Partial Redundancy Elimination

Loops

JoeQ Framework

Dataflow Analysis

Constant Propagation

Register Allocation

Instruction Scheduling

Software Pipelining

Parallelization

Loop Transformations

Pipelined Parallelism

Pointer Analysis

BDDs in Pointer Analysis

Satisfiability Modulo Theories (SMT)

Garbage Collection

Natural Language Programming

CS 243: Program Analysis and Optimizations Stanford University Winter 2023 Focuses on efficient high-level programming through code optimization and program analysis for quality improvement. Also explores automatic memory management and natural language coding through machine learning. ## What do we teach?

- How do we make high-level programming languages efficient by optimizing the code? (Data-flow analysis)
- Machine learning and scientific computing need a lot of cycles; machines such as GPUs are complex to code. How do we automatically generate efficient code for these machines effectively. (Parallelism and Locality)
- Can we use program analysis to improve the quality of the code such as detecting security bugs in programs? (Pointer analysis, Satisfiability Modulo Theories)
- How do we automatically manage memory efficiently so users do not have to manage it themselves? (Garbage collection)
- The highest programming language is obviously natural language. With the recent introduction of large language models such as GPT3 and CodeX, how do we let consumers code in natural language using machine learning? Note: no prior knowledge in machine learning is needed. (Neural networks)

## How do you learn this?

As compilers are one of the most complex programs we write, by teaching compilers, we are also teaching software engineering. It is a course where programming and mathematics meet. We show how to apply general mathematical concepts (fixpoint computations, graph theory, linear algebra, binary decision diagrams, neural networks) to handle various complex programming problems. You will learn how to formulate solutions to problems, and not just memorizing prior work. ## Course Emphasis

- **Methodology: apply the methodology to other real life problems**
    - Problem statement
        - Which problem to solve?
    - Theory and Algorithm 
        -Theoretical frameworks 
        - Algorithms
    - Experimentation: Hands-on experience (Weekly programming/written homeworks)
- **Compiler knowledge:**
    - Non-goal: how to build a complete optimizing compiler
    - Important algorithms
    - Exposure to new ideas
    - Background to learn existing techniques CS 103 or CS 103B, and CS 107; Java programming language experience 

CS 243: Program Analysis and Optimizations

Compilers

Static Single-assignment (SSA)

Control-flow Graph (CFG)

Basic Blocks

Instruction Selection

Middle End

Dataflow Theory

Lexing

Parsing

Typechecking

Static Semantics

Calling Conventions

Dynamic Semantics

Mutable Store

struct (C programming language)

Locality

Alias Analysis

15-411 Compiler Design Carnegie Mellon University Fall 2020 Comprehensive study of compiler design and implementation, examining interaction between language design and runtime organization. Topics include program analysis, code generation, optimization, memory management. This course covers the design and implementation of compiler and runtime systems for high-level languages, and examines the interaction between language design, compiler design, and runtime organization. Topics covered include lexical and syntactic analysis, type-checking, program analysis, code generation and optimization, memory management, and runtime organization. - Distinguish the main phases of a state-of-the-art compiler
- Understand static and dynamic semantics of an imperative language
- Develop parsers and lexers using parser generators and combinators
- Perform semantic analysis
- Translate abstract syntax trees to intermediate representations and static single assignment form
- Analyze the dataflow in an imperative language
- Perform standard compiler optimizations
- Generate efficient assembly code for a modern architecture
- Allocate registers using a graph-coloring algorithm
- Understand opportunities and limitations of compiler optimizations
- Appreciate design tradeoffs how representation affects optimizations
- Automatically manage memory using garbage collection
- Develop complex software following high-level specifications  

15-411 Compiler Design