Accelerate analytical placement with GPU: A generic approach

Chun-Xun Lin, Martin D. F. Wong

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

14 Citations (Scopus)

Abstract

This paper presents a generic approach of exploiting GPU parallelism to speed up the essential computations in VLSI nonlinear analytical placement. We consider the computation of wirelength and density which are widely used as cost and constraint in nonlinear analytical placement. For wirelength gradient computing, we utilize the sparse characteristic of circuit graph to transform the compute-intensive portions into sparse matrix multiplications, which effectively optimizes the memory access pattern and mitigates the imbalance workload. For density, we introduce a computation flattening technique to achieve load balancing among threads and a High-Precision representation is integrated into our approach to guarantee the reproducibility. We have evaluated our method on a set of contest benchmarks from industry. The experimental results demonstrate our GPU method achieves a better performance over both the CPU methods and the straightforward GPU implementation.
Original languageEnglish
Title of host publication2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)
PublisherIEEE
Pages1345-1350
Number of pages6
ISBN (Electronic)9783981926309, 9783981926316 (USB)
DOIs
Publication statusPublished - Mar 2018
Event2018 Design, Automation & Test in Europe Conference & Exhibition, DATE 2018 - Dresden, Germany
Duration: 19 Mar 201823 Mar 2018
https://ieeexplore.ieee.org/xpl/conhome/8337149/proceeding (Link to conference proceedings)

Publication series

NameProceedings of Design, Automation & Test in Europe Conference & Exhibition

Conference

Conference2018 Design, Automation & Test in Europe Conference & Exhibition, DATE 2018
Country/TerritoryGermany
CityDresden
Period19/03/1823/03/18
Internet address

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