Stanford CS149 I 2023 I Lecture 9 - Distributed Data-Parallel Computing Using Spark

Producer-consumer locality, RDD abstraction, Spark implementation and scheduling
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Kayvon Fatahalian
Associate Professor of Computer Science, Stanford University
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Kunle Olukotun
Cadence Design Systems Professor, Professor of Electrical Engineering and of Computer Science, Stanford University
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0:00 - Overview of Distributed Computing with Spark
- Context from previous topics (ISPC, CUDA, thread-based programming)
- Introduction to distributed computing concepts
2:00 - Core Challenges in Distributed Computing
- Scaling to hundreds of thousands of cores
- Handling system faults and recovery
- Efficient memory usage considerations
2:30 - Motivation for Cluster Computing
- Processing large-scale data (hundreds of terabytes)
- Example: Website log processing across multiple nodes
- I/O bandwidth advantages of distributed systems
3:36 - Reliability Challenges in Large Clusters
- Mean time to failure statistics
- Need for fault-tolerant frameworks
- Programming model considerations
4:35 - Warehouse-Scale Computing Introduction
- Definition and infrastructure overview
- Luiz Barroso's contributions and concepts
- Book reference: "Datacenter as a Computer"
5:49 - Evolution of Cluster Architecture
- From commodity PCs to modern clusters
- Network performance improvements
- Comparison with supercomputers
8:04 - Cluster Organization and Structure
- Rack-based architecture
- Top of rack switches
- Server configurations and power constraints
9:53 - Individual Node Architecture
- Dual socket systems
- Memory and storage specifications
- Network connectivity details
11:05 - System Components and Bandwidth
- Memory bandwidth considerations
- Storage system characteristics
- Network communication speeds
14:33 - Inter-Node Communication Model
- Message passing concepts
- Send and receive operations
- Synchronization requirements
18:36 - Distributed File System Overview
- Google File System (GFS) and HDFS
- File chunking and replication
- Master node architecture
24:55 - Example: CS149 Website Log Analysis
- Distributed data processing scenario
- Block distribution across nodes
- Analysis requirements
30:35 - MapReduce Programming Model
- Mapper function design
- Reducer function implementation
- Key-value pair processing
34:31 - MapReduce Implementation Details
- Task distribution strategies
- Data locality optimization
- Load balancing approaches
46:50 - Fault Handling in MapReduce
- Node failure detection
- Task recovery mechanisms
- Handling slow machines
50:33 - MapReduce Limitations
- Programming model constraints
- Iterative algorithm challenges
- Performance considerations
54:48 - Memory vs. Storage Trade-offs
- Working set analysis
- Memory capacity considerations
- Performance implications
59:54 - Introduction to Spark
- In-memory processing benefits
- Fault tolerance requirements
- High-performance goals
1:01:41 - Resilient Distributed Datasets (RDDs)
- Core abstraction concepts
- Read-only collection properties
- Transformation operations
1:07:04 - Spark Transformations and Actions
- Available operations overview
- Programming model examples
- Action types and usage
1:10:24 - RDD Implementation Strategies
- Partitioning approaches
- Memory optimization techniques
- Dependency management
1:13:23 - RDD Optimization Techniques
- Narrow vs. wide dependencies
- Fusion opportunities
- Performance considerations
1:17:16 - Lecture Conclusion
- Summary of key concepts
- Preview of upcoming topics (cache coherency)
- Next lecture planning

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