  Graph-specific computing with the support of dedicated accelerator has
greatly boosted the graph processing in both efficiency and energy.
Nevertheless, their data conflict management is still sequential in essential
when some vertex needs a large number of conflicting updates at the same time,
leading to prohibitive performance degradation. This is particularly true for
processing natural graphs.
  In this paper, we have the insight that the atomic operations for the vertex
updating of many graph algorithms (e.g., BFS, PageRank and WCC) are typically
incremental and simplex. This hence allows us to parallelize the conflicting
vertex updates in an accumulative manner. We architect a novel graphspecific
accelerator that can simultaneously process atomic vertex updates for massive
parallelism on the conflicting data access while ensuring the correctness. A
parallel accumulator is designed to remove the serialization in atomic
protection for conflicting vertex updates through merging their results in
parallel. Our implementation on Xilinx Virtex UltraScale+ XCVU9P with a wide
variety of typical graph algorithms shows that our accelerator achieves an
average throughput by 2.36 GTEPS as well as up to 3.14x performance speedup in
comparison with state-of-the-art ForeGraph (with single-chip version).
