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Found 4 papers, 2 papers with code
NeuraChip: Accelerating GNN Computations with a Hash-based Decoupled Spatial Accelerator
We introduce a rolling eviction strategy to mitigate data idling in on-chip memory as well as address the prevalent issue of memory bloat in sparse graph computations.
Enabling Accelerators for Graph Computing
Furthermore, we extend our exploration to emerging GNN workloads in the domain of graph neural networks.
MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training
In this paper, we present MaxK-GNN, an advanced high-performance GPU training system integrating algorithm and system innovation.
SMASH: Sparse Matrix Atomic Scratchpad Hashing
One approach to tackle this problem is to use an inner product method for the SpGEMM kernel implementation.
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