Compiler construction lecture 01 .pptx.pdf
- 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
- 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