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GPUSparse system accelerates learned sparse retrieval using GPU parallelization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GPUSparse, a novel system designed to accelerate learned sparse retrieval models by leveraging GPU parallelization. This system addresses the CPU-bound bottleneck in current sparse retrieval methods, which hinders real-time performance. GPUSparse introduces a GPU-parallel inverted index, a batched scatter-add scoring algorithm, and fused Triton kernels to achieve significant speedups while maintaining high retrieval quality. AI

IMPACT This development could enable real-time serving of learned sparse retrieval models at scale, improving the performance of search and recommendation systems.

RANK_REASON The item describes a new system and its performance evaluation presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GPUSparse system accelerates learned sparse retrieval using GPU parallelization

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The item describes a new system and its performance evaluation presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Ashutosh Sharma ·

    GPUSparse: GPU-Accelerated Learned Sparse Retrieval with Parallel Inverted Indices

    Learned sparse retrieval models such as SPLADE achieve retrieval quality competitive with dense models while preserving the interpretability and exact-match advantages of sparse representations. However, inference-time scoring still relies on CPU-bound inverted index traversal al…

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