High-Performance Big Data Computing

✍ Scribed by Dhabaleswar K. Panda, Xiaoyi Lu, Dipti Shankar

• Publisher: MIT Press
• Year: 2022
• Tongue: English
• Leaves: 275
• Category: Library

✦ Table of Contents

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