Course Description

Course objectives: To obtain the high level of end-to-end performance needed in problem domains like graphics, computer games, and scientific computing, it is necessary for programs to exploit many of the features of modern computer architectures.  In this course, we will study the performance-critical features of modern computer architectures, and discuss how applications can take advantage of them to obtain high performance.  This is not a course on software tricks; rather, the emphasis is on abstractions of computer architecture, understanding performance, and obtaining performance when you need it.

Topics covered in lecture include the following:

  1. Analysis of applications that need high end-to-end performance
  2. Understanding performance: performance models, Amdahl's law
  3. Measurement and design of computer experiments
  4. Microbenchmarks for abstracting performance-critical aspects of computer systems
  5. Memory hierarchy: caches, virtual memory, exploiting spatial and temporal locality
  6. Vectors and vectorization
  7. GPUs and GPU programming
  8. Multi-core processors and shared-memory programming, OpenMP
  9. Distributed-memory machines and message-passing programming, MPI
  10. Optimistic parallelization
  11. Self-optimizing software

Prerequisites: programming maturity, knowledge of C/C++, basic course on modern computer architecture

Course work: There will 6 substantial programming assignments (60% of grade), a mid-semester exam (15% of grade) and a final exam (25% of grade).

Discussion and assignment: You need to use Canvas and Piazza for discussion and submitting assignments.
Please enroll in Piazza and follow the instruction to create TACC account ASAP.

Lecture slides and notes


Basic material on computer architecture:
Book: "Computer Architecture: A Quantitative Approach"  by Hennessy & Patterson, Morgan Kaufmann Publishers.
Lecture slides on computer architecture

Academic honesty policy

You may discuss concepts with classmates, but all written work and programming assignments must be your own or your project team's work when teamwork is permitted. You may not search online for existing implementations of algorithms related to the programming assignments, even as a reference. Students caught cheating will automatically fail the course and will be reported to the university. If in doubt about the ethics of any particular action, talk to the instructor or the TA .

Notice about students with disabilities

The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. For more information, contact the Division of Diversity and Community Engagement Services for Students with Disabilities at 512-471-6529.