CS GRE

Anything above a 800 (it's on a 200-990-point scale) seems pretty good. It appears that quality single-volume preparation materials cannot be had at any price. Perhaps one ought be written?

• ETS Practice Booklet (PDF), from the Computer Science Exam page

(This second link is pretty dubiously-formed; YMMV. Go to the GRE page, click on Subject Info details, click on Computer Science Dank 14:58, 30 July 2009 (UTC))

• UCB notes from 2001
• GT notes from 2006

Subject Material

This was taken from the ETS CS GRE page, 2009-07-30 1500 UTC.

SOFTWARE SYSTEMS AND METHODOLOGY — 40%

• 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 run-time systems
  • Operating systems, including resource management and protection/security
  • Networking, Internet, and distributed systems
  • Databases
  • System analysis and development tools

COMPUTER ORGANIZATION AND ARCHITECTURE — 15%

• 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
• High-performance architectures
  • Pipelining superscalar and out-of-order execution processors
  • Parallel and distributed architectures

THEORY AND MATHEMATICAL BACKGROUND — 40%

• 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
• 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 TOPICS — 5%

Example areas include numerical analysis, artificial intelligence, computer graphics, cryptography, security, and social issues.