On the Acceleration of Shortest Path Calculations in Transportation Networks

Shortest path algorithms are key elements of many graph problems. They are used in such applications as online direction finding and navigation, and modeling of traffic for large scale simulations of major metropolitan areas. As shortest path algorithm are execution bottlenecks, it is beneficial to move their execution to parallel hardware such as field programmable gate arrays (FPGAs). One of the innovations of this approach is the use of a small bubble sort core to produce the extract-min function. While bubble sort is not usually considered an appropriate algorithm for any non-trivial usage, it is appropriate in this case as it can produce a single minimum out of the list in O(n) cycles, where n is the number of elements in the vertex list. The cost of this min operation does not impact the running time of the architecture, because the queue depth for fetching the next set of edges from memory is roughly equivalent to the number of cores in the system. Additionally, this work provides a collection of simulation results that model the behavior of the node queue in hardware. The results show that a hardware queue, implementing a small bubble-type minimum function, need only be on the order of 16 elements to provide both correct and optimal paths. With support for a large DRAM graph store with SRAM-based caching on a Cray XD-1 FPGA-accelerated system, the system provides a speedup of roughly 50x over the CPU-based implementation.