Scaling SINBAD software to 3-D on Yemoja
Transcript of Scaling SINBAD software to 3-D on Yemoja
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University of British ColumbiaSLIM
Curt Da Silva, Haneet Wason, Mathias Louboutin, Bas Peters, Shashin Sharan, Zhilong Fang
Scaling SINBAD software to 3-D on Yemoja
Wednesday, 28 October, 15
Released to public domain under Creative Commons license type BY (https://creativecommons.org/licenses/by/4.0).Copyright (c) 2018 SINBAD consortium - SLIM group @ The University of British Columbia.
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This talk
Showcase SLIM software as it applies to larg(er) scale problems on the Yemoja cluster
Performance scaling• as # parallel resources increases• comparisons to existing codes in C
Large data examples
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FWI - Time DomainMathias Louboutin
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Linear algebra form
Au = q
1
v2@2u
@t2�r2u = q
Acoustic wave equation in time domain
4
A : time domain forward modelling matrix
u : vectorized wavefield of all time steps and modelling grid points
q : source
Continuous form
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Usual forward modelling
Fully discretized wave equation
with:
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A1 = diag(m
4t2)
A3 = diag(m
4t2)
A2 = �L� 2diag(m
4t2)
A1uk+1 +A2u
k +A3uk�1 = qk�1
qk : Source wave field at time step k
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FWI Gradient
The FWI gradients have to pass the adjoint test:‣ we only compute actions of , never the matrices itself‣ to ensure they are true adjoints, the migration/demigration operators need to satisfy
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J,JT
||�dTJ�m� �mTJT �d|| < ✏
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Gradient Test
7 10 -6 10 -5 10 -4 10 -3 10 -2 10 -110 -12
10 -10
10 -8
10 -6
10 -4
10 -2
10 0
10 2
10 4 Multiparameter Gradient Test
Zeroth order Taylor ErrorO(h)First order Taylor ErrorO(h2)
||F(m0 + h · �m, ✏0 + h · �✏)� F(m0, ✏0)||
||F(m0 + h · �m, ✏0 + h · �✏)�F(m0, ✏0)� h · Jm�m� h · J✏�✏||
Ensure 2nd order convergence of Taylor expansion
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True velocity
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X location (m)0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Dep
th (m
)
0
500
1000
1500
2000 1.5
2
2.5
3
3.5
4
4.5
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Good initial model
X location (m)0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Dep
th (m
)
0
500
1000
1500
2000 1.5
2
2.5
3
3.5
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4.5
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FWI
X location (m)0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Dep
th (m
)
0
500
1000
1500
2000 1.5
2
2.5
3
3.5
4
4.5
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Implementation
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Chevron Modeling code
Modeling only
SSE and AVX enabled
10th order in space, 4th order in time
Stencil based (No matrix)
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Matlab basic
Matrix based
No fancy speed up yet
Contains • Forward time stepping and adjoint time stepping (true adjoint)• Jacobian and its adjoint (true adjoint)• Necessary for FWI,LSRTM.....
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Matlab basic
Setup : 300sec for 400x400x400 (1 source)
Time step
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A1_inv : 1.891 GBA2 : 16.968 GBA3 : 1.891 GBPs : 1.703 KBU1 : 645.481 MBU2 : 645.481 MBU3 : 645.481 MBadjoint_mode : 1.000 Bmode : 8.000 Bnt : 8.000 Bop : 645.494 MBx : 38.586 KBy : 645.481 MB==========================T : 23.902 GB
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Matlab advanced
No matrix => stencil based + 3 vectors
Double precision => Single precision
Matlab MatVec => C MatVec with multi RHS
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Matlab advanced
Single precision + stencil based : • 20 times less memory than sparse matrices
Single precision :• Wavefields two times less expensive memory-‐wise
C MatVec• Multi-‐threaded over RHS • No matrix instead of 20• Communication overhead between Matlab-‐C
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Setup : 17 sec for 400x400x400 (20 sources)
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N : 12.000 BP : 8.000 BU1 : 5.673 GBU2 : 5.673 GBU3 : 5.673 GBa1i : 322.741 MBa2 : 322.741 MBa3 : 322.741 MBadjoint_mode : 1.000 Bd : 4.000 Bi : 8.000 Bidx : 52.000 Bidxsrc : 7.594 KBmode : 8.000 Bnt : 8.000 Bop : 645.495 MBtsrc : 8.000 Bwsrc : 7.594 KBx : 19.293 KBy : 5.673 GB==========================T : 24.269 GB
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Memory usage
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N : 12.000 BP : 8.000 BU1 : 5.673 GBU2 : 5.673 GBU3 : 5.673 GBa1i : 322.741 MBa2 : 322.741 MBa3 : 322.741 MBadjoint_mode : 1.000 Bd : 4.000 Bi : 8.000 Bidx : 52.000 Bidxsrc : 7.594 KBmode : 8.000 Bnt : 8.000 Bop : 645.495 MBtsrc : 8.000 Bwsrc : 7.594 KBx : 19.293 KBy : 5.673 GB==========================T : 24.269 GB
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Timings and memory
How does Matlab scale
• Compared with the Chevron modelling code
• Compared to single precision multi RHS multiplication
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Single time step
For a given 561x561x194 cube
Matlab
• 100Gb of RAM
• 2 sec per time-‐step per source
• 40 sec per time step for 20 sources (20 runs)
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Single time step
For a given 561x561x194 cube
Chevron
• .10 sec per time-‐step (20 threads)
• 2 sec per time step for 20 sources (needs to run 20 times)
• Stencil based, 0 RAM for matrices
• 1Gb of RAM (one source at a time)
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Single time step
For a given 561x561x194 cube
Chevron
• 2 sec per time-‐step (1 threads)
• 2 sec per time step for 20 sources (can run 20 at once)
• Stencil based, 0 RAM for matrices
• 1Gb of RAM (one source at a time)
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Single time step
For a given 561x561x194 cube
Single precision MEX Matlab
• ~.2 sec per Time-‐step per source
• ~ 4 sec for 20 source per time step
• 0Gb of RAM for the matrices
• 1Gb of RAM per source
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Full Waveform Inversion - Time HarmonicZhilong Fang
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FWI performance scalingModel size : 134*134*28Number of shots : 30Number of frequencies : 1
25
Time per iteration Memory use per node
1 node * 8 processes 1 hour 0.5 GB
1 node * 16 processes 0.53 hours 1 GB
5 nodes * 16 processes 0.15 hours 1 GB
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FWI performance scalingModel size : 268*268*56Number of shots : 30Number of frequencies : 1
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Time per iteration Memory use per node
1 node * 8 processes 12 hours 4 GB
1 node * 16 processes 6.3 hours 8 GB
5 nodes * 16 processes 1.3 hours 8 GB
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3D WRIBas Peters
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Scaling
• 6 km cube model• ~40 wavelengths propagated between source & receiver• 8 nodes• Each node solves the PDE’s in the sub-‐problems for 8 right-‐hand-‐sides simultaneously.
• This setup can process 8 x 8 = 64 PDE solves simultaneously.• Fixed tolerance for all PDE solves.
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298 Hz. Varying number of sources & receivers (8 -‐ 256).
101 102101
102
103
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nsrc
time
[s]
8 nodes , 8Hz
Totalcomp Ucomp Wother
0 50 100 150 200 250 30040
50
60
70
80
90
100
110
nsrc
time
[s]
time per source
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101 102101
102
103
104
nsrc
time
[s]
8 nodes , 8Hz
Totalcomp Ucomp Wother
0 50 100 150 200 250 30040
50
60
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80
90
100
110
nsrc
time
[s]
time per source
not enough sources & receiversto use computational capacity of the nodes
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101 102101
102
103
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nsrc
time
[s]
8 nodes , 8Hz
Totalcomp Ucomp Wother
0 50 100 150 200 250 30040
50
60
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80
90
100
110
nsrc
time
[s]
time per source
more sources & receivers,still close to constant time per source
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101 102101
102
103
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nsrc
time
[s]
8 nodes , 8Hz
Totalcomp Ucomp Wother
0 50 100 150 200 250 30040
50
60
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90
100
110
nsrc
time
[s]
time per source
more sources & receivers,still close to constant time per source
other costs (including communication) increase, but remain relatively small
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100 101101
102
103
104
# of nodes
time
[s]
64 sources & receivers , 8Hz
Totalcomp Ucomp Wother
8 Hz. 64 sources & 64 receivers. Varying number of nodes (2 -‐ 16).
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100 101101
102
103
104
# of nodes
time
[s]
64 sources & receivers , 8Hz
Totalcomp Ucomp Wother
not enough sources & receivers to use computational capacity of the nodes; results in smaller speedup
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3D data simulationHaneet Wason & Shashin Sharan
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Simulation parameters
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3D ocean bottom cable/node data set generated on the BG 3D Compass model- model size (nz x nx x ny): 164 x 601 x 601- grid size: 6 m x 25 m x 25 m
Data dimensions (2501 x 500 x 500 x 85 x 85)
- number of time samples: 2501 - number of receivers in x & y direction: 500 - number of shots in x & y direction: 85- sampling intervals: 0.004 s, 25 m (receiver), 150 m (shot)
Simulated with the Chevron 3D modeling code
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BG 3D Compass model
Lateral [m]0 5000 10000 15000
Dept
h [m
]0
500
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Lateral [m]0 5000 10000 15000
Dept
h [m
]
0
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x direction
y direction
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25 m
25 mx
y
150 m
150 m
Source
Receiver
Source-receiver layout
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Computational resources used
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Time & memory usage
Node partition: 128 GBNumber of nodes: 660
Simulation per 3D shot: 1.5 hoursCumulative simulation time (85 x 85 shots): 27 hours Memory storage of one shot record: 2.5 GBMemory storage of all shot records: 18 TB
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Running jobs & activated nodes (SENAI Yemoja cluster)
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3D shot records
Ry direction Rx direction
(nt x nrx x nry = 2500 x 500 x 500)
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X & Y receiver spacing(m)
X & Y shot spacing (m)
Number of shots(X x Y)
Disk space (TB)
25 25 500 x 500 610
25 50 250 x 250 153
25 75 165 x 165 67
25 100 125 x 125 38.5
25 125 100 x 100 25
25 150 85 x 85 18
Simulation estimation
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Simultaneous acquisitionHaneet Wason & Shashin Sharan
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Performance scaling
Size of 3D survey: 2500 x 500 x 10 x 500 x 50- number of time samples: 2500 - number of streamers: 10 (with 500 channels each)- number of shots in x & y direction: 500 x 50
Number of workers Number of SPGL1 iteraDons
Recovery Dme per seismic line (hrs)
Recovery Dme all data (days)
20 200 78 162
50 200 31 64
100 200 16 33
500 200 3 6
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Interpolation - Tensor CompletionCurt Da Silva
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Hierarchical Tucker formatX � n1 ⇥ n2 ⇥ n3 ⇥ n4 tensor
U12
n1n2
k12
! U12n1
n2k12
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Hierarchical Tucker formatX � n1 ⇥ n2 ⇥ n3 ⇥ n4 tensor
U12
n1n2
k12
! U12n1
n2k12
! U1
UT2
n1
k1 n2
k2
B12
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Data set
Generated from the BG compass model using time-‐stepping
Transformed in to frequency slices, ~26 GB in size
85 x 85 sources at 150m spacing, 500 x 500 receivers at 25m spacing
90% receiver pairs removed, on-‐grid sampling
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Tensor interpolation
Parallelized over frequencies, implicit parallelism via Matlab’s calls to LAPACK
20 iterations, Gauss-‐Newton Hierarchical Tucker interpolation
Each frequency slice takes 13-‐15 hours to interpolate, ~70-‐80 GB max memory
Run on the Yemoja cluster in Brazil “out of the box”
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HT Interpolation - 90% missing receiversCommon source gather - 10Hz
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receiver x50 100 150 200 250 300 350 400 450 500
rece
iver y
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True data Subsampled data (input)
receiver x100 200 300 400 500
rece
iver y
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receiver x50 100 150 200 250 300 350 400 450 500
rece
iver y
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receiver x100 200 300 400 500
rece
iver y
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True data Interpolated data -‐ SNR 19.3 dB
HT Interpolation - 90% missing receiversCommon source gather - 10Hz
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receiver x50 100 150 200 250 300 350 400 450 500
rece
iver y
50
100
150
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250
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True data Difference
receiver x100 200 300 400 500
rece
iver y
50
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HT Interpolation - 90% missing receiversCommon source gather - 10Hz
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SNR vs Frequency
53frequency [hz]
50 60 70 80 90 100
SNR
[dB]
18
20
22
24
26
28
30Train SNRTest SNR
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Acknowledgements
This work was financially supported by SINBAD Consortium members BG Group, BGP, CGG, Chevron, ConocoPhillips, DownUnder GeoSolutions, Hess, Petrobras, PGS, Schlumberger, Statoil, Sub Salt Solutions and Woodside; and by the Natural Sciences and Engineering Research Council of Canada via NSERC Collaborative Research and Development Grant DNOISEII (CRDPJ 375142-‐-‐08).
Thank you for your attention
Wednesday, 28 October, 15