GPU 환경에서 머신러닝 워크로드의 효율적인 실행

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2023. 2. 전병곤.Machine learning (ML) workloads are becoming increasingly important in many types of real-world applications. We attribute this trend to the development of software systems for ML, which have facilitated the widespread adoption of heterogeneous accelerators such as GPUs. Todays ML software stack has made great improvements in terms of efficiency, however, not all use cases are well supported. In this dissertation, we study how to improve execution efficiency of ML workloads on GPUs from a software system perspective. We identify workloads where current systems for ML have inefficiencies in utilizing GPUs and devise new system techniques that handle those workloads efficiently. We first present Nimble, a ML execution engine equipped with carefully optimized GPU scheduling. The proposed scheduling techniques can be used to improve execution efficiency by up to 22.34×. Second, we propose Orca, an inference serving system specialized for Transformer-based generative models. By incorporating new scheduling and batching techniques, Orca significantly outperforms state-of-the-art systems – 36.9× throughput improvement at the same level of latency. The last topic of this dissertation is WindTunnel, a framework that translates classical ML pipelines into neural networks, providing GPU training capabilities for classical ML workloads. WindTunnel also allows joint training of pipeline components via backpropagation, resulting in improved accuracy over the original pipeline and neural network baselines.최근 경향을 보면 다양한 종류의 애플리케이션에서 머신 러닝(ML) 워크로드가 점 점 더 중요하게 활용되고 있다. 이는 ML용 시스템 소프트웨어의 개발을 통해 GPU 와 같은 이기종 가속기의 광범위한 활용이 가능해졌기 때문이다. 많은 연구자들의 관심 덕에 ML용 시스템 소프트웨어 스택은 분명 하루가 다르게 개선되고 있지만, 여전히 모든 사례에서 높은 효율성을 보여주지는 못한다. 이 학위논문에서는 시스 템 소프트웨어 관점에서 GPU 환경에서 ML 워크로드의 실행 효율성을 개선하는 방법을 연구한다. 구체적으로는 오늘날의 ML용 시스템이 GPU를 효율적으로 사 용하지 못하는 워크로드를 규명하고 더 나아가서 해당 워크로드를 효율적으로 처리할 수 있는 시스템 기술을 고안하는 것을 목표로 한다. 본 논문에서는 먼저 최적화된 GPU 스케줄링을 갖춘 ML 실행 엔진인 Nimble 을 소개한다. 새 스케줄링 기법을 통해 Nimble은 기존 대비 GPU 실행 효율성 을 최대 22.34배까지 향상시킬 수 있다. 둘째로 Transformer 기반의 생성 모델에 특화된 추론 서비스 시스템 Orca를 제안한다. 새로운 스케줄링 및 batching 기 술에 힘입어, Orca는 동일한 수준의 지연 시간을 기준으로 했을 때 기존 시스템 대비 36.9배 향상된 처리량을 보인다. 마지막으로 신경망을 사용하지 않는 고전 ML 파이프라인을 신경망으로 변환하는 프레임워크 WindTunnel을 소개한다. 이 를 통해 고전 ML 파이프라인 학습을 GPU를 사용해 진행할 수 있게 된다. 또한 WindTunnel은 gradient backpropagation을 통해 파이프라인의 여러 요소를 한 번에 공동으로 학습 할 수 있으며, 이를 통해 파이프라인의 정확도를 더 향상시킬 수 있음을 확인하였다.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Dissertation Overview 2 1.3 Previous Publications 4 1.4 Roadmap 5 Chapter 2 Background 6 2.1 ML Workloads 6 2.2 The GPU Execution Model 7 2.3 GPU Scheduling in ML Frameworks 8 2.4 Engine Scheduling in Inference Servers 10 2.5 Inference Procedure of Generative Models 11 Chapter 3 Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning 17 3.1 Introduction 17 3.2 Motivation 21 3.3 System Design 24 3.3.1 Ahead-of-time (AoT) Scheduling 25 3.3.2 Stream Assignment Algorithm 28 3.4 Evaluation 32 3.4.1 Inference Latency 36 3.4.2 Impact of Multi-stream Execution 36 3.4.3 Training Throughput 37 3.5 Summary 38 Chapter 4 Orca: A Distributed Serving System for Transformer-Based Generative Models 40 4.1 Introduction 40 4.2 Challenges and Proposed Solutions 44 4.3 Orca System Design 51 4.3.1 Distributed Architecture 51 4.3.2 Scheduling Algorithm 54 4.4 Implementation 60 4.5 Evaluation 61 4.5.1 Engine Microbenchmark 63 4.5.2 End-to-end Performance 66 4.6 Summary 71 Chapter 5 WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Model 72 5.1 Introduction 72 5.2 Pipeline Translation 78 5.2.1 Translating Arithmetic Operators 80 5.2.2 Translating Algorithmic Operators: GBDT 81 5.2.3 Translating Algorithmic Operators for Categorical Features 85 5.2.4 Fine-Tuning 87 5.3 Implementation 87 5.4 Experiments 88 5.4.1 Experimental Setup 89 5.4.2 Overall Performance 94 5.4.3 Ablation Study 95 5.5 Summary 98 Chapter 6 Related Work 99 Chapter 7 Conclusion 105 Bibliography 107 Appendix A Appendix: Nimble 131 A.1 Proofs on the Stream Assignment Algorithm of Nimble 131 A.1.1 Proof of Theorem 1 132 A.1.2 Proof of Theorem 2 134 A.1.3 Proof of Theorem 3 135 A.1.4 Time Complexity Analysis 137 A.2 Evaluation Results on Various GPUs 139 A.3 Evaluation Results on Different Training Batch Sizes 139박

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

NLP-Based Techniques for Cyber Threat Intelligence

In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is related to all the solutions inherent to data collection, processing, and analysis useful to understand a threat actor's targets and attack behavior. Currently, CTI is assuming an always more crucial role in identifying and mitigating threats and enabling proactive defense strategies. In this context, NLP, an artificial intelligence branch, has emerged as a powerful tool for enhancing threat intelligence capabilities. This survey paper provides a comprehensive overview of NLP-based techniques applied in the context of threat intelligence. It begins by describing the foundational definitions and principles of CTI as a major tool for safeguarding digital assets. It then undertakes a thorough examination of NLP-based techniques for CTI data crawling from Web sources, CTI data analysis, Relation Extraction from cybersecurity data, CTI sharing and collaboration, and security threats of CTI. Finally, the challenges and limitations of NLP in threat intelligence are exhaustively examined, including data quality issues and ethical considerations. This survey draws a complete framework and serves as a valuable resource for security professionals and researchers seeking to understand the state-of-the-art NLP-based threat intelligence techniques and their potential impact on cybersecurity

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Cyber Security and Critical Infrastructures 2nd Volume

The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond

Modeling Time-Series and Spatial Data for Recommendations and Other Applications

With the research directions described in this thesis, we seek to address the critical challenges in designing recommender systems that can understand the dynamics of continuous-time event sequences. We follow a ground-up approach, i.e., first, we address the problems that may arise due to the poor quality of CTES data being fed into a recommender system. Later, we handle the task of designing accurate recommender systems. To improve the quality of the CTES data, we address a fundamental problem of overcoming missing events in temporal sequences. Moreover, to provide accurate sequence modeling frameworks, we design solutions for points-of-interest recommendation, i.e., models that can handle spatial mobility data of users to various POI check-ins and recommend candidate locations for the next check-in. Lastly, we highlight that the capabilities of the proposed models can have applications beyond recommender systems, and we extend their abilities to design solutions for large-scale CTES retrieval and human activity prediction. A significant part of this thesis uses the idea of modeling the underlying distribution of CTES via neural marked temporal point processes (MTPP). Traditional MTPP models are stochastic processes that utilize a fixed formulation to capture the generative mechanism of a sequence of discrete events localized in continuous time. In contrast, neural MTPP combine the underlying ideas from the point process literature with modern deep learning architectures. The ability of deep-learning models as accurate function approximators has led to a significant gain in the predictive prowess of neural MTPP models. In this thesis, we utilize and present several neural network-based enhancements for the current MTPP frameworks for the aforementioned real-world applications.Comment: Ph.D. Thesis (2022

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems