Advanced Computer Architecture

(3-0-0-3)

CMPE Degree: This course is Elective for the CMPE degree.

EE Degree: This course is Elective for the EE degree.

Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.

Technical Interest Group(s) / Course Type(s): Computer Systems and Software

Course Coordinator: Moinuddin K Qureshi

Prerequisites: ECE 3057

Corequisites: None.

Catalog Description

Comprehensive coverage of the architecture and system issues that confront
the design of high performance workstation/PC computer architectures with
emphasis on quantitative evaluation. Credit is not allowed for both ECE
4100 and any of the following courses: ECE 6100, CS 4290, CS 6290.

Textbook(s)

Computer Architecture: A Quantitative Approach

Course Outcomes

  1. Perform quantiative evalution of in-order and out-of-order processors.
  2. Analyze modern memory systems.
  3. Perform power and energy trade-offs in modern computer systems.

Student Outcomes

In the parentheses for each Student Outcome:
"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.

1. ( P ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics

2. ( M ) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors

3. ( LN ) An ability to communicate effectively with a range of audiences

4. ( LN ) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts

5. ( LN ) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives

6. ( P ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions

7. ( LN ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Strategic Performance Indicators (SPIs)

Not Applicable

Course Objectives

Topical Outline

Performance Evaluation
Metrics for performance (MIPS, IPC, Execution time)
Different means and when it is right to use them
Benchmarks
Amdahl’s law
Pipelines
Review basic principles, hazards, dependencies
Data hazards, scoreboards, Tomasulo algorithm
Control hazards, advanced branch prediction techniques
Multiple instruction issue
Precise exception and in-order-retirement
Reorder buffer
Memory systems
Review of caches principles
Techniques to reduce miss rate, hit time, miss time (multi-level caches)
DRAM memory: basic organization and operation (row hit/miss)
Prefetching and simple prefetch algorithms (stream/stride)
Virtual memory
Parallel Computers
Taxonomy of parallel architectures, parallel applications
Shared memory and message passing computers
Multi-core processors: shared cache and private cache
Multi-threading: Coarse-grained, fine-grained, and SMT
Vectors and SIMD
GPU basic organization and operation
Coherence protocols: bus based and directory based
Memory consistency models, relaxed consistency models
Synchronization, locks and barriers
Interconnection networks, bi-section bandwidth, topologies
Power and Energy Issues
Basics of energy dissipation: dynamic and static
Clock gating and power gating
Dynamic voltage and frequency scaling
Turbo-boost
Microarchitecture power models
Storage Systems
Reliability: RAID, RAID-1 to RAID-5
Parity and error-correction code