Memory-Efficient GPU Acceleration for Recommendation Systems with Large-Scale Top-K Retrieval

Abstract

Large-scale recommender systems increasingly depend on accelerated retrieval pipelines to convert massive candidate sets into small ranked lists under strict latency and cost constraints. The central systems challenge is not merely scoring items but performing top-K retrieval over hundreds of millions or billions of embeddings while respecting limited GPU memory, bandwidth, and energy budgets. This paper examines memory-efficient GPU acceleration for recommendation systems with large-scale top-K retrieval. It argues that retrieval performance must be understood as a socio-technical property shaped by algorithmic choices, hardware architecture, deployment topology, governance policies, and sustainability commitments. The discussion first reviews the evolution from classical recommendation models to deep and retrieval-oriented architectures, then analyzes memory bottlenecks such as embedding tables, candidate indexes, intermediate score buffers, and kernel launch overheads. It explores compression, quantization, graph-based indexing, row-wise selection, multi-stage retrieval, and heterogeneous scheduling as mutually dependent design levers. The paper then connects these technical choices to infrastructure governance, fairness, robustness, and environmental accountability. It suggests that memory-efficient top-K acceleration is not a single optimization but a negotiated balance among recall, latency, fairness, reproducibility, and operational resilience. Evaluation practices must therefore include end-to-end system metrics, tail-latency behavior, workload skew, and drift-aware monitoring. The conclusion emphasizes that future recommendation platforms require co-designed retrieval kernels, memory hierarchies, policy controls, and sustainable deployment strategies rather than isolated GPU micro-optimizations.