Block-Parallel IDA* for GPUs

Proceedings of the International Symposium on Combinatorial Search, 10

Volume

Vol. 10 No. 1 (2017): Tenth Annual Symposium on Combinatorial Search

Short Papers

We investigate GPU-based parallelization of Iterative-Deepening A* (IDA*). We show that straightforward thread-based parallelization techniques which were previously proposed for massively parallel SIMD processors perform poorly due to warp divergence and load imbalance. We propose Block-Parallel IDA* (BPIDA*), which assigns the search of a subtree to a block (a group of threads with access to fast shared memory) rather than a thread. On the 15-puzzle, BPIDA* on a NVIDIA GRID K520 with 1536 CUDA cores achieves a speedup of 4.98 compared to a highly optimized sequential IDA* implementation on a Xeon E5-2670 core.

10.1609/socs.v8i1.18440