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Hybrid CPU-GPU Implementation of Edge-Connected Jaccard Similarity in Graph Datasets

DescriptionTypical GPU programs consist of four steps: (1) data preparation, (2) host CPU-to-GPU data transfers, (3) execution of one or more GPU kernels, and (4) transfer of results back to CPU. While the kernel is running on the GPU, the CPU cores often remain idle, waiting on the GPU to finish kernel execution.

In recent years, several frameworks have been presented that perform automated distribution of workload to both CPU and GPU. While the aforementioned frameworks offer techniques for CPU+GPU workload distribution for regular applications, identifying a performant CPU+GPU workload distribution for irregular applications remains a difficult problem due to workload imbalance and irregular memory access patterns.

This work evaluates a hybrid CPU+GPU implementation of an irregular workload -- graph link prediction using the Jaccard similarity. For the graphs that benefit the most from our hybrid CPU-GPU approach, our implementation delivers a 16.4-28.4% improvement over the state-of-the-art Jaccard similarity implementation.

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