GRE Subject Test: Computer Science
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GRE will comprises of 70 multiple choice questions (MCQs), which are clubbed in the sets on the basis of materials as diagrams, graphs and program fragments.
The allocation of all the questions in each edition of the test as per the content categories is mentioned in the outline stated below:
Percentages that are mentioned below are approximates; actual percentages can somehow differ from one edition of the test to another.

Software Systems and Methodology40%

Data organization, Data types, Data structures and implementation techniques.

Program control and structure, Iteration and recursion Procedures, functions, methods, and exception handlers Concurrency, communication and synchronization.

Programming languages and notation Constructs for data organization and program control Scope, binding, and parameter passing, Expression evaluation.

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

Systems, Compilers, interpreters and runtime systems, Operating systems comprising resource management and security, Networking, Internet and distributed systems, Databases, System analysis and development tools.


Computer Organization and Architecture15%

Digital logic design, Implementation of combinational and sequential circuits, Optimization and analysis.

Processors and control units, Instruction sets, Computer arithmetic and number representation, Register and ALU organization, Data paths and control sequencing.

Memories and their hierarchies, Performance, implementation, and management Cache, main, and secondary storage Virtual memory, paging and segmentation.

Networking and communications, Interconnect structures (e. g. Buses, switches, routers) I/O systems and protocols, Synchronization.

Highperformance architectures, Pipelining superscalar and outoforder execution processors, Parallel and distributed architectures.


Theory and Mathematical Background40%

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 also comprising NPcompleteness.

Automata and language theory, Models of computation (finite automata, Turing machines), Formal languages and grammars (regular and context free), Decidability.

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


Other Topics5%, Examples will cover numerical analysis, artificial intelligence, computer graphics, cryptography, security and social issues.