hardware acceleration of computional fluid dynamics simulations in an oxygenator - part 2
TRANSCRIPT
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Politecnico di MilanoDipartimento di Elettronica, Informazione e Bioingegneria (DEIB)
Hardware Acceleration of
Computational Fluid Dynamics
Simulations in an Oxygenator
Work progress
Chiara [email protected]
B3Lab, May 30th 2016
Guido [email protected]
HAMSproject
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1
Our goal
Study an oxygenator for ECC through a lumped
parameters model in MATLAB and accelerate the
simulation by means of an FPGA-based system
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1
Our goal
Study an oxygenator for ECC through a lumped
parameters model in MATLAB and accelerate the
simulation by means of an FPGA-based system
![Page 4: Hardware Acceleration of Computional Fluid Dynamics Simulations in an Oxygenator - Part 2](https://reader031.vdocuments.net/reader031/viewer/2022030303/587c2ceb1a28aba0118b5ad7/html5/thumbnails/4.jpg)
1
Our goal
Study an oxygenator for ECC through a lumped
parameters model in MATLAB and accelerate the
simulation by means of an FPGA-based system
Lumped
parameters
model
![Page 5: Hardware Acceleration of Computional Fluid Dynamics Simulations in an Oxygenator - Part 2](https://reader031.vdocuments.net/reader031/viewer/2022030303/587c2ceb1a28aba0118b5ad7/html5/thumbnails/5.jpg)
1
Our goal
Study an oxygenator for ECC through a lumped
parameters model in MATLAB and accelerate the
simulation by means of an FPGA-based system
Lumped
parameters
model
![Page 6: Hardware Acceleration of Computional Fluid Dynamics Simulations in an Oxygenator - Part 2](https://reader031.vdocuments.net/reader031/viewer/2022030303/587c2ceb1a28aba0118b5ad7/html5/thumbnails/6.jpg)
1
Our goal
Study an oxygenator for ECC through a lumped
parameters model in MATLAB and accelerate the
simulation by means of an FPGA-based system
Lumped
parameters
model
![Page 7: Hardware Acceleration of Computional Fluid Dynamics Simulations in an Oxygenator - Part 2](https://reader031.vdocuments.net/reader031/viewer/2022030303/587c2ceb1a28aba0118b5ad7/html5/thumbnails/7.jpg)
2
Progress
Context
(the oxygenator)
Implementation
(SW-HW)Validation
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3
Context
(the oxygenator)
Implementation
(SW-HW)Validation
Until 04.05
Progress
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4
Context
(the oxygenator)
Implementation
(SW-HW)Validation
Until 04.05 TODAY
Progress
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Implementation
Model = linear system
Solution achieved through matrix inversion
(Moore-Penrose Pseudoinverse)
5
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Implementation
Model = linear system
Solution achieved through matrix inversion
(Moore-Penrose Pseudoinverse)
SVD methodGreville’s algo Others
5
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Implementation
Model = linear system
Solution achieved through matrix inversion
(Moore-Penrose Pseudoinverse)
SVD methodGreville’s algo Others
QR method Jacobi Others
5
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6
Implementation
Jacobi method: accuracy and parallelism
for (n_times)
split matrix into columns
compute c,s for each couple
update columns
end
until convergence
multiply&accumulate
streaming computation
Ãi Ãj
Coefficient
computation
Column
updateAi Aj
c,s
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7
Implementation
Sparse matrix
The matrix is generated through the device meshing
(with known meshing pattern)
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7
Implementation
Sparse matrix
The matrix is generated through the device meshing
(with known meshing pattern)
The modeled matrix is sparse
(with known pattern)
We identify non-null elements
through MATLAB script
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8
Methods
MATLAB
functionsHigh level
Low level
Vivado HLS Vivado SDK
FPGAs
Our framework
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9
Results
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10
Context
(the oxygenator)
Implementation
(SW-HW)Validation
Until 04.05 From 1.06
Progress
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11
Validation
MAIN COMPARISONS
FPGAs
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Validation
MAIN COMPARISONS
FPGAs
vs vs
11
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Validation
KEY PARAMETERS
imgflip.com
SPEEDUP
vs competitors
12
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Validation
KEY PARAMETERS
imgflip.com
SPEEDUP
vs competitors
POWER
REQUIREMENTS
12
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Validation
KEY PARAMETERS
imgflip.com
SPEEDUP
vs competitors
POWER
REQUIREMENTS
EASE OF USE
12
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QUESTIONS?
Contact us!
HAMSproject
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