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LOCATION:DEF Concourse
DTSTART;TZID=America/Denver:20231115T100000
DTEND;TZID=America/Denver:20231115T170000
UID:submissions.supercomputing.org_SC23_sess303_rpost221@linklings.com
SUMMARY:Hybrid CPU-GPU Implementation of Edge-Connected Jaccard Similarity
  in Graph Datasets
DESCRIPTION:Atharva Gondhalekar, Paul Sathre, and Wu-chun Feng (Virginia T
 ech)\n\nTypical GPU programs consist of four steps: (1) data preparation, 
 (2) host CPU-to-GPU data transfers, (3) execution of one or more GPU kerne
 ls, and (4) transfer of results back to CPU. While the kernel is running o
 n the GPU, the CPU cores often remain idle, waiting on the GPU to finish  
 kernel execution.\n\nIn recent years, several frameworks have been present
 ed that perform automated distribution of workload to both CPU and GPU. Wh
 ile the aforementioned frameworks offer techniques for CPU+GPU workload di
 stribution for regular applications, identifying a performant CPU+GPU work
 load distribution for irregular applications remains a difficult problem d
 ue to workload imbalance and irregular memory access patterns.\n\nThis wor
 k evaluates a hybrid CPU+GPU implementation of an irregular workload -- gr
 aph link prediction using the Jaccard similarity.  For the graphs that ben
 efit the most from our hybrid CPU-GPU approach, our implementation deliver
 s a 16.4-28.4% improvement over the state-of-the-art Jaccard similarity im
 plementation.\n\nTag: Artificial Intelligence/Machine Learning, Architectu
 re and Networks, Heterogeneous Computing, I/O and File Systems, Performanc
 e Measurement, Modeling, and Tools, Post-Moore Computing, Programming Fram
 eworks and System Software, Quantum Computing\n\nRegistration Category: Te
 ch Program Reg Pass, Exhibits Reg Pass\n\n
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