  Temporal motif mining is the task of finding the occurrences of subgraph
patterns within a large input temporal graph that obey the specified structural
and temporal constraints. Despite its utility in several critical application
domains that demand high performance (e.g., detecting fraud in financial
transaction graphs), the performance of existing software is limited on
commercial hardware platforms, in that it runs for tens of hours. This paper
presents Everest - a system that efficiently maps the workload of mining
(supports both enumeration and counting) temporal motifs to the highly parallel
GPU architecture. In particular, using an input temporal graph and a more
expressive user-defined temporal motif query definition compared to prior
works, Everest generates an execution plan and runtime primitives that optimize
the workload execution by exploiting the high compute throughput of a GPU.
Everest generates motif-specific mining code to reduce long-latency memory
accesses and frequent thread divergence operations. Everest incorporates novel
low-cost runtime mechanisms to enable load balancing to improve GPU hardware
utilization. To support large graphs that do not fit on GPU memory, Everest
also supports multi-GPU execution by intelligently partitioning the edge list
that prevents inter-GPU communication. Everest hides the implementation
complexity of presented optimizations away from the targeted system user for
better usability. Our evaluation shows that, using proposed optimizations,
Everest improves the performance of a baseline GPU implementation by 19x, on
average.
