GRE Subject Computer Science

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Download the Computer Science Practice Booklet (PDF)

  • The test consists of approximately 70 multiple-choice questions, some of which are grouped in sets and based on such materials as diagrams, graphs and program fragments.
  • The approximate distribution of questions in each edition of the test according to content categories is indicated by the following outline.
  • The percentages given are approximate; actual percentages will vary slightly from one edition of the test to another.

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.

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