GPU-Accelerated Video Encoding - Nvidia

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ME – 2D-wave • A non-insulting way to resolve spatial data dependency • Splits processing into the list of groups of blocks, with possibility of parallel processing of blocks within any group. Groups have to be executed in order • The basic idea – to move by independent slices from the top-left corner to the down-right. TN stands for the group ID (and an iteration) • Inefficiency of CUDA implementation: – Low SMs load – Need work pool with producer\consumer roles, or several kernel launches – Irregular data access 59 PRESENTED BY
ME – 3D-wave • 3D-wave treats time as a 3 rd dimension and moves from the top-left corner of the first frame (cube origin) by groups of independent blocks • Useful for resolving spatio-temporal candidate set dependencies Frame 0 Frame 1 Frame 2 60 Legend Blocks with MV found Blocks to be processed (in parallel) PRESENTED BY
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GPU-Accelerated Video Encoding NVID
Page 3 and 4:
Outline • Motivation • Video en
Page 5 and 6:
Trends • GPU encoding is gaining
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Outline • Motivation • Video en
Page 9 and 10: Outline • Motivation • Video en
Page 11 and 12: Hybrid CPU-GPU encoding pipeline
Page 13 and 14: GPU Encoding Use Cases • Video en
Page 15 and 16: GPU Encoding Use Cases Helper libra
Page 17 and 18: Algorithm primitives • Fundamenta
Page 19 and 20: Reduce • Use half of N threads
Page 21 and 22: Reduce sample: 8 numbers 5 14 9 5 2
Page 23 and 24: Reduce sample: 8 numbers 33 14 19 5
Page 25 and 26: Scan sample: 8 numbers 2 9 5 1 3 6
Page 31 and 32: Compact • Having a vector of numb
Page 33 and 34: Compact sample The algorithm makes
Page 35 and 36: Algorithm primitives discussion •
Page 37 and 38: Intra Prediction 36 PRESENTED BY
Page 39 and 40: Intra Prediction • Select the rul
Page 41 and 42: Intra Prediction Plane Constant Ver
Page 43 and 44: CUDA: Intra Prediction 16x16 1. Loa
Page 45 and 46: CUDA: Intra Prediction 16x16 3. __d
Page 47 and 48: CUDA: Intra Prediction 16x16 4. Cal
Page 49 and 50: SAD and SATD • Sum of Absolute Tr
Page 51 and 52: Motion Estimation 50 PRESENTED BY
Page 53 and 54: Motion Estimation 52 PRESENTED BY
Page 55 and 56: Motion Estimation Motion vectors fi
Page 57 and 58: ME - Diamond search • Template me
Page 59: ME - Candidate Set • Objects that
Page 63 and 64: ME - GPU-friendly candidate set •
Page 65 and 66: ME - Hierarchical Search Algorithm:
Page 69 and 70: Tesla and Fermi architectures • A
Page 71 and 72: Multi-GPU encoding • N GOPs for N
Page 75 and 76: Recommended GTC talks We have a ple
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GPU-Accelerated Video Encoding - Nvidia

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