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We present a new parallel algorithm for probabilistic graphical modeloptimization. The algorithm relies on data-parallel primitives (DPPs), whichprovide portable performance over hardware architecture. We evaluate results onCPUs and GPUs for an image segmentation problem. Compared to a serial baseline,we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compareour performance to a reference, OpenMP-based algorithm, and find speedups of upto 7X (CPU).

DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives