Computer Science Subject GRE consists of approximately 70 questions.
Questions are multiple choice. The percentages given are approximate; actual percentages will vary slightly from one edition of the test to another.
Lets look at Subject GRE Computer Science Syllabus, test contents and structure.
Subject GRE Computer Science Syllabus
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
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Mathematical logic
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Elementary combinatorics and graph theory
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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.
