Accelerating aerial image simulation using improved CPU/GPU collaborative computing. (August 2015)

• Record Type: Journal Article
• Authors: Zhang, Fan; Hu, Chen; Wu, Pei-Ci; Zhang, Hongbo; Wong, Martin D.F.
• Is Part Of: Computers & electrical engineering. Volume 46(2015)
• Page Start: 176
• Page End: 189
• Publication Date: 2015-08
• Subjects: Lithography simulation -- Collaborative computing -- Dynamic task scheduling -- Advanced vector extensions -- GPU parallel; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.05.018
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: 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.