Parallel Implementation of Real-Time Semi-Global Matching on Embedded Multi-Core Architectures

Embedded real-time algorithms are often realized with dedicated hardware, exhibiting high production costs and low programming flexibility thereafter. For instance, semi-global matching for stereo image processing, including complex data flows, traditionally runs on customized hardware modules. Combining the processing and memory capabilities of multiple individual cores, emerging embedded multi-core technologies address these problems. However, considering concurrency issues (e. g., data races and lock contentions), parallel programming requires experienced programmers and technology-specific techniques (e. g., synchronization libraries) and tools (e. g., parallel profilers), which are often not available on embedded platforms. In this work, we introduce a parallel version of a semi-global matching algorithm and demonstrate within this case study runtime optimizations necessary to meet real-time requirements. We also show structured steps of the applied parallelization workflow, illustrating an efficient migration strategy to multi-core platforms using runtime information (e. g., profiles and hardware counters). Finally, to evaluate the resulting performance characteristics, we compare the runtime behavior of the parallel version running on a Freescale P4080 processor with reference values taken on an Intel i7, a field-programmable logic device, an extended general purpose processor and a GPU.