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NVIDIA GTC San Jose 2017

S7291 - Accelerating Semi-Global Block Matching for Stereo Image Processing Using CUDA

Session Speakers
Session Description

Real-time stereo matching is the need of many practical applications. Matching algorithms are required to perform at high speeds. We'll present a semi-global matching (SGBM) algorithm, which has several advantages. We'll present our hybrid implementation, which achieves around 23x performance over well known OpenCV implementations. We'll present a simplified approach to break problems into multiple modules and port suitable sections to CUDA and optimize sequential sections to the CPU itself. Our CUDA implementation is accelerated on a Tesla K20 card with Kepler architecture. We focused on basic CUDA performance optimizations like coalesced access pattern, collapsing of nested loops, reduction of iterative data transfers between CPU and GPU, etc. We'll present how with a simplified CPU/GPU hybrid programming approach we achieved 23 times faster performance.

Additional Session Information
Beginner
Talk
Video and Image Processing, HPC and Supercomputing
25 minutes
Session Schedule