Compiler construction lecture 01 .pptx.pdf

Compiler construction lecture 01

1. Compiler Construction
Lecture 2

2. Compiler construction is a complex process that involves
multiple phases, from lexical analysis to code generation.
Various tools have been developed to simplify this process,
helping compiler developers build, debug, and optimize
compilers efficiently.

Why Use Compiler Construction Tools?
Automates repetitive tasks in compiler development.
Reduces errors and increases reliability.
Improves efficiency and speed of compiler design.
Provides support for different languages and platforms.

Compiler Construction
Tools

3. Compiler tools can be categorized based on the specific
phase they assist in:


Categories of Compiler
Construction Tools

4. 1. Lexical Analyzer Generators
These tools generate lexers (tokenizers) to break
source code into tokens.
Examples:
Lex: A widely used lexical analyzer generator for C.
Flex (Fast Lexical Analyzer): A faster alternative to
Lex with additional features.


Key Compiler Construction
Tools

5. 2. Syntax Analyzer Generators
These tools generate parsers based on grammar rules.
Examples:
Yacc (Yet Another Compiler Compiler): Creates
parsers for LALR(1) grammars.
Bison: GNU version of Yacc with additional features.
ANTLR (ANother Tool for Language Recognition):
A powerful tool for generating parsers.

Key Compiler Construction
Tools

6. 3. Semantic Analysis Tools
These tools assist in type checking, symbol table management, and semantic
rule enforcement.
Examples:
LLVM Clang Static Analyzer: Analyzes C, C++, and Objective-C code.
ROSE Compiler: Provides deep static analysis and transformation tools.
4. Intermediate Code Generators
These tools help in generating an intermediate representation (IR) of the source
code.
Examples:
LLVM (Low-Level Virtual Machine): Converts code into an intermediate
representation.
GCC GIMPLE: An intermediate representation used in the GNU Compiler
Collection.

Key Compiler Construction
Tools

7. 5. Code Optimization Tools
These tools optimize the intermediate representation for better
performance.
Examples:
LLVM Optimizer (opt): Performs various optimizations on LLVM IR.
GCC Optimizer (GCC -O2, -O3): Provides different optimization
levels.
6. Code Generation Tools
These tools generate machine code from the optimized intermediate
representation.
Examples:
LLVM Backend: Translates LLVM IR to assembly or machine code.
GCC Code Generator: Converts intermediate code to
platform-specific assembly.

Key Compiler Construction
Tools

8. 7. Debugging & Profiling Tools
These tools help in debugging and analyzing the
performance of compilers.
Examples:
GDB (GNU Debugger): Debugs compiled programs.
Valgrind: Detects memory leaks.
Perf: Measures CPU performance of compiled
programs.

Key Compiler Construction
Tools

9. A compiler can be built using a combination of these tools in the
following sequence:
Lexical Analysis: Use Flex to generate a lexer.
Syntax Analysis: Use Bison to generate a parser.
Semantic Analysis: Implement type checking manually or use
static analyzers.
Intermediate Code Generation: Convert the code into LLVM
IR.
Optimization: Apply LLVM Optimizer (opt) for performance
improvements.
Code Generation: Use LLVM Backend to generate machine
code.
Debugging and Profiling: Use GDB and Valgrind for testing.


Workflow of Compiler
Construction Using Tools

10. Comparison of Compiler
Construction Tools

11. What is a Domain-Specific Language (DSL)?
A Domain-Specific Language (DSL) is a
programming language designed for a specific domain
or problem rather than general-purpose programming.
Examples of DSLs:
SQL – for database queries.
HTML/CSS – for web design.
MATLAB – for mathematical computing.
Regular Expressions (RegEx) – for text pattern
matching.

Domain-Specific Languages
(DSL) and Their Compilers

12. Comparison: DSL vs
General-Purpose Languages
(GPLs)

13. External DSLs
Standalone languages with their own syntax and
grammar.
Require their own parsers and compilers.
Example: SQL, MATLAB, HTML.
Internal DSLs (Embedded DSLs)
Built within a general-purpose language.
Uses host language syntax and features.
Example: LINQ in C#.

Types of DSLs

14. Steps in DSL Compilation
Lexical Analysis
◦Converts input into tokens.
◦Uses tools like Lex/Flex.
◦Example: Breaking down SELECT * FROM users; into keywords (SELECT, FROM),
identifiers (users), and symbols (*, ;).
Parsing
◦Checks the syntax using grammar rules.
◦Uses tools like Yacc/Bison/ANTLR.
◦Example: Validating SELECT column FROM table;.
Semantic Analysis
◦Ensures meaning is correct (e.g., column exists in the table).
Intermediate Code Generation
◦Converts DSL into an intermediate representation (IR).
Code Optimization
◦Improves performance (e.g., SQL query optimization).
Code Generation
◦Produces final machine code, bytecode, or interpreted commands.

DSL Compiler Construction

15. ANTLR – Used for writing custom DSL parsers.
Lex & Yacc (Flex & Bison) – Traditional tools for
compiler construction.
Xtext – Java-based DSL development framework.
LLVM – For generating optimized machine code.


Tools for Building DSL
Compilers

16. In modern computing, compilers must handle increasingly
complex and large-scale programs efficiently.

Parallel and Distributed Compilers aim to speed up the
compilation process by distributing tasks across multiple
processors or computers.

Why Use Parallel and Distributed Compilation?
Faster compilation for large codebases.
Better resource utilization in multi-core and distributed
environments.
Improves productivity in software development.
Enables real-time optimizations in high-performance computing.

Parallel and Distributed
Compilers

17. What is Parallel Compilation?
Parallel compilation divides the compilation process
into multiple independent tasks that can be executed
simultaneously on multiple processors.
Parallelism in Compilation Phases



Parallel Compilation

18. File-level parallelism – Different source files are compiled
in parallel (e.g., gcc -j8 in multi-threaded mode).
Module-level parallelism – Different modules of the same
program are compiled separately.
Task parallelism – Different compiler tasks (lexing,
parsing, optimization) run in parallel.
Tools for Parallel Compilation
ccache – Caching compiled objects to speed up
recompilation.
distcc – Distributes compilation tasks across multiple
machines.
LLVM – Supports multi-threaded compilation.


Techniques for Parallel
Compilation

19. What is Distributed Compilation?
Distributed compilation spreads the compilation workload
across multiple networked machines to speed up the
process and reduce the load on a single machine.
How Does Distributed Compilation Work?
Source Code Partitioning – The compiler divides the
source code into independent compilation units.
Task Distribution – The system assigns compilation tasks
to different machines in a cluster.
Compilation Execution – Each machine compiles its
assigned portion of the code.
Result Merging – The compiled object files are linked
together to produce the final executable.

Distributed Compilation

20. Improved Speed – Large projects compile much faster.
Scalability – Can utilize multiple machines for better
efficiency.
Resource Optimization – Uses idle machines in a
network to assist in compilation.


Advantages of Distributed
Compilation

21. Tools for Distributed
Compilation

22. Synchronization Issues
Ensuring dependencies are resolved correctly when
compiling in parallel.
Load Balancing
Efficiently distributing compilation tasks across processors
or networked machines.
Network Latency
In distributed compilation, slow network speeds can reduce
performance gains.
Fault Tolerance
If a machine crashes during distributed compilation, the
system should handle failures gracefully.


Challenges in Parallel and
Distributed Compilation

23. Large-Scale Software Development – Google,
Microsoft, and other tech companies use distributed
compilation for massive codebases.
Embedded Systems – Cross-compiling for different
architectures efficiently.
Game Development – Optimizing shader and asset
compilation for high-performance gaming.
Cloud Computing – Cloud-based distributed
compilers for mobile and web applications.


Applications of Parallel and
Distributed Compilation