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
• 100 shareware CS GRE-like problems from Christopher Scaffidi

<countdown time="10/10/2009 8:30 AM UTC-0500"><D> days, <H> hours, <M> minutes, seconds until the 2009-10-10 CS GRE</countdown>

Books I'm using for general preparation

• Software Systems and Methodology
  • Peter Van Roy and Seif Haridi, Concepts, Techniques, and Models of Computer Programming. First edition, fourth printing.
  • Harold Abelson and Gerald Jay Sussman, Structure and Interpretation of Computer Programs. Second edition, first printing, and online version.
• Computer Organization and Architecture
  • John Hennessy and David Patterson, Computer Architecture: A Quantitative Approach. Second and fourth editions (first printing each).
  • Stephen Ward and Robert Halstead, Computation Structures. First edition, first printing.
• Theory and Mathematical Background
  • Thomas Cormen, Charles Leiserson and Ron Rivest, Introduction to Algorithms. First edition, 19th printing.
  • Michael Sipser, Introduction to the Theory of Computation. First edition, first printing.
  • Michael Garey and David Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness. First edition, 26th printing.

Further texts are listed in the applicable sections below.

Online courseware

• The Stanford Engineering Everywhere project at the Stanford School of Engineering
• MIT's OpenCourseware project at the Open Courseware Consortium

Subject Material

Outline taken from the ETS CS GRE page, 2009-07-30 1500 UTC)

Area vocabulary listings follow each area in small, italicized, bolded text	References and Supplements
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 Dijkstra: A Discipline of Programming
Verification techniques Berg, Boebert, Franta, Moher: Formal Methods of Program Verification and Specification
Software development models, patterns, and tools
Systems
Compilers, interpreters, and run-time systems Muchnick: Advanced Compiler Design and Implementation Allen, Kennedy: Optimizing Compilers for Modern Architectures
Operating systems, including resource management and protection/security
Networking, Internet, and distributed systems Leon-Garcia, Widjaja: Communication Networks
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 signed magnitude, one's complement, two's complement, binary coded decimal, IEEE 754
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)	Divide+Conquer, Dynamic Programming and Greedy algorithm lectures from MIT's 6.046J
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.