- #Ultra chip computational fluid dynamics benchmarks how to#
- #Ultra chip computational fluid dynamics benchmarks code#
© 2020 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc. This detailed computational framework with improved modeling techniques and an extensive validation procedure will be used in future CFD studies of centrifugal blood pumps to aid in device design and predictions of their biological responses.įood and Drug Administration centrifugal blood pump computational fluid dynamics hemolysis validation. These results suggest that the importance of accounting for blood's viscoelasticity may be dependent on the specific blood pump operating conditions.
Small differences were observed between the Newtonian and viscoelastic blood models in pressure head and hemolysis at the higher flow rate cases (FDA Conditions 4 and 5) but were more significant at lower flow rate and pump impeller speeds (FDA Condition 1). While CFD radial velocity profiles between the impeller blades also compared well to the PIV velocity results, more work is still needed to address the large variability among both experimental and computational predictions of velocity in the diffuser outlet jet. The CFD simulations were able to match the FDA pressure and hemolysis data for multiple pump operating conditions, with the CFD results being within the standard deviations of the experimental results. The established computational framework, including a dynamic rotating mesh, animal blood-specific fluid properties and hemolysis modeling, and a k-ω SST turbulence model, was shown to more accurately predict pump pressure heads, velocity fields, and hemolysis compared to previously published CFD studies of the FDA centrifugal pump. A viscoelastic blood model was then incorporated into the CFD solver to investigate the importance of modeling blood's viscoelasticity in centrifugal pumps. A Newtonian blood model was first used to compare to the PIV data with a blood analog fluid while hemolysis data were compared using a power-law hemolysis model fit to porcine blood data. Therefore, the Food and Drug Administration (FDA) benchmark centrifugal blood pump and its database of experimental PIV and hemolysis data were used to thoroughly validate CFD simulations of the same blood pump. They must also account for and accurately model the specific working fluid in the pump, whether that is a blood-analog solution to match an experimental PIV study or animal blood in a hemolysis experiment. So I expect I left some performance on the table by using gfortran for M1.In order to simulate hemodynamics within centrifugal blood pumps and to predict pump hemolysis, CFD simulations must be thoroughly validated against experimental data. The Intel compiler does some aggressive vectorization and optimization when compiling USM3D, and historically it has given better performance on x86-64 than gfortran.
#Ultra chip computational fluid dynamics benchmarks how to#
“I’ve used the Intel Fortran compiler for over 30 years (it was DEC Fortran then Compaq Fortran before becoming Intel Fortran) and I know how to get the most out of it. “To be honest, I think this puts the M1 USM3D executable at a slight disadvantage to the Intel USM3D executable,” he continued.
“I used the stock USM3D source with gfortran and did a fairly standard compile with -O3 optimization.”
#Ultra chip computational fluid dynamics benchmarks code#
“I didn’t link to any Apple frameworks when compiling USM3D on M1, or attempt to tune or optimize code for Accelerate or AMX,” the engineer and app developer said. According to Hunter’s results, an M1 Ultra running six threads matches the performance of a 28-core Xeon workstation from 2019.Īny lingering hopes that the M1 Ultra suffers a sudden and unexplained scaling calamity above six cores are dashed once we extend the graph’s y-axis high enough to accommodate the data. While the chip family has been widely reviewed in a number of common consumer applications, inevitable differences between macOS and Windows, the impact of emulation, and varying degrees of optimization between x86 and M1 all make precise measurement more difficult.Īn interesting new benchmark result and accompanying review from app developer and engineer Craig Hunter shows the M1 Ultra absolutely destroying every Intel x86 CPU on the field. It’s surprisingly hard to pin down exactly how Apple’s M1 compares to Intel’s x86 processors. By Joel Hruska in ExtremeTech on at 5:00 am.The Apple M1 Ultra Crushes Intel in Computational Fluid Dynamics Performance
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