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SURGE system optimizes GPU encoding for large-scale text embedding generation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SURGE, a new system designed to improve the efficiency of generating text embeddings on GPUs. SURGE addresses the bottleneck of processing numerous small data partitions by employing a streaming SuperBatch approach, which significantly reduces memory usage and speeds up the time to first output compared to traditional fixed-batch methods. The system has been deployed in production, handling over 800 million texts and demonstrating a 68x faster time-to-first-output with substantially lower memory requirements. AI

IMPACT This system could improve the efficiency and scalability of embedding generation pipelines for large-scale AI applications.

RANK_REASON The cluster contains an arXiv preprint detailing a new system for GPU encoding. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

SURGE system optimizes GPU encoding for large-scale text embedding generation

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The cluster contains an arXiv preprint detailing a new system for GPU encoding. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shashank Kapadia, Deep Narayan Mishra, Sujal Reddy Alugubelli, Ajay Kumar, Swapnil Yadav, Rishi Bhatia ·

    SURGE: SuperBatch Unified Resource-efficient GPU Encoding for Heterogeneous Partitioned Data

    arXiv:2605.01060v1 Announce Type: cross Abstract: We present SURGE, a streaming GPU encoding system deployed in production to generate embeddings for over 800 million texts across 40,000 logical partitions. Production embedding pipelines face a tension between logical data partit…

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