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Home » Applied Sciences » Engineering » Electrical Engineering and Computer Science (M-I-T) » Performance Engineering of Software Systems (Fall 2018) (M-I-T) » Lecture 2: Bentley Rules for Optimizing Work (M-I-T)

Lecture 2: Bentley Rules for Optimizing Work (M-I-T)

Course:Performance Engineering of Software Systems (Fall 2018) (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. Dr. Julian Shun
Level:Undergraduate

Performance Engineering of Software Systems (Fall 2018) (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 1: Introduction and Matrix Multiplication (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)
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 5: C to Assembly (M-I-T)
Lecture 6: Multicore Programming (M-I-T)

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