High-Performance Big Data Computing

by Dhabaleswar K. Panda, Dipti Shankar, Xiaoyi Lu

Length: 272 pages
Edition: 1
Language: English
Publisher: The MIT Press
Publication Date: 2022-08-02
ISBN-10: 0262046857
ISBN-13: 9780262046855
Sales Rank: #8689798 (See Top 100 Books)

An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning.

Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions.

The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.

Cover
Title Page
Copyright
Table of Contents
Acknowledgments
1. Introduction
1.1. Overview
1.2. Big Data Characteristics and Trends
1.3. Current Systems for Data Management and Processing
1.4. Technological Trends
1.5. Convergence in HPC, Big Data, and Deep Learning
1.6. Outline of the Book
1.7. Summary
2. Parallel Programming Models and Systems
2.1. Overview
2.2. Batch Processing Frameworks
2.3. Stream Processing Frameworks
2.4. Query Processing Frameworks
2.5. Graph Processing Frameworks
2.6. Machine Learning and Deep Learning Frameworks
2.7. Interactive Big Data Tools
2.8. Monitoring and Diagnostics Tools
2.9. Summary
3. Parallel and Distributed Storage Systems
3.1. Overview
3.2. File Storage
3.3. Object Storage
3.4. Block Storage
3.5. Memory-Centric Storage
3.6. Monitoring and Diagnostics Tools
3.7. Summary
4. HPC Architectures and Trends
4.1. Overview
4.2. Computing Capabilities
4.3. Storage
4.4. Network Interconnects
4.5. Summary
5. Opportunities and Challenges in Accelerating Big Data Computing
5.1. Overview
5.2. C1: Computational Challenges
5.3. C2: Communication and Data Movement Challenges
5.4. C3: Memory and Storage Management Challenges
5.5. C4: Challenges of Codesigning Big Data Systems and Applications
5.6. C5: Challenges of Big Data Workload Characterization and Benchmarking
5.7. C6: Deployment and Management Challenges
5.8. Summary
6. Benchmarking Big Data Systems
6.1. Overview
6.2. Oﬄine Analytical Data Processing
6.3. Streaming Data Processing
6.4. Online Data Processing
6.5. Graph Data Processing
6.6. Machine Learning and Deep Learning Workloads
6.7. Comprehensive Benchmark Suites
6.8. Summary
7. Accelerations with RDMA
7.1. Overview
7.2. Batch and Stream Processing Systems
7.3. Graph Processing Systems
7.4. RPC Libraries
7.5. Query Processing in Databases
7.6. In-Memory KV Stores
7.7. HiBD Project
7.8. Case Studies and Performance Benefits
7.9. Summary
8. Accelerations with Multicore/Accelerator Technologies
8.1. Introduction
8.2. Multicore CPUs
8.3. GPU Acceleration for Big Data Computing
8.4. FPGAs and ASICs
8.5. Case Studies and Performance Benefits
8.6. Summary
9. Accelerations with High-Performance Storage Technologies
9.1. Overview
9.2. Exploring NVM-Centric Designs
9.3. Hybrid and Hierarchical Storage Middleware
9.4. Burst Buffer Systems
9.5. Case Studies and Performance Benefits
9.6. Summary
10. Deep Learning over Big Data
10.1. Overview
10.2. Convergence of Deep Learning, Big Data, and HPC
10.3. Challenges of Designing DLoBD Stacks
10.4. Distributed Deep Learning Training Basics
10.5. Overview of DLoBD Stacks
10.6. Characterization of DLoBD Stacks
10.7. Case Studies and Performance Benefits
10.8. Discussions on Optimizations for Deep Learning Workloads
10.9. Summary
11. Designs with Cloud Technologies
11.1. Overview
11.2. Overview of High-Performance Cloud Technologies
11.3. State-of-the-Art Designs
11.4. Case Studies and Performance Benefits
11.5. Summary
12. Frontier Research on High-Performance Big Data Computing
12.1. Heterogeneity-Aware Big Data Processing and Management Systems
12.2. Big Data Processing and Management for Hybrid Storage Systems
12.3. Efficient and Coherent Communication and Computation in Network for Big Data Systems
12.4. Summary
References
Index