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Original research
Quantification of speed-up and accuracy of multi-CPU computational flow dynamics simulations of hemodynamics in a posterior communicating artery aneurysm of complex geometry
  1. Christof Karmonik1,
  2. Christopher Yen1,
  3. Edgar Gabriel2,
  4. Sasan Partovi3,
  5. Marc Horner4,
  6. Yi J Zhang1,
  7. Richard P Klucznik5,
  8. Orlando Diaz5,
  9. Robert G Grossman1
  1. 1Department of Neurosurgery, The Methodist Hospital Neurological Institute, Houston, Texas, USA
  2. 2Department of Computer Science, University of Houston, Houston, Texas, USA
  3. 3Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
  4. 4ANSYS, Inc, Evanston, Illinois, USA
  5. 5Department of Radiology, The Methodist Hospital, Houston, Texas, USA
  1. Correspondence to Dr Christof Karmonik, Department of Neurosurgery, The Methodist Hospital Neurological Institute, 6565 Fannin ST 9, Houston, TX 77030, USA; ckarmonik{at}


Background Towards the translation of computational fluid dynamics (CFD) techniques into the clinical workflow, performance increases achieved with parallel multi-central processing unit (CPU) pulsatile CFD simulations in a patient-derived model of a bilobed posterior communicating artery aneurysm were evaluated while simultaneously monitoring changes in the accuracy of the solution.

Methods Simulations were performed using 2, 4, 6, 8, 10 and 12 processors. In addition, a baseline simulation was obtained with a dual-core dual CPU computer of similar computational power to clinical imaging workstations. Parallel performance indices including computation speed-up, efficiency (speed-up divided by number of processors), computational cost (computation time × number of processors) and accuracy (velocity at four distinct locations: proximal and distal to the aneurysm, in the aneurysm ostium and aneurysm dome) were determined from the simulations and compared.

Results Total computation time decreased from 9 h 10 min (baseline) to 2 h 34 min (10 CPU). Speed-up relative to baseline increased from 1.35 (2 CPU) to 3.57 (maximum at 10 CPU) while efficiency decreased from 0.65 to 0.35 with increasing cost (33.013 to 92.535). Relative velocity component deviations were less than 0.0073% and larger for 12 CPU than for 2 CPU (0.004±0.002%, not statistically significant, p=0.07).

Conclusions Without compromising accuracy, parallel multi-CPU simulation reduces computing time for the simulation of hemodynamics in a model of a cerebral aneurysm by up to a factor of 3.57 (10 CPUs) to 2 h 34 min compared with a workstation with computational power similar to clinical imaging workstations.

  • Aneurysm
  • MRI
  • Technique

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