Graph Cuts with CUDA

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Naive <strong>CUDA</strong> Implementation • Both kernels are memory-bound • Observations on the naive implementation – Push: Atomic memory bandwidth is lower – Relabel: 1-bit per edge would be sufficient Addressing these bottlenecks improves overall performance
Push, improved • Idea: – Work on tiles in shared memory • Share data between threads of a block – Each thread updates M pixels • Push first M times in first edge direction • Then M times in next edge direction
Page 1 and 2: Graph Cuts with CUDA San Jose| 02/1
Page 3 and 4: Problems solvable with Graphcuts St
Page 5 and 6: Example: Binary Segmentation Proble
Page 7 and 8: Maximum Flow = Minimum Cut Source P
Page 9 and 10: Graph Cut Solution Input Result
Page 11 and 12: Push-Relabel in a nutshell • Some
Page 13 and 14: Relabel Pseudocode void relabel(x,
Page 15 and 16: Graph setup Excess Flow Height 3 So
Page 17 and 18: Initialized HEIGHT_MAX = 5 Source 3
Page 19 and 20: After Push Source 6/0 3/3 0/8 1/1 -
Page 21 and 22: After Relabel Source 6/0 3/3 0/6 1/
Page 23 and 24: After 3 more Iterations, Terminated
Page 25 and 26: Graph Cut and Solution 3 Source 9 5
Page 27 and 28: CUDA Implementation • Datastructu
Page 29 and 30: Relabel Data Access Patterns • Re
Page 31: Naive CUDA Implementation • Itera
Page 35 and 36: Wave Push Active Thread Push direct
Page 37 and 38: Wave Push Active Thread Push direct
Page 39 and 40: Wave Push • After tile pushing, b
Page 41 and 42: Relabel 5 0 3 9 0 0 1 7 • Compres
Page 43 and 44: Tile based push-relabel 80% 70% 60%
Page 45 and 46: Tile based push-relabel • Repeat
Page 47 and 48: Global Relabel • BFS from sink -
Page 49 and 50: Global Relabel • Initialize all p
Page 51 and 52: Results • Comparison between Boyk
Page 53 and 54: Example Application: GrabCut
Page 55: Summary • Introduction to Graph C

Graph Cuts with CUDA

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