Instructor: Allan Struthers Office: Fisher 212 Phone: 487-3541 e-mail: struther@mtu.edu Web Site: http://www.mathlab.mtu.edu/~struther/Courses/5580/ My schedule: https://huskymail.mtu.edu/home/struther@mtu.edu/Calendar.html Meeting 3-4pm M/W/F Fisher 101 Fisher 
- Notes and Matearials:
- We do not have a text for this course. We will draw from a variety of sources including the online lecture notes (http://www.cs.berkeley.edu/~demmel/) for the UC berkely course CS267 taught by James Demell and Kathy Yelick in Spring 2011 and the online notes from Mary Hall (http://www.cs.utah.edu/~mhall/) from the University of Utah. We will also use the CUDA documentation available on line from NVIDIA at http://developer.nvidia.com/category/zone/cuda-zone.
- Objectives:
- The course will address design and implementation of parallel algorithms for a number of different fundamental computational areas including Linear Algebra (Sparse and Dense), Numerical ODE., and Numerical Optimization. The course should be accessible to anyone with some computational programming experience. The aspects of computer architecture important to the design of algorithms will be introduced and discussed. When designing and implementing algorithms our primary target will be the very hierarchical design of a modern Graphical Programming Unit GPU with a few hundred hierarchical cores, a hierarchical memory structure including shared memory blocks, a hierarchical thread structure, and a scheduler capable of effectively managing a few thousand threads. Examples will be implemented in either the small CUDA extension to C (run on mathlab machines, the paracuda cluster, or suitable home machines) and/or simulated in Mathematica or Matlab depending on the audience. Substantial computer exercises will be required in this class. To implement code on the GPU some epxerience with sequential base-level C is very desirable: in this case C is better than C++ or Fortran. I will run a "Boring C Boot Camp" if neccessary during the second week of the term.
- HW exams etc.: There will be frequent HW (all equally weighted and mostly computational) and a computational final project that will count as several (we will discuss this in class on the first day) HW assignments.
- Affirmative Action Notice: “MTU complies with all federal and state laws and regulations regarding discrimination, including the ADA Act of 1990. If you have a disability and a need, a reasonable accommodation for equal access to education or services can be made through the Dean of Students office (Gloria Melton 487-2212). For concerns regarding discrimination of any kind, contact your advisor, department head, or affirmative action office. ”