
| Slim Chourou | 3 | 3 | 0.90 |
| Elaine R. Chan | 4 | 3 | 0.90 |
| Alexander Hexemer | 5 | 5 | 2.71 |