CS 426 Parallel Computing
Parallel programming platforms: distributed memory, shared address space, accelerators. Principles of parallel algorithm design: decomposition techniques, tasks and interactions, mapping for load balancing, interaction overheads, parallel algorithm models (data-parallel, task-graph, work-pool, master-slave, pipeline). Basic communication operations. Analytical modeling of parallel programs: sources of parallel programming overhead, performance metrics for parallel systems, scalability of parallel systems (speedup, efficiency, cost, overhead function, isoefficiency, cost optimality, degree of concurrency, granularity), parallel programming paradigms: programming using MPI, programming shared address space platforms (threads, OpenMP, Intel Thread Building Blocks), programming GPUs (CUDA, OpenCL). Parallel computing kernels: matrix transposition, matrix-vector multiplication, matrix-matrix multiplication, matrix partitioning schemes for load-balancing and communication minimization.
Prerequisite: CS 342
Introduction to Parallel Computing,
Author: Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar
Dr. Özcan Öztürk Office Hours: 13:30 - 15:30, Monday or by appointment.
Credit Hours: 3
Class Schedule: 15:40 - 17:30, M and 13:40 - 15:30, Th
Office Hours: 13:30 - 15:30, Tuesday or by appointment.
Grading Policy (Tentative):
Midterm Exam 1 25% 4 Apr, in class
Midterm Exam 2 25% 16 May, in class
Projects (3-5) 35% No late assignments will be accepted.
Class participation & pop quizzes 15%
Minimum Requirements to Qualify for the Final Exam:
%50 minimum weighted grade average from midterm 1, project, quiz
Lecture Contents (Tentative!)