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GRE Subject Test -Computer Science Syllabus
  • The test consists of about 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