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MIT 6.172 Performance Engineering Fall 2018, coursework

course link
playlist with lectures on youtube

Instructors: Prof. Charles Leiserson, Prof. Julian Shun

Status

In progress

Lectures

1. Introduction and matrix multiplication

  • cache-efficiency
  • compiler flags
  • parallel algorithms of matrix multiplication
  • vectorization

2. Bentley Rules for optimizing work

  • data structures
  • logic
  • loops
  • functions

3. Bit Hacks

  • Bit operations and their applications

4. Assembly Language & Computer Architecture

  • Assembly language overview
  • Floating-point and vector hardware
  • Overview of computer architecture
  • Superscalar processing
  • Out-of-order execution
  • Branch prediction

5. C to Assembly

  • LLVM IR Primer
  • C to LLVM IR
  • LLVM IR to Assembly

6. Multicore Programming

  • Shared-memory hardware
  • Concurrency platforms

7. Races and Parallelism

  • Race conditions
  • Cilksan
  • Work and span analysis
  • Cilkscale
  • Scheduling

8. Analysis of Multithreaded Algorithms

  • Parallelization analysis
  • Cilk loop parallelism

9. What Compilers Can and Cannot Do

  • Compiler reports
  • Optimizing a scalar
  • Optimizing a structure
  • Optimizing function calls
  • Optimizing loops

10. Measurement and Timing

  • Timing variability
  • Ways to measure time
  • Interpretation of measurements

11. Storage Allocation

  • Stack and heap allocation
  • Garbage collection

12. Parallel Storage Allocation

  • Virtual memory allocation
  • Cactus stack
  • Parallel allocation strategies

13. The Cilk Runtime System

  • Internals of Cilk runtime system

14. Caching and Cache-Efficient Algorithms

  • Cache associativity
  • Cache-aware algorithms
  • Cache-oblivious algorithms

15. Cache-Oblivious Algorithms

  • Cache-oblivious stencil computations
  • Cache-oblivious sorting

16. Nondeterministic Parallel Programming

  • Nondeterministic parallel programming
  • Atomicity
  • Data races
  • Deadlock
  • Transactional memory

17. Synchronization Without Locks

  • Sequential consistency
  • Mutual exclusion without locks
  • Relaxed memory consistency
  • Instruction reordering
  • Compare-and-swap

18. Domain Specific Languages and Autotuning

  • GraphIt
  • Halide
  • OpenTuner

19. Leiserchess Codewalk

  • Project 4 overview

20. Speculative Parallelism & Leiserchess

  • Speculative parallelism
  • More project 4 insights

21. Tuning a TSP Algorithm

  • Review of recursive generation
  • The traveling salesperson problem
  • A sequence of TSP algorithms
  • Principles of algorithm engineering

22. Graph Optimization

  • Graph representations
  • Breadth-first search
  • Graph compression/reordering

23. High Performance in Dynamic Languages

  • Julia overview

Assignments

1. Basic Tools, C Primer

  • valgrind
  • asan
  • llvm-cov

2. Profiling

  • perf
  • cachegrind
  • inlining analysis
  • pointers vs arrays
  • efficient memory usage

3. Vectorization

  • vectorization in clang
  • sse and avx2 comparison
  • performance measure
  • vectorization cost