Semi-Global Matching (SGM) is widely used for real-time stereo vision in the automotive context. Despite its popularity, only implementations using reconfigurable hardware (FPGA) or graphics hardware (GPU) achieve high enough frame rates for intelligent vehicles. Existing real-time implementations for general purpose PCs use image and disparity sub-sampling at the expense of matching quality. We study methods to improve the efficiency of SGM on general purpose PCs, through fine grained parallelization and usage of multiple cores. The different approaches are evaluated on the KITTI benchmark, which provides real imagery with LIDAR ground truth. The system is able to compute disparity maps of VGA image pairs with a disparity range of 128 values at more than 16 Hz. The approach is scalable to the number of available cores and portable to embedded processors.