Accelerating aerial image simulation using improved CPU/GPU collaborative computing

Fan Zhang, Chen Hu, Pei Ci Wu, Hongbo Zhang, Martin D.F. Wong

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)176-189
Number of pages14
JournalComputers and Electrical Engineering
StatePublished - 2015


  • Advanced vector extensions
  • Collaborative computing
  • Dynamic task scheduling
  • GPU parallel
  • Lithography simulation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science
  • Electrical and Electronic Engineering


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