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Research Topics

Research Topics. CSC 3990. Parallel Computing & Compilers. CSC 3990. What is a Compiler?. Compiler Converts source code into machine code Automatic Relieve programmer from having to know about machine (processor). What is a Parallel Compiler?. Parallel Compiler

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Research Topics

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  1. Research Topics CSC 3990

  2. Parallel Computing & Compilers CSC 3990

  3. What is a Compiler? • Compiler • Converts source code into machine code • Automatic • Relieve programmer from having to know about machine (processor)

  4. What is a Parallel Compiler? • Parallel Compiler • Converts source code into machine code to run on a parallel computer • Centralized shared memory computer or supercomputer • Distributed computer • Anything where a single program will run on more than one processor

  5. compiler front-end intermediate code loop optimization register allocation code generation code scheduling machine code Compiler Structure source code

  6. Intermediate-code Generator Lexical Analyzer (Scanner) Non-optimized Intermediate Code Tokens Intermediate-code Optimizer Syntax Analyzer (Parser) Optimized Intermediate Code Parsetree Target-code Generator Semantic Analyzer Abstract Syntax Tree w/ Attributes Target machine code Phases of a Compiler Source program

  7. Source code Front end Machine requirements analysis Machine description generation Back end Processor generator Executable code processor Dynamic profiler Nanocompiler – an initial vision • Machine description generated from IR • Processor generated from machine description • Executable runs on generated processor • Dynamic profiler feeds back to analyzer • Processor reconfigured at run-time

  8. Example: Loop Unrolling • Loops are popular places for identifying “parallelism” • Can separate iterations of the same loop execute at the same time? • If so, how can the code be modified… automatically… to make that happen? for (i=0; i<100; i++) A[i] = B[i] * C[i];

  9. Natural Language Processing CSC 3990

  10. What is NLP? • Natural Language Processing (NLP) • Computers use (analyze, understand, generate) natural language • A somewhat applied field • Computational Linguistics (CL) • Computational aspects of the human language faculty • More theoretical

  11. Why Study NLP? • Human language interesting & challenging • NLP offers insights into language • Language is the medium of the web • Interdisciplinary: Ling, CS, psych, math • Help in communication • With computers (ASR, TTS) • With other humans (MT) • Ambitious yet practical

  12. Goals of NLP • Scientific Goal • Identify the computational machinery needed for an agent to exhibit various forms of linguistic behavior • Engineering Goal • Design, implement, and test systems that process natural languages for practical applications

  13. Applications • speech processing: get flight information or book a hotel over the phone • information extraction: discover names of people and events they participate in, from a document • machine translation: translate a document from one human language into another • question answering: find answers to natural language questions in a text collection or database • summarization: generate a short biography of Noam Chomsky from one or more news articles

  14. General Themes • Ambiguity of Language • Language as a formal system • Rule-based vs. Statistical Methods • The need for efficiency

  15. Topic Ideas • Textual Analysis – readability • Plagiarism Detection – candidate selection • Intelligent Agents – machine interaction

  16. Textual Analysis - Readability • Text Input • Analyze text & estimate “readability” • Grade level of writing • Consistency of writing • Appropriateness for certain educ. level • Output results • Research question: How can computer analyze text and measure readability? • Opportunities for hands-out research

  17. Plagiarism Detection • Text Input • Analyze text & locate “candidates” • Find one or more passages that might be plagiarized • Algorithm tries to do what a teacher does • Search on Internet for candidate matches • Output results • Research question: What algorithms work like humans when finding plagiarism? • Experimental CS research

  18. Intelligent Agents • Example: ELIZA • AIML: Artificial Intelligence Modeling Lang. • Human types something • Computer parses, “understands”, and generates response • Response is viewed by human • Research question: How can computers “understand” and “generate” human writing? • Also good area for experimentation

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