TY - JOUR
T1 - Accelerating aerial image simulation using improved CPU/GPU collaborative computing
AU - Zhang, Fan
AU - Hu, Chen
AU - Wu, Pei Ci
AU - Zhang, Hongbo
AU - Wong, Martin D.F.
N1 - Funding Information:
This work was supported by the Beijing Higher Education Young Elite Teacher Project under Grant YETP0500 , the Fundamental Research Funds for the Central Universities under Grant YS 1404 , and the Interdisciplinary Research Project in Beijing University of Chemical Technology .
Publisher Copyright:
© 2015 Elsevier Ltd.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2015
Y1 - 2015
N2 - Aerial image simulation is a fundamental problem in advanced lithography for chip fabrication. Since it requires a huge number of mathematical computations, an efficient yet accurate implementation becomes a necessity. In the literature, graphic processing unit (GPU) or multi-core single instruction multiple data (SIMD) CPU has demonstrated its potential for accelerating simulation. However, the combination of GPU and multi-core SIMD CPU was not exploited thoroughly. In this paper, we present and discuss collaborative computing algorithms for the aerial image simulation on multi-core SIMD CPU and GPU. Our improved method achieves up to 160× speedup over the baseline serial approach and outperforms the state-of-the-art GPU-based approach by up to 4× speedup with a hex-core SIMD CPU and Tesla K10 GPU. We show that the performance on the collaborative computing is promising, and the medium-grained task scheduling is suitable for improving the collaborative efficiency.
AB - Aerial image simulation is a fundamental problem in advanced lithography for chip fabrication. Since it requires a huge number of mathematical computations, an efficient yet accurate implementation becomes a necessity. In the literature, graphic processing unit (GPU) or multi-core single instruction multiple data (SIMD) CPU has demonstrated its potential for accelerating simulation. However, the combination of GPU and multi-core SIMD CPU was not exploited thoroughly. In this paper, we present and discuss collaborative computing algorithms for the aerial image simulation on multi-core SIMD CPU and GPU. Our improved method achieves up to 160× speedup over the baseline serial approach and outperforms the state-of-the-art GPU-based approach by up to 4× speedup with a hex-core SIMD CPU and Tesla K10 GPU. We show that the performance on the collaborative computing is promising, and the medium-grained task scheduling is suitable for improving the collaborative efficiency.
KW - Advanced vector extensions
KW - Collaborative computing
KW - Dynamic task scheduling
KW - GPU parallel
KW - Lithography simulation
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U2 - 10.1016/j.compeleceng.2015.05.018
DO - 10.1016/j.compeleceng.2015.05.018
M3 - Article
AN - SCOPUS:84931051757
VL - 46
SP - 176
EP - 189
JO - Computers and Electrical Engineering
JF - Computers and Electrical Engineering
SN - 0045-7906
ER -