Gre cs test

Hello sir I will appear in GRE CS test in this year so can you tell me content which includes in this exam , can you here give me material of this exam

[Shaleen]

Graduate Record Examination (GRE) is a standardized test that is an admissions requirement for most graduate schools in the United States, and GRE Subject Tests are designed to help graduate
school admission committees and fellowship sponsors
assess the qualifications of applicants in specific fields
of study.

GRE CS (computer science ) study material
I. SOFTWARE SYSTEMS AND
METHODOLOGY — 40%
A. Data organization
1. Data types
2. Data structures and implementation
techniques
B. Program control and structure
1. Iteration and recursion
2. Procedures, functions, methods, and
exception handlers
3. Concurrency, communication, and
synchronization
C. Programming languages and notation
1. Constructs for data organization and
program control
2. Scope, binding, and parameter
passing
3. Expression evaluation
D. Software engineering
1. Formal specifications and assertions
2. Verification techniques
3. Software development models, patterns,
and tools
E. Systems
1. Compilers, interpreters, and run-time systems
2. Operating systems, including resource
management and protection/security
3. Networking, Internet, and distributed systems
4. Databases
5. System analysis and development tools
II. COMPUTER ORGANIZATION AND
ARCHITECTURE — 15%
A. Digital logic design
1. Implementation of combinational and
sequential circuits
2. Optimization and analysis
B. Processors and control units
1. Instruction sets
2. Computer arithmetic and number representation
3. Register and ALU organization
4. Data paths and control sequencing
C. Memories and their hierarchies
1. Performance, implementation, and management
2. Cache, main, and secondary storage
3. Virtual memory, paging, and segmentation
D. Networking and communications
1. Interconnect structures (e.g., buses,
switches, routers)
2. I/O systems and protocols
3. Synchronization
E. High-performance architectures
1. Pipelining superscalar and out-of-order
execution processors
2. Parallel and distributed architectures
III. THEORY AND MATHEMATICAL
BACKGROUND — 40%
A. Algorithms and complexity
1. Exact and asymptotic analysis of specific
algorithms
2. Algorithmic design techniques (e.g., greedy,
dynamic programming, divide and conquer)