Update @30.05 of our project's application in the biomedical field: CFD simulations in an oxygenator for ECC !!

1. Politecnico di Milano
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB)
Hardware Acceleration of
Computational Fluid Dynamics
Simulations in an Oxygenator
Work progress
Chiara Gatti
chiara1.gatti@mail.polimi.it
B3Lab, May 30th 2016
Guido Lanfranchi
guido2.lanfranchi@mail.polimi.it
HAMSproject

2. 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

3. 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

4. 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

5. 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

6. 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

7. 2
Progress
Context
(the oxygenator)
Implementation
(SW-HW)
Validation

8. 3
Context
(the oxygenator)
Implementation
(SW-HW)
Validation
Until 04.05
Progress

9. 4
Context
(the oxygenator)
Implementation
(SW-HW)
Validation
Until 04.05 TODAY
Progress

10. Implementation
Model = linear system
Solution achieved through matrix inversion
(Moore-Penrose Pseudoinverse)
5

11. Implementation
Model = linear system
Solution achieved through matrix inversion
(Moore-Penrose Pseudoinverse)
SVD methodGreville’s algo Others
5

12. Implementation
Model = linear system
Solution achieved through matrix inversion
(Moore-Penrose Pseudoinverse)
SVD methodGreville’s algo Others
QR method Jacobi Others
5

13. 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
update
Ai Aj
c,s

14. 7
Implementation
Sparse matrix
The matrix is generated through the device meshing
(with known meshing pattern)

15. 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

16. 8
Methods
MATLAB
functions
High level
Low level
Vivado HLS Vivado SDK
FPGAs
Our framework

17. 9
Results

18. 10
Context
(the oxygenator)
Implementation
(SW-HW)
Validation
Until 04.05 From 1.06
Progress

19. 11
Validation
MAIN COMPARISONS
FPGAs

20. Validation
MAIN COMPARISONS
FPGAs
vs vs
11

21. Validation
KEY PARAMETERS
imgflip.com
SPEEDUP
vs competitors
12

22. Validation
KEY PARAMETERS
imgflip.com
SPEEDUP
vs competitors
POWER
REQUIREMENTS
12

23. Validation
KEY PARAMETERS
imgflip.com
SPEEDUP
vs competitors
POWER
REQUIREMENTS
EASE OF USE
12

24. QUESTIONS?
Contact us!
HAMSproject
hams.necst@gmail.com
chiara1.gatti@mail.polimi.it
guido2.lanfranchi@mail.polimi.it
www.facebook.com/hams.project
https://twitter.com/HAMS_project
http://www.slideshare.net/HAMSproject