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Graph processing is a vital component of many AI and big data applications. However, due to its poor locality and complex data access patterns, graph processing is also a known performance killer of AI and big data applications. In this work, we ...

Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the ...

Graph analytics plays a significant role in various application domains. However, the performance of graph analytics is limited by the inefficiencies of the cache hierarchy. In recent years, plenty of works focus on eliminating the irregular data ...$\mathrm{}$$\mathrm{}$$\mathrm{}$

Previous graph analytics accelerators have achieved great improvement on throughput by alleviating irregular off-chip memory accesses. However, on-chip side datapath conflicts and design centralization have become the critical issues hindering further ...

Heterogeneous graph neural networks (HGNNs) deliver powerful capacity in heterogeneous graph representation learning. The execution of HGNNs is usually accelerated by GPUs. Therefore, characterizing and understanding the execution pattern of HGNNs on GPUs ...

Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs. To scale GNN training for large graphs, a widely adopted approach is distributed training which accelerates training ...

Concurrent file systems are pervasive but hard to correctly implement and formally verify due to nondeterministic interleavings. This paper presents AtomFS, the first formally-verified, fine-grained, concurrent file system, which provides linearizable ...