Presentation
Accelerating Clustering Algorithms for Large-Scale Datasets via Collaborative GPU and CXL-Memory Architecture
DescriptionThis paper addresses the challenges in accelerating clustering algorithms for large-scale datasets, emphasizing memory capacity and bandwidth limitations. By introducing a GPU and CXL-Memory collaborative architecture, we enhance the efficiency of Approximate Nearest Neighbor (ANN) searches. Leveraging the recent CXL protocol advancements, this architecture splits compute-intensive tasks for the GPU and memory-intensive processes for the CXL memory expander. The design incorporates near-data processing (NDP) units within each CXL memory rank to handle local computations, reducing memory bottlenecks and improving throughput without the need for multi-GPU configurations. Experimental results demonstrate significant performance gains in ANN tasks, achieving cost-effective and scalable solutions for billion-scale datasets.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby
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