FlexMiner: A Pattern-Aware Accelerator for Graph Pattern Mining

Graph pattern mining (GPM) is a class of algorithms widely used in many real-world applications in bio-medicine, e-commerce, security, social sciences, etc. GPM is a computationally intensive problem with an enormous amount of coarse-grain parallelism and therefore, attractive for hardware acceleration. Unfortunately, existing GPM accelerators have not used the best known algorithms and optimizations, and thus offer questionable benefits over software implementations.We present FlexMiner, a software/hardware co-designed GPM accelerator that improves the efficiency without compromising the generality or productivity of state-of-the-art software GPM frameworks. FlexMiner exploits massive amount of coarse-grain parallelism in GPM by deploying a large number of specialized processing elements. For efficient searches, the FlexMiner hardware accepts pattern-specific execution plans, which are generated automatically by the FlexMiner compiler from the given pattern(s). To avoid repetitive computation on neighborhood connectivity, we provide dedicated on-chip storage to memoize reusable connectivity information in a connectivity map (c-map ) which is implemented with low-cost yet high-throughput hardware. The on-chip memories in FlexMiner are managed dynamically using heuristics derived by the compiler, and thus are fully utilized. We have evaluated FlexMiner with 4 GPM applications on a wide range of real-world graphs. Our cycle-accurate simulation shows that FlexMiner with 64 PEs achieves 10.6× speedup on average over the state-of-the-art software system executing 20 threads on a 10-core Intel CPU.