Acceleration of tensor-product operations for high-order finite element methods

Swirydowicz K., Chalmers N., Karakus A., Warburton T.

INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS, cilt.33, sa.4, ss.735-757, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33 Sayı: 4
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1177/1094342018816368
  • Dergi Adı: INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.735-757
  • Anahtar Kelimeler: Finite element method, elliptic problem, hexahedral elements, matrix-vector product, GPU tensor operations, NVIDIA Tesla P100, LEVEL, MODEL
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

This article is devoted to graphics processing unit (GPU) kernel optimization and performance analysis of three tensor-product operations arising in finite element methods. We provide a mathematical background to these operations and implementation details. Achieving close to peak performance for these operators requires extensive optimization because of the operators' properties: low arithmetic intensity, tiered structure, and the need to store intermediate results during the kernel execution. We give a guided overview of optimization strategies and we present a performance model that allows us to compare the efficacy of these optimizations against an empirically calibrated roofline.