Performance Engineering of Software Systems (Fall 2018) (M-I-T)

• Lecture 1: Introduction and Matrix Multiplication (M-I-T)
• Lecture 2: Bentley Rules for Optimizing Work (M-I-T)
• Lecture 3: Bit Hacks (M-I-T)
• Lecture 4: Assembly Language & Computer Architecture (M-I-T)
• Lecture 6: Multicore Programming (M-I-T)
• Lecture 7: Races and Parallelism (M-I-T)
• Lecture 8: Analysis of Multithreaded Algorithms (M-I-T)
• Lecture 9: What Compilers Can and Cannot Do (M-I-T)
• Lecture 10: Measurement and Timing (M-I-T)
• Lecture 11: Storage Allocation (M-I-T)
• Lecture 12: Parallel Storage Allocation (M-I-T)
• Lecture 13: The Cilk Runtime System (M-I-T)
• Lecture 14: Caching and Cache-Efficient Algorithms (M-I-T)
• Lecture 15: Cache-Oblivious Algorithms (M-I-T)
• Lecture 16: Nondeterministic Parallel Programming (M-I-T)
• Lecture 17: Synchronization Without Locks (M-I-T)
• Lecture 18: Domain Specific Languages and Autotuning (M-I-T)
• Lecture 19: Leiserchess Codewalk (M-I-T)
• Lecture 20: Speculative Parallelism & Leiserchess (M-I-T)
• Lecture 21: Tuning a TSP Algorithm (M-I-T)
• Lecture 22: Graph Optimization (M-I-T)
• Lecture 23: High Performance in Dynamic Languages (M-I-T)