NDMiner: accelerating graph pattern mining using near data processing

Graph Pattern Mining (GPM) algorithms mine structural patterns in graphs. The performance of GPM workloads is bottlenecked by control flow and memory stalls. This is because of data-dependent branches used in set intersection and difference operations that dominate the execution time.

This paper first conducts a systematic GPM workload analysis and uncovers four new observations to inform the optimization effort. First, GPM workloads mostly fetch inputs of costly set operations from different memory banks. Second, to avoid redundant computation, modern GPM workloads employ symmetry breaking that discards several data reads, resulting in cache pollution and wasted DRAM bandwidth. Third, sparse pattern mining algorithms perform redundant memory reads and computations. Fourth, GPM workloads do not fully utilize the in-DRAM data parallelism.

Based on these observations, this paper presents NDMiner, a Near Data Processing (NDP) architecture that improves the performance of GPM workloads. To reduce in-memory data transfer of fetching data from different memory banks, NDMiner integrates compute units to offload set operations in the buffer chip of DRAM. To alleviate the wasted memory bandwidth caused by symmetry breaking, NDMiner integrates a load elision unit in hardware that detects the satisfiability of symmetry breaking constraints and terminates unnecessary loads. To optimize the performance of sparse pattern mining, NDMiner employs compiler optimizations and maps reduced reads and composite computation to NDP hardware that improves algorithmic efficiency of sparse GPM. Finally, NDMiner proposes a new graph remapping scheme in memory and