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.
PY - 2015/11
Y1 - 2015/11
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.
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-84931051757&origin=inward
U2 - 10.1016/j.compeleceng.2015.05.018
DO - 10.1016/j.compeleceng.2015.05.018
M3 - Journal article
SN - 0045-7906
VL - 46
SP - 176
EP - 189
JO - Computers and Electrical Engineering
JF - Computers and Electrical Engineering
ER -