6.172: Performance Engineering of Software Systems

Introduction

Purpose: Study software performance engineering
Challenge: Performance is not a primary concern in programming but is essential as a currency to trade for other properties (e.g., extensibility, correctness, security).
Historical Context: Initially crucial due to limited machine resources (1964-1977). Machine resources were extremely limited.
Modern Context: Multi-core processors are standard since clock rates plateaued around 2004. Performance today is mainly extracted through parallelism rather than raw clock speeds.

Performance as Currency: Used to buy other software properties.
Historical Quotes: Emphasizing pitfalls of premature optimization and focusing purely on efficiency can lead to bad coding practices.

Historical Machine Capabilities: From 524KB in 1964 to several MB and GB today.
Moore's Law: Enabled rapid performance increase until 2004 when clock speeds plateaued.
Dennard Scaling: Ceased to allow clock frequencies to scale due to power density issues.
Result in 2005: Introduction of multi-core processors to leverage parallel computing.

Impact on Software: Need for parallel programming and leveraging cache hierarchies, vector units, and more.
Future Job Relevance: Strong understanding of performance engineering is valuable for software development careers.

Basic Idea: Nornally straightforward; involves O(n^3) operations.
Machine Used: Haswell microarchitecture, 2.9GHz, 18 cores, etc.
**Initial Performance in Different Languages: Python, Java, and C. There were significant differences.

Python Slowness: Due to interpretation overhead.
Java Performance: Middling as a JIT compiler balances interpretation and compilation.
C Performance: Faster due to direct compilation to machine code.

Loop Order Impact: Significant effect on cache performance.
Compiler Optimization Flags: (-O0 to -O3 can have major effects).
Parallel Execution: Using parallel loops effectively speeds up computations.
Cache Management: Tiling or blocking to better utilize cache.
Vectorization: Utilizing vector units significantly boosts performance.
Divide-and-Conquer: Further improves performance due to efficient cache use.
Practices: Experiment with performance, monitor cache misses, use advanced compiler options and tools.

Hierarchy of Caches: Leveraging multi-level caches effectively can require complex solutions like 2-level or more tiling.
Efficiency in Real-World Scenarios: Not all code can be optimized to the same extent, but gaining expertise in optimizing for multi-core processors prepares one for various other performance challenges in computing.