Lecture 4: Computer Architecture and Performance Engineering

Instructor: Saman Amarasinghe, John Dong

View the complete course: ocw.mit.edu/6-172F10

License: Creative Commons BY-NC-SA
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1. Matrix Multiply: A Case Study
2. Bit Hacks
3. Basic Performance Engineering
4. Computer Architecture and Performance Engineering
5. Performance Engineering with Profiling Tools
6. C to Assembler
7. Memory Systems and Performance Engineering
8. Cache-Efficient Algorithms
9. Cache-Efficient Algorithms II
10. Dynamic Storage Allocation
11. What Compilers Can and Cannot Do
12. Multicore Programming
13. Parallelism and Performance
14. Analysis of Multithreaded Algorithms
15. Nondeterministic Programming
16. Synchronizing without Locks
17. Performance Issues in Parallelization
18. Primer on Ray Tracing Techniques
19. How TokuDB Fractal Tree Indexes Work
20. Distributed Systems
21. Quiz 2 Review
22. A Tale of 10 Bugs: Performance Engineering at Vmware
23. Final Competition and Student Feedback
24. BONUS: Industry Mentor Overview

This class is a hands-on, project-based introduction to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, cache and memory hierarchy optimization, parallel programming, and building scalable distributed systems.

Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard practice.