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GRE Computer Science Syllabus 2016

Here you will find GRE Computer Science Syllabus 2018.

GRE Syllabus of Computer Science:-


I. SOFTWARE SYSTEMS AND METHODOLOGY — 40%

A. Data organization

    * Data types
    * Data structures and implementation techniques

B. Program control and structure

    * Iteration and recursion
    * Procedures, functions, methods and exception handlers
    * Concurrency, communication and synchronization

C. Programming languages and notation

    * Constructs for data organization and program control
    * Scope, binding and parameter passing
    * Expression evaluation

D. Software engineering

    * Formal specifications and assertions
    * Verification techniques
    * Software development models, patterns and tools

E. Systems

    * Compilers, interpreters and run-time systems
    * Operating systems, including resource management and protection/security
    * Networking, Internet and distributed systems
    * Databases
    * System analysis and development tools

II. COMPUTER ORGANIZATION AND ARCHITECTURE — 15%

A. Digital logic design

    * Implementation of combinational and sequential circuits
    * Optimization and analysis

B. Processors and control units

    * Instruction sets
    * Computer arithmetic and number representation
    * Register and ALU organization
    * Data paths and control sequencing

C. Memories and their hierarchies

    * Performance, implementation and management
    * Cache, main and secondary storage
    * Virtual memory, paging and segmentation

D. Networking and communications

    * Interconnect structures (e.g., buses, switches, routers)
    * I/O systems and protocols
    * Synchronization

E. High-performance architectures

    * Pipelining superscalar and out-of-order execution processors
    * Parallel and distributed architectures

III. THEORY AND MATHEMATICAL BACKGROUND — 40%

A. Algorithms and complexity

    * Exact and asymptotic analysis of specific algorithms
    * Algorithmic design techniques (e.g., greedy, dynamic programming, divide and conquer)
    * Upper and lower bounds on the complexity of specific problems
    * Computational complexity, including NP-completeness

B. Automata and language theory

    * Models of computation (finite automata, Turing machines)
    * Formal languages and grammars (regular and context-free)
    * Decidability

C. Discrete structures

  
      Mathematical logic
      Elementary combinatorics and graph theory
      Discrete probability, recurrence relations and number theory

IV. OTHER TOPICS — 5%

Example areas include numerical analysis, artificial intelligence, computer graphics, cryptography, security and social issues.

Note: Students are assumed to have a mathematical background in the areas of calculus and linear algebra as applied to computer science.

Literature in English:-

   1. Literary Analysis (40 – 55%)
      An ability to interpret given passages of prose and poetry. Such questions may involve recognition of conventions and genres, allusions and references, meaning and tone, grammatical structures and rhetorical strategies, and literary techniques.
   2. Identification (15 – 20%)
      Recognition of date, author or work by style and/or content (for literary theory identifications see IV below).
   3. Cultural and Historical Contexts (20 – 25%)
      Questions on literary, cultural and intellectual history as well as identification of author or work through a critical statement or biographical information. Also identification of details of character, plot or setting of a work.
   4. History and Theory of Literary Criticism (10 – 15%)
      Identification and analysis of the characteristics and methods of various critical and theoretical approaches.
 

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