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LayoutBench benchmark evaluates cloud storage for multimedia ML workloads

By PulseAugur Editorial · [1 sources] ·

A new benchmark called LayoutBench has been developed to evaluate the performance of different cloud storage layouts for multimedia machine learning workloads. The benchmark compares three strategies: individual objects (L1), sequential tar archives (L2), and columnar Parquet files (L3). Experiments on ImageNet using AWS S3 and EC2 instances show that L2 offers lower latency through connection reuse, while L3 is fastest for very large retrievals but transfers significantly more data and requires more memory. Data transfer costs were found to be the dominant expense across all layouts. AI

IMPACT Provides insights into optimizing data retrieval costs and performance for large-scale multimedia machine learning datasets in cloud environments.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating cloud storage layouts for multimedia data. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

LayoutBench benchmark evaluates cloud storage for multimedia ML workloads

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The cluster contains a research paper introducing a new benchmark for evaluating cloud storage layouts for multimedia data. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Debopam Sanyal, Hongjie Chen, Alexey Tumanov, Joshua Kimball ·

    LayoutBench: Performance Benchmarking of Cloud Storage Layouts for Multimedia Data

    arXiv:2607.28880v1 Announce Type: cross Abstract: Modern multimedia machine learning workloads increasingly store large-scale datasets in cloud object storage services such as AWS S3. How these samples are physically organized in storage (i.e.,storage layout) directly affects how…

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