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GRE Computer Science Syllabus

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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