Garbage Collection

Loops

JoeQ Framework

Partial Redundancy Elimination

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)

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

Boolean

Function (computer programming)

Lambda

Regular Expressions

Intermediate Representations

Type Checking

Pattern Matching

Unary Operations

Binary Operations

Function Pointers

OCaml

S-Expression

Conditional (computer programming)

Handling Errors

First-Class Functions

Constant Folding

Compiler

Types

Correctness and Testing

Naming Expressions

Tail Call Optimization

Functional Programming (FP)

Custom Types

Pairs

Parsing

Optimization

CS 164: Programming Languages and Compilers UC Berkeley Fall 2022 Explores how compilers translate high-level languages into machine-understandable code, offering practical experience with developing compilers for various languages. Also covers reasoning about compiler correctness and understanding runtime errors.  Have you ever wondered why C programs seem to run faster than Python programs? Have you ever been confused by an error message and wondered why Java couldn’t understand your program? In CS 164, we’ll learn how compilers take in code in one language and produce code in another; in particular, we’ll learn how to translate high-level languages into code that your computer’s processor can understand. We’ll get hands-on practice developing compilers for a series of increasingly complex languages. Along the way, we’ll learn some general best practices for developing and testing complex software systems. After completing this course, students will be able to:

- Develop programs to translate between different programming languages
- More broadly, develop programs that operate on programs
- Explain the relationship between compilers and interpreters, and the differences between them
- Understand the tools they use to run programs—and the errors those tools produce
- Assign meaning to high-level language constructs
- Implement high-level language constructs
- Reason about what it means for a compiler to be correct
- Reason about undefined behavior
- Read and write assembly language
- Write a parser - [CS 61B. Data Structures](https://www2.eecs.berkeley.edu/Courses/CS61B/)
- [CS 61C. Machine Structures](https://www2.eecs.berkeley.edu/Courses/CS61C/)

### Course Culture

Students taking CS 164 come from a wide range of backgrounds. We hope to foster an inclusive and safe learning environment based on curiosity rather than competition. All members of the course community—the instructor, students, and GSIs—are expected to treat each other with courtesy and respect. Some of the responsibility for that lies with the staff, but a lot of it ultimately rests with you, the students. There is no textbook for the course; all readings will be provided digitally.

CS 164: Programming Languages and Compilers

Programming Languages

Waste collection

Garbage Collection

2 courses cover this concept

CS 243: Program Analysis and Optimizations

CS 164: Programming Languages and Compilers