SNIA-SDC23-Inupakutika-Lofton-Benchmarking-Storage-with-AI-Workloads_1.pdf

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1、1|2023 SNIA.All Rights Reserved.Virtual ConferenceSeptember 28-29,2021Benchmarking Storage with AI WorkloadsPresented byDevasena Inupakutika,Charles Lofton,Bridget Davis Samsung Semiconductor Inc.2|2023 SNIA.All Rights Reserved.MotivationGrowing production datasets:10s,100s of petabytesSamsungs data

2、center storage and memory productsResearch involving the impact of storage on AI/ML pipelines is limitedHow to showcase Samsung datacenter products impact to real world workloads?3|2023 SNIA.All Rights Reserved.Introduction Benchmarking essential to evaluating storage systems:Storage needs for large

3、 machine learning datasets are growing Evaluating storage for AI workloads is challenging Real-world AI training requires specialized hardware System resources stressed by AI application Do AI workloads benefit from high performance storage systems?Is there a realistic method to showcase high perfor

4、mance storage for AI workloads?Can the test methods be easily implemented and reproducible?4|2023 SNIA.All Rights Reserved.IntroductionBenchmark datasets are smaller whereas data is the moving force of AI algorithmsReal-world production workloads demands huge data(both for training and generation du

5、ring streaming)Empirical study to understand how AI workloads utilize storage devices through I/O patterns5|2023 SNIA.All Rights Reserved.AI Workloads I/O CharacterizationBetter understanding of AI I/O profilesProvides insights on the design and configuration of storage systemsMain aspects under con

6、sideration:I/O Rates Throughput Rates Randomness Locality of reference I/O size distribution%Reads vs Writes6|2023 SNIA.All Rights Reserved.Blocktrace Analysis of AI WorkloadsGives deeper insight into I/O profileThe block report generated by“btt”provides detail about each I/O:Command(read or write),