TPCx-AI -人工智能和机器学习系统的行业标准基准

IF 2.6 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611554
Christoph Brücke, Philipp Härtling, Rodrigo D Escobar Palacios, Hamesh Patel, Tilmann Rabl
{"title":"TPCx-AI -人工智能和机器学习系统的行业标准基准","authors":"Christoph Brücke, Philipp Härtling, Rodrigo D Escobar Palacios, Hamesh Patel, Tilmann Rabl","doi":"10.14778/3611540.3611554","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (AI) and machine learning (ML) techniques have existed for years, but new hardware trends and advances in model training and inference have radically improved their performance. With an ever increasing amount of algorithms, systems, and hardware solutions, it is challenging to identify good deployments even for experts. Researchers and industry experts have observed this challenge and have created several benchmark suites for AI and ML applications and systems. While they are helpful in comparing several aspects of AI applications, none of the existing benchmarks measures end-to-end performance of ML deployments. Many have been rigorously developed in collaboration between academia and industry, but no existing benchmark is standardized. In this paper, we introduce the TPC Express Benchmark for Artificial Intelligence (TPCx-AI), the first industry standard benchmark for end-to-end machine learning deployments. TPCx-AI is the first AI benchmark that represents the pipelines typically found in common ML and AI workloads. TPCx-AI provides a full software kit, which includes data generator, driver, and two full workload implementations, one based on Python libraries and one based on Apache Spark. We describe the complete benchmark and show benchmark results for various scale factors. TPCx-AI's core contributions are a novel unified data set covering structured and unstructured data; a fully scalable data generator that can generate realistic data from GB up to PB scale; and a diverse and representative workload using different data types and algorithms, covering a wide range of aspects of real ML workloads such as data integration, data processing, training, and inference.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"7 1","pages":"0"},"PeriodicalIF":2.6000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"TPCx-AI - An Industry Standard Benchmark for Artificial Intelligence and Machine Learning Systems\",\"authors\":\"Christoph Brücke, Philipp Härtling, Rodrigo D Escobar Palacios, Hamesh Patel, Tilmann Rabl\",\"doi\":\"10.14778/3611540.3611554\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial intelligence (AI) and machine learning (ML) techniques have existed for years, but new hardware trends and advances in model training and inference have radically improved their performance. With an ever increasing amount of algorithms, systems, and hardware solutions, it is challenging to identify good deployments even for experts. Researchers and industry experts have observed this challenge and have created several benchmark suites for AI and ML applications and systems. While they are helpful in comparing several aspects of AI applications, none of the existing benchmarks measures end-to-end performance of ML deployments. Many have been rigorously developed in collaboration between academia and industry, but no existing benchmark is standardized. In this paper, we introduce the TPC Express Benchmark for Artificial Intelligence (TPCx-AI), the first industry standard benchmark for end-to-end machine learning deployments. TPCx-AI is the first AI benchmark that represents the pipelines typically found in common ML and AI workloads. TPCx-AI provides a full software kit, which includes data generator, driver, and two full workload implementations, one based on Python libraries and one based on Apache Spark. We describe the complete benchmark and show benchmark results for various scale factors. TPCx-AI's core contributions are a novel unified data set covering structured and unstructured data; a fully scalable data generator that can generate realistic data from GB up to PB scale; and a diverse and representative workload using different data types and algorithms, covering a wide range of aspects of real ML workloads such as data integration, data processing, training, and inference.\",\"PeriodicalId\":54220,\"journal\":{\"name\":\"Proceedings of the Vldb Endowment\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Vldb Endowment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14778/3611540.3611554\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Vldb Endowment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14778/3611540.3611554","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 1

摘要

人工智能(AI)和机器学习(ML)技术已经存在多年,但新的硬件趋势和模型训练和推理方面的进步从根本上提高了它们的性能。随着算法、系统和硬件解决方案的数量不断增加,即使是专家也很难确定好的部署。研究人员和行业专家已经观察到这一挑战,并为人工智能和机器学习应用程序和系统创建了几个基准套件。虽然它们有助于比较人工智能应用程序的几个方面,但现有的基准测试都无法衡量机器学习部署的端到端性能。许多是在学术界和工业界的合作下严格开发的,但没有现有的标准是标准化的。在本文中,我们介绍了TPC快速人工智能基准(TPCx-AI),这是端到端机器学习部署的第一个行业标准基准。TPCx-AI是第一个AI基准,它代表了常见ML和AI工作负载中常见的管道。TPCx-AI提供了一个完整的软件包,其中包括数据生成器、驱动程序和两个完整的工作负载实现,一个基于Python库,另一个基于Apache Spark。我们描述了完整的基准测试,并展示了各种规模因素的基准测试结果。TPCx-AI的核心贡献是一个涵盖结构化和非结构化数据的全新统一数据集;一个完全可扩展的数据生成器,可以生成从GB到PB规模的真实数据;以及使用不同数据类型和算法的多样化和代表性工作负载,涵盖了实际ML工作负载的广泛方面,如数据集成,数据处理,训练和推理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
TPCx-AI - An Industry Standard Benchmark for Artificial Intelligence and Machine Learning Systems
Artificial intelligence (AI) and machine learning (ML) techniques have existed for years, but new hardware trends and advances in model training and inference have radically improved their performance. With an ever increasing amount of algorithms, systems, and hardware solutions, it is challenging to identify good deployments even for experts. Researchers and industry experts have observed this challenge and have created several benchmark suites for AI and ML applications and systems. While they are helpful in comparing several aspects of AI applications, none of the existing benchmarks measures end-to-end performance of ML deployments. Many have been rigorously developed in collaboration between academia and industry, but no existing benchmark is standardized. In this paper, we introduce the TPC Express Benchmark for Artificial Intelligence (TPCx-AI), the first industry standard benchmark for end-to-end machine learning deployments. TPCx-AI is the first AI benchmark that represents the pipelines typically found in common ML and AI workloads. TPCx-AI provides a full software kit, which includes data generator, driver, and two full workload implementations, one based on Python libraries and one based on Apache Spark. We describe the complete benchmark and show benchmark results for various scale factors. TPCx-AI's core contributions are a novel unified data set covering structured and unstructured data; a fully scalable data generator that can generate realistic data from GB up to PB scale; and a diverse and representative workload using different data types and algorithms, covering a wide range of aspects of real ML workloads such as data integration, data processing, training, and inference.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Proceedings of the Vldb Endowment
Proceedings of the Vldb Endowment Computer Science-General Computer Science
CiteScore
7.70
自引率
0.00%
发文量
95
期刊介绍: The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.
期刊最新文献
Auditory Brainstem Response in a Child with Mitochondrial Disorder-Leigh Syndrome. Breathing New Life into an Old Tree: Resolving Logging Dilemma of B + -tree on Modern Computational Storage Drives QO-Insight: Inspecting Steered Query Optimizers A Learned Query Rewrite System Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1