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Energy-Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous
Architectures: A DDDAS Approach
Sanjay Ranka (PI)
Sartaj Sahni (Co-PI) • Mark Schmalz (Co-PI)
Anand Ranagarajan (Consultant)
University of Florida • Department of CISE
Gainesville, FL 32611-6120
DDDAS Program • PI Meeting • 06 Sep 2017
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Overview of Presentation
1. Research Team
2. Technical Objectives
3. Technical Approach & Results• Reconstruction
• Energy and Power Reduction
• Video SAR Simulation
• Coherent Change Detection
4. Ongoing and Future Work
5. Discussion
Energy-Aware Time Change Detection in SAR - Ranka (PI)DDDAS PI Meeting - 27 Jan 2016 2
Air Force Relevance:
High-Performance Computing using multi-resolution SAR processing technology.
Surveillance – Efficient reconstruction of SAR imagery from multiple pulse history using “green” multi-resolution approach.
Target Detection / Recognition – Power-efficient multiresolution Change Detection algorithm for reconstructed video SAR, yielding reduced power consumption as a result of multi-resolution processing.
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Research Team
Principal Investigator Sanjay Ranka, Ph.D.• Research Interests: High-Performance Computing
Energy-Aware Computing
Big Data Analytics
Co-PI Sartaj Sahni, Ph.D.• Research Interests: High-Performance Computing
Data Structures and Algorithms
Signal & Image Processing
Co-PI Mark Schmalz, Ph.D, O.D.• Research Interests: High-Performance Computing
Signal & Image Processing
Simulation, Error Analysis
DDDAS PI Meeting - 27 Jan 2016 3Energy-Aware Time Change Detection in SAR - Ranka (PI)
Students: Adeesha Wijayasiri,
Xiaohui Huang
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Objectives
Develop Energy-Efficient Algorithms for Change Detection in Video Synthetic Aperture Radar (SAR) Imagery
Topics of Investigation
• Heterogeneous Parallel Architectures (CPUs, GPUs, HMPs)
• Adaptive Algorithms for Image Tiling at Multiple Spatial Resolutions
• Adaptive Segmentation of SAR Pulse Dataset(s) at Multiple Resolutions
• Efficient SAR Image Reconstruction at Multiple Spatial Resolutionso STEEP Constraints: Space, Time, Error, Energy Profile, and Power Consumption
• Multiresolution Algorithms for Change Detectiono Effects of Noise and Cluttero Incomplete Data – Packet Drop-out, Channel Interruption (Denied Environments)o Support for Object Detection, Segmentation and Recognitiono Complexity, Efficiency (Time, Space, and Energy or Power Consumption)
DDDAS PI Meeting – 06 Sep 2017 4Energy-Aware Time Change Detection in SAR - Ranka (PI)
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Energy-Aware HPC
5Energy-Aware Time Change Detection in SAR - Ranka (PI)DDDAS PI Meeting – 06 Sep 2017
Overlapping
Communication
Hybrid
Core
Mapping
Dynamic
Voltage
Scaling
Static
Load
Balancing
Manual
Tuning
Multiresolution
Approach
Power/Energy
Performance
Tradeoffs
Man
ual
Tun
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Hyb
rid
Co
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s
Overlap
pin
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Co
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un
ication
Dyn
amic V
oltag
e Scalin
g
Mu
ltiresolu
tion
Ap
pro
ach
Perfo
rman
ce /
En
ergy
Tradeo
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Static/D
ynam
ic Lo
ad B
alance
Approach
Be
nef
its
Overview of Computational Optimization
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – SAR Reconstruction with Backprojection
6Energy-Aware Time Change Detection in SAR - Ranka (PI)
Backprojection on a Single GPU
DDDAS PI Meeting – 06 Sep 2017
Approach #1:Pulse Partitioning
Approach #2:Output Image Partitioning
Backprojection is decomposed (1) along output image dimension, so each processing device rendersa tile using all pulse data, or (2) each processing device renders an image using a subset of the pulses.
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Reconstruction (cont’d)
7Energy-Aware Time Change Detection in SAR - Ranka (PI)DDDAS PI Meeting – 06 Sep 2017
Change detection accepts twoinput images as well as a windowsize, then generates a differencemap.
DDDAS Approach: Output of
change detector is input to
algorithm that assigns spatial
resolution to image tiles.
Overview of DDDAS Approach
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Reconstruction (cont’d)
8Energy-Aware Time Change Detection in SAR - Ranka (PI)
DDDAS Resolution and Scheduling1. Master decomposes the
problem into atoms (pulses
rendered onto one tile of
output image).
2. Resolution Controller
determines spatial resolution
for rendering each tile.
3. Master sends each atom to
Multilevel Scheduler that
balances load for hetero-
geneous devices and
maintains locality of access
for efficiency.
DDDAS – Multi-Resolution Architecture
DDDAS PI Meeting – 06 Sep 2017
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Multiresolution HPC
9Energy-Aware Time Change Detection in SAR - Ranka (PI)DDDAS PI Meeting – 06 Sep 2017
Instead of computing 256 tiles at full
resolution, we compute only
6 tiles at full resolution
58 tiles at ¼ resolution
13 tiles at 1/16 resolution
Overall speedup factor = 10X
DDDAS – Multi-Resolution Backprojection Realization
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Multiresolution HPC (cont’d)
10Energy-Aware Time Change Detection in SAR - Ranka (PI)DDDAS PI Meeting – 06 Sep 2017
DDDAS – Multi-Resolution Backprojection Experimental Platform
ORNL Titan
▪ 18,688 nodes,
▪Hybrid node architecture
▪ AMD Opteron 16-core CPU and one Nvidia Tesla K20 GPU.
▪Memory per node:
32GB CPU + 6GB GPU
▪ Peak performance: 20+ petaflops
▪ 512 service and I/O nodes, and 200 cabinets, 4352 sq. ft. floor space
▪ Cray Gemini 3D torus interconnect
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Tuning:• Reduced Register usage by reordering instructions• Global memory reads optimized via Float4 array instead of 4 float arrays• Single array storing 𝑥, 𝑦, 𝑧 coordinates of sensor platform location • L1 cache size increased to 48KB
Technical Approach - Multiresolution HPC (cont’d)
11Energy-Aware Time Change Detection in SAR - Ranka (PI)DDDAS PI Meeting – 06 Sep 2017
Manual Tuning of Multi-Resolution Backprojection for Single GPUReconstructed
Image Size(pixels)
Number of SAR Pulses
High ResolutionExecution Time (sec)
Medium ResolutionExecution Time (sec)
Low ResolutionExecution Time (sec)
Single Res Multi-Res Single Res Multi-Res Single Res Multi-Res
8192x8192 1000 10.5 4.385 2.7 1.18 0.51 0.28
5000 53.75 22.36 13.36 6.01 2.56 1.37
4096x4096 1000 2.79 1.18 0.72 0.303 0.16 0.073
5000 13.72 5.83 3.52 1.56 0.76 0.36
Execution Time directly propor-tional to image resolution
700 GFlops per GPU
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Multiresolution HPC (cont’d)
12Energy-Aware Time Change Detection in SAR - Ranka (PI)
Backprojection on Multiple GPUs
DDDAS PI Meeting – 06 Sep 2017
Approach #1:Pulse Partitioning
Approach #2:Output Image Partitioning
➢ Multiresolution images create load imbalance Tile distribution balances computational load - and - Bin packing approaches are employed
Pu
lse
s
Range Bins
Output Image
GPU 1
GPU 2
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Multiresolution HPC (cont’d)
13Energy-Aware Time Change Detection in SAR - Ranka (PI)
Techniques for Computing Backprojection on Multiple GPUs
DDDAS PI Meeting – 06 Sep 2017
LA (List Assignment) Algorithm▪ Compute lower bound 𝐿𝐵 = 𝑇𝑊/𝑔 where g is the number of GPUs.
▪ Order tiles to form linear list L.
▪ Assign tiles to GPU1 in the order of list L until the first tile that causes the work load in GPU1 to be ≥ 𝐿𝐵.
▪ Then assign tiles to GPU2 beginning with next tile in L , stopping when workload in GPU2 becomes ≥ 𝐿𝐵. … and so forth for remainder of GPUs …
Theorem. When list assignment is used, the ratio of the maximum aggregate workload assigned to any GPU and the maximum aggregate assigned to any GPU in an optimal assignment is at most 1 +
𝑤𝑚𝑎𝑥−1
𝑇𝑜𝑡𝑎𝑙 𝑊𝑜𝑟𝑘𝑔 where 𝑤𝑚𝑎𝑥 is the maximum tile workload.
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Multiresolution HPC (cont’d)
14Energy-Aware Time Change Detection in SAR - Ranka (PI)
Algorithms for Computing Backprojection on Multiple GPUs
DDDAS PI Meeting – 06 Sep 2017
LPT (Longest Processing Time) Algorithm ▪ Sort the tiles into decreasing order of workload
▪ Tiles are assigned to GPUs in this order
▪ When tile 𝑖 is considered, it is assigned to the GPU that has the least aggregate workload assigned thus far
▪ For g GPUs, LPT performance bound yields 4
3−
1
3𝑔on the ratio of the maximum workload
assigned to any GPU by LPT and the maximum assigned to any GPU in an optimal assignment.
Theorem. LPT generates optimal schedules when job times come from a set𝑤𝑖| 1 ≤ 𝑖 ≤ 𝑘 such that 𝑤𝑖 < 𝑤𝑖+1, 𝑖 < 𝑘 and𝑤𝑗 is an integer multiple of 𝑤𝑖 , whenever 𝑗 > 𝑖.
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Multiresolution HPC (cont’d)
15Energy-Aware Time Change Detection in SAR - Ranka (PI)
Comparison of Algorithms for Backprojection on Multiple GPUs
DDDAS PI Meeting – 06 Sep 2017
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5
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13 14
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1 2 3 4
7 8
11 12
15 16
5 6
1 2 3 4
7 8
9 10
13 14
11 12
15 16
GPU1 (20)
GPU2 (16)
High resolution tile
Low resolution tile
Naïve Algorithm
LA Algorithm
GPU1 (18)
GPU2 (14)
LPT Algorithm
GPU2 (17)GPU1 (17)
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1 2 3 4
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1 3 15 4
9 11 13
16
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2 6 5
10 12 14
7 8
9 10
13 14
11 12
15 16
(14) GPU workload
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC
16Energy-Aware Time Change Detection in SAR - Ranka (PI)
Scaling with Multiple GPUs
DDDAS PI Meeting – 06 Sep 2017
▪ 60% of the tiles are high resolution, 20% are medium resolution, and 20% are low resolution for image size of 32,768 x 32,768 pixels for a SAR dataset of 5,000 pulses
▪ The tiles appear randomly (Naïve algorithm) or in a sorted manner (LA or LPT algorithm)▪ LPT and LA algorithms have comparable performance, and perform better than Naïve
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
17Energy-Aware Time Change Detection in SAR - Ranka (PI)
Communication Optimizations for Multiple GPUs
DDDAS PI Meeting – 06 Sep 2017
▪ Communication is overlapped with GPU Computation using CUDA streams.▪ When GPU computation time is sufficient, both MPI broadcasting and CPU-to-GPU
communication times are covered by GPU computation time.
Comparison with and without communication
overlapping in broadcasting pulse data and
location data to 256 nodes of Titan machine
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
18Energy-Aware Time Change Detection in SAR - Ranka (PI)
Communication Optimization for Multiple GPUs
DDDAS PI Meeting – 06 Sep 2017
0
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Tim
e (
Seco
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GPU Nodes
Communication Time
GPU Time
LA LPT Naive
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16 32 64 128 256 512
Tim
e (
Seco
nd
s)
GPU Nodes
Communication Time
GPU Time
LA LPT Naive
5,000 pulses 40,000 pulses
▪ 60% of the tiles are high resolution, 20% are medium resolution, and 20% are low resolution for image size of 32,768 x 32,768 pixels for a SAR dataset of 5,000 pulses
▪ The tiles appear randomly (Naïve algorithm) or in a sorted manner (LA or LPT algorithm)▪ LPT and LA algorithms have comparable performance, and perform better than Naïve
30 to 40 TFlop on 128 GPUs
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
19Energy-Aware Time Change Detection in SAR - Ranka (PI)DDDAS PI Meeting – 06 Sep 2017
Real Machine – Representative of Future Node Architecture
Portion of Computation on CPU
Portion of Computation on GPU
GPU
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
20Energy-Aware Time Change Detection in SAR - Ranka (PI)DDDAS PI Meeting – 06 Sep 2017
Theoretical Framework for Performance OptimizationMinimize E(n, f, g, S)Subject to T(n, f, g, S) <= T1 – O(n, f, g, S)
n < Nf ∈ Fg ∈ G
24 (e,g,, 2 12 core machines)
14
4
1344 R
Minimize ECPU(n, f, X) + EGPU(g, S - X)Subject to TCPU(n, f , X) < T1 – Co(n, S)
TGPU(g, S - X) < T1 – Co’(S - X)
n < Nf ∈ Fg ∈ G
24
14
4
(N.F+G).R=340R TCPU(n, f, X) × PCPU(n, f, X) TGPU(g, S-X) × PGPU(g, S-X)
TCPU(n, X)/f × PCPU(n, f) TGPU(S-X) / g × PGPU(g)N.R+N.F+R+G =340+25R
n CPU cores, freq f GPU core, freq g
X S-X
Note: Can have more than 1 GPU
E Energy consumed
n Number of CPUs
f CPU Frequency
g GPU Frequency
S Problem size
T Computation Time
T1 Constrained time
O Communic’n. overhead
N Total available CPUs
F Set of available CPU
frequencies
G Set of available GPU
frequencies
R Cardinality of workload distribution set
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
21
Modeling Framework for Performance Optimization
DDDAS PI Meeting – 06 Sep 2017
Observation 1: CPU Compu-
tation Time is Inversely Pro-
portional to CPU Clock Fre-
quency
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
Modeling Framework for Performance Optimization
DDDAS PI Meeting – 06 Sep 2017
Observation 2: GPU Compu-
tation Time is Inversely Pro-
portional to GPU Clock Fre-
quency
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
Modeling Framework for Performance Optimization
DDDAS PI Meeting – 06 Sep 2017
Observation 3: CPU
Power Consumption
Does Not Depend
Much on Problem Size
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
Modeling Framework for Performance Optimization
DDDAS PI Meeting – 06 Sep 2017
Observation 4: GPU
Power Consumption
Does Not Depend
Much on Problem Size
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
Modeling Framework for Performance Optimization
DDDAS PI Meeting – 06 Sep 2017
Observation 5: Equations for CPU
Power Consumption (including
DRAM power) Depend on the
Number of CPU Cores
PCPU(1, 𝑓) = 46.58979 + 0.008488 × 𝑓
PCPU(2, 𝑓) = 43.32728 + 0.009557 × 𝑓
PCPU(4, 𝑓) = 39.42679 + 0.011428 × 𝑓
PCPU(12, 𝑓) = 33.68219 + 0.022718 × 𝑓
PCPU(23, 𝑓) = 74.73546 + 0.043811 × 𝑓
Number of CPU Cores
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
Pareto Curves for Energy versus Runtime (Aggregated & Actual)
DDDAS PI Meeting – 06 Sep 2017
Tota
l En
erg
y (J
)
Actual
Aggregated
484-23-2400-875
256-12-2400-875
36-12-2400-87516-23-2400-875 16-12-2400-81036-12-2400-87516-12-2400-745
484-23-2400-875
256-12-2400-875
36-12-2400-875 16-23-2400-875 16-12-2400-810 36-12-2400-74516-12-2400-745
900
1000
1100
1200
1300
1400
1500
1600
1700
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1900
4.5749 4.8379 5.1285 5.1316 5.5082 5.9775 6.1803
4.5489 4.8111 5.0728 5.1113 5.5266 5.9450 5.9980
Aggregated EnergyResults
Number of Tiles in CPU Number of CPU cores
4K x 4K Image5000 pulses
--- Actual Energy ResultsCPU Frequency
GPU Frequency
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
Modelling Energy versus Runtime (Aggregated & Actual)
DDDAS PI Meeting – 06 Sep 2017
Tota
l En
erg
y (J
)
Time (seconds)
4K x 4K Image, 5000 pulses
Aggregated
Actual Tota
l En
erg
y (J
)
Time (seconds)
900
1000
1100
1200
1300
1400
1500
1600
1700
1800
1900
4.5 5 5.5 6 6.5 7900
1100
1300
1500
1700
1900
4.5 5 5.5 6 6.5 7
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
Modelling Power Consumption vs. Runtime (Aggregated & Actual)
DDDAS PI Meeting – 06 Sep 2017
Aggregated
Actual
4K x 4K Image5000 pulses
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach - Energy-Aware HPC (cont’d)
Energy vs. Runtime - Aggregated Results
DDDAS PI Meeting – 06 Sep 2017
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach (cont’d)
Simulation
• Construct Experimental Pulse Datasets from Digital Elevation Maps• Precisely Controlled Test Data• Controllable Parameters to Facilitate Performance & Error Analysis
Change Detection (CD)
• CD Algorithm Developed at UF Highlights Regions of Interest • Isolation of Regions Containing Moving Vegetation (high frequency variance)
• Construction of Multiresolution Scene Representation Optical Flow• Identification of Future Target Movement Regions (by variance & context) • Statistical ID and Tracking of Targets by Spatiotemporal (ST) Variance
30Energy-Aware Time Change Detection in SAR - Ranka (PI)DDDAS PI Meeting – 06 Sep 2017
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Simulation
Construct Experimental Pulse Datasets and Video SAR (VSAR)
31
x
y
z
Pulse EmitterReceiver
zsrS
T
Given:
Bidirectional Reflectivity Distribution Function (BRDF)
Emitter Intensity I
Received Intensity Ir = I • BRDF(qi)
Emitter Track S = (s1, s2, …, sP) S = T monostatic SAR
Receiver Track T = (t1, t2, …, tP) S ≠ T bistatic SAR
Number of Pulses P
Pulse Data Resolution (per pulse) NB bins
Resolution NxN pixels of Reconstructed Image defined on X
Frame Rate F
Objective: Construct pulse dataset D having NP
pulses of NB bins each F P NB elements
SimulatedGroundPlane X
Energy-Aware Time Change Detection in SAR - Ranka (PI)DDDAS PI Meeting – 06 Sep 2017
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Simulation (cont’d)
Results: Simulation & Reconstruction Example (urban scene)
1. Construct Digital Map (point cloud) using POVrayTM
2. Construct Experimental Pulse Datasets from Digital Elevation Maps3. Compute VSAR from Pulse Dataset using SAR Reconstruction Algorithm
DDDAS PI Meeting – 06 Sep 2017 32Energy-Aware Time Change Detection in SAR - Ranka (PI)
VSAR Frame Noise = 0, = 0.001 256x256 pixels Noise = 0, = 0.009 256x256 pixels Noise = 0, = 0.09
Packet Dropouts
SensorNoise
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach (cont’d)
33Energy-Aware Time Change Detection in SAR - Ranka (PI)
Simulation
• Construct Experimental Pulse Datasets from Digital Elevation Maps• Precisely Controlled Test Data• Controllable Parameters to Facilitate Performance & Error Analysis
Change Detection (CD)
• CD Algorithm Developed at UF Highlights Regions of Interest • Isolation of Regions Containing Moving Vegetation (high frequency variance)
• Construction of Multiresolution Scene Representation Optical Flow• Statistical ID and Tracking of Targets by Spatiotemporal (ST) Variance• Identification of Future Target Movement Regions (by ST variance & context)
DDDAS PI Meeting – 06 Sep 2017
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Change Detection
34Energy-Aware Time Change Detection in SAR - Ranka (PI)
Background
DDDAS PI Meeting – 06 Sep 2017
▪ Optical flow and Supervoxel-based Segmentation• 2D gPb-UCM (Arbelaez, 2011) extension to 3D• Optical flow estimation• More accurate and scalable approach
▪ Challenges in 3D volumetric image segmentation• Complex backgrounds• Variations of object size• Imbalance between the number of foreground and
background voxels
▪ Integration with optical flow• Optical flow estimates object motion• Assume different objects move differently
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Change Detection (cont’d)
35Energy-Aware Time Change Detection in SAR - Ranka (PI)
Contributions of 3D-UCM
DDDAS PI Meeting – 06 Sep 2017
Step 1: Image Gradient Features Detection• Developed 3D counterpart of the oriented
gradient operators
Step 2: Globalization• More robust way to compute affinity
matrix
• Reduced order Normalized Cuts
Step 3: Agglomeration• Graph based methods instead of
Oriented Watershed Transform
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Change Detection (cont’d)
36Energy-Aware Time Change Detection in SAR - Ranka (PI)
Application to Change Detection in Airborne/Spaceborne Imagery
DDDAS PI Meeting – 06 Sep 2017
▪ Optical flow for moving object detection/tracking
▪ Advance in convolutional neural network
▪ Spatiotemporal machine learning for remote sensing
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Change Detection (cont’d)
37Energy-Aware Time Change Detection in SAR - Ranka (PI)
Application to Change Detection in Airborne/Spaceborne Imagery
DDDAS PI Meeting – 06 Sep 2017
▪ Optical Flow – Video Application
1. Segmentation
• Background subtraction
• Shot boundary detection
• Motion segmentation
• Object detection and tracking
2. Video stabilization
3. 3D structure estimation
4. Image registration
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Change Detection (cont’d)
38Energy-Aware Time Change Detection in SAR - Ranka (PI)
Application to Change Detection in Airborne/Spaceborne Imagery
DDDAS PI Meeting – 06 Sep 2017
▪ Deep learning – Modified U-Net
• End-to-end approach
• Train on co-registered image pairs in a segmentation CNN
• Deep convolutional encoder decoder architecture for object-based labelling
• Segment images with small changes in objects or regions
• Predict object-based labels from supervised learning
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Change Detection (cont’d)
39Energy-Aware Time Change Detection in SAR - Ranka (PI)
Preliminary Results of Change Detection on Simulated VSAR
DDDAS PI Meeting – 06 Sep 2017
Format: 512x512-pixel SAR video of simulated urban intersection
Noise (additive) = 0 = 0.001 to 0.008
A few False Positives (FPs) – We Are Developing Contextual Methods to Remove FPs
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Change Detection (cont’d)
40Energy-Aware Time Change Detection in SAR - Ranka (PI)
Preliminary Results of Change Detection on Simulated VSAR
DDDAS PI Meeting – 06 Sep 2017
Format: 256x256-pixel SAR video of simulated urban intersection
Noise (additive) = 0 = 0.001 to 0.008
Original image Optical Flow Approach Detection Result
Format: 512x512-pixel SAR video of simulated 5x5-block urban scene
Noise (additive) = 0 = 0.001 to 0.008
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Technical Approach – Change Detection (cont’d)
41Energy-Aware Time Change Detection in SAR - Ranka (PI)
Preliminary Results of Change Detection on Simulated VSAR
DDDAS PI Meeting – 06 Sep 2017
Format: 512x512-pixel SAR video of simulated 5x5-block urban scene
Noise (additive) = 0 = 0.001 to 0.008
Detection ResultsPartial TargetsOccluded TargetsFalse Positives
Resolved withContextual Data
Detection Result 512x512 VSAR Segmentation of Target Regions
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Accomplishments & Future Work
42Energy-Aware Time Change Detection in SAR - Ranka (PI)
Accomplishments• Success 1: Green Computing: 1/f power reduction in SAR Image Reconstruction
where f denotes resolution factor• Success 2: Fast, Scalable SAR Image Reconstruction on ORNL TITAN • Success 3: Accurate Models of CPU, GPU Energy and Power Consumption• Success 4: Computationally Efficient VSAR Simulation & Analysis• Success 5: Change Detection Successful on Noisy VSAR imagery
Future Technical Work• Enhance Performance (accuracy, speedup) of Multiresolution Backprojection• Extensive Modeling of Urban Scenes Library of VSAR Test Videos• Integration of Point Cloud Computation from Multiple Images (Transparent Sky LLC)
to Drive UF’s VSAR Simulation Capability• Improvement of Change Detection Algorithm – Multiresolution (faster), Increased
Tolerance of Noise and ClutterDDDAS PI Meeting – 06 Sep 2017
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Accomplishments (cont’d)
43Energy-Aware Time Change Detection in SAR - Ranka (PI)
Publications▪ Monograph
• Schmalz, M.S., W.H. Chapman, S. Ranka, S. Sahni, E. Hayden, U. Majumder, G. Seetharaman (submitted, in revision with added DDDAS chapter) Parallel Hierarchical Reconstruction and Change Detection of Synthetic Aperture Radar Imagery.
▪ Journal Papers
• Wijayasiri, A., T. Banerjee, S. Ranka, S. Sahni and M.S. Schmalz (submitted) “SAR Image Reconstruction in GPU Systems”.
• Seetharaman, G., E.T. Hayden, M.S. Schmalz, W.H. Chapman, S.Ranka, and S. Sahni (in re-submission) “Dynamic multistatic synthetic aperture radar (DMSAR) with image reconstruction algorithms and analysis”.
▪ Conference Paper
• A. Wijayasiri, T. Banerjee, S. Ranka, S. Sahni and M.S. Schmalz. “MultiobjectiveOptimization of SAR reconstruction on hybrid multicore systems”, to appear in Proceedings of HiPC 2017 Conference.
DDDAS PI Meeting – 06 Sep 2017
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Accomplishments (cont’d)
44Energy-Aware Time Change Detection in SAR - Ranka (PI)
Coordination / Synergy▪ With Other PIs• Dr. Steve Suddarth (Transparent Sky LLC) and Dr. K. Palaniappan (Univ. Missouri)
✓ Sharing UF’s Computational Optimization Technology✓ Developing Sharing Protocol for UF’s SAR / VSAR Reconstruction & Simulation Technology
▪ AFRL / DoD Efforts – Cross-Fertilization by Current AFRL Project• Computation of 3D Models with Low SWaP Architectures, Subcontract to Transparent Sky
LLC on Navy Phase 1 SBIR, 2017 (PI: M.S. Schmalz)• IPNAC – Intelligent Programming of New Architectures for Computing, Subcontract to
Transparent Sky LLC on DARPA Phase 1 SBIR, 2015 (PI: M.S. Schmalz).• Hierarchical Scalable Multi-Dimensional Indexing for High Performance Video
Search/Retrieval in Image Based Query Systems, US Air Force, 2014-2015 (PI: M.S. Schmalz)
▪ Data Coordination• Data Sharing of 3D Point Clouds among UF, Transparent Sky LLC, and Univ. MIssouri
DDDAS PI Meeting – 06 Sep 2017
Energy Aware Time Change Detection Using Synthetic Aperture Radar On High-Performance Heterogeneous Architectures: A DDDAS Approach
PI: Sanjay Ranka, Ph.D. -- University of Florida Department of CISE
Discussion
45Energy-Aware Time Change Detection in SAR - Ranka (PI)DDDAS PI Meeting – 06 Sep 2017