Enhancing the performance of conjugate gradient solvers on graphic processing units

Maryam Mehri Dehnavi, David M. Fernández, Dennis Giannacopoulos

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


A study of the fundamental obstacles to accelerate the preconditioned conjugate gradient (PCG) method on modern graphic processing units (GPUs) is presented and several techniques are proposed to enhance its performance over previous work independent of the GPU generation and the matrix sparsity pattern. The proposed enhancements increase the performance of PCG up to 23 times compared to vector optimized PCG results on modern CPUs and up to 3.4 times compared to previous GPU results.

Original languageEnglish (US)
Article number5754727
Pages (from-to)1162-1165
Number of pages4
JournalIEEE Transactions on Magnetics
Issue number5
StatePublished - May 2011

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering


  • Computer architecture
  • conjugate gradients (CGs)
  • graphic processing units (GPUs)
  • parallel processing

Fingerprint Dive into the research topics of 'Enhancing the performance of conjugate gradient solvers on graphic processing units'. Together they form a unique fingerprint.

Cite this