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We propose to reuse the SubCSR structure in Subway across a few iterations to significantly reduce the computational overhead, but without compromising the ...
... GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This article surveys the key issues of graph processing on GPUs, including.
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Reduce, Reuse, and Adapt: Accelerating Graph Processing on GPUs. Conference ... Accelerating large graph algorithms on the GPU using CUDA. Conference ...
Feb 26, 2024 · We extensively evaluate PyGim on a real-world PIM system with 1992 PIM cores using emerging GNN models, and demonstrate that it outperforms its ...
The current graph processing accelerators mainly opti- mize the memory accesses. Specifically, Graphicionado [23] reduces memory access latency and improves ...
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... Graphics processing units (GPUs) have become popular platforms for processing graph ... Reduce, Reuse, and Adapt: Accelerating Graph Processing on GPUs.
This work presents a GPU-optimized implementation for finding the connected components of a given graph, and tries to minimize the impact of irregularity, ...
Feb 26, 2024 · Optimized Backward Computation with Outer Product-Based SSpMM Kernel Design: This segment focuses on the acceleration of the computation pattern ...
To sequentially propagate new vertex states along the paths for large-scale graph processing, existing systems need high CPU-GPU data transfer cost and also ...
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A reading list for deep graph learning acceleration, including but not limited to related research on software and hardware level.