ECE 492

• Computer Engineering

Introduction to fundamental issues in design and development of parallel programs for various types of parallel computers. Various programming models according to both machine type and application area. Cost models, debugging, and performance evaluation of parallel programs with actual application examples.

Same as CS 420 and CSE 402

• Ideal and real machine models: vector (pipelined) processors, array machines, shared-memory multiprocessors, message-passing multiprocessors, others; programming constructs native to each class of machines
• Programming models and their languages: data-parallel models (array parallelism, parallel loops), process-based models; illustrative examples, such as matrix multiplication, sorting, and the n-body problem
• Cast models and efficiency analysis of parallel programs
• Parallel programming issues: locality, grain size, scheduling, load balancing, data distribution and alignment, communication analysis, synchronous programming, determinacy and nondeterminacy
• Debugging parallel programs
• Performance measurement, evaluation and tuning
• Application case studies, e.g., from computational fluid dynamics, computational biology, and operations research
• Optional topic selected, e.g., from discrete event simulation, grid-structured computations, tree-structured computations, sparse and dense linear systems, parallelizing compilers

Topics:

• Ideal and real machine models: vector (pipelined) processors, array machines, shared-memory multiprocessors, message-passing multiprocessors, others; programming constructs native to each class of machines
• Programming models and their languages: data-parallel models (array parallelism, parallel loops), process-based models; illustrative examples, such as matrix multiplication, sorting, and the n-body problem
• Cast models and efficiency analysis of parallel programs
• Parallel programming issues: locality, grain size, scheduling, load balancing, data distribution and alignment, communication analysis, synchronous programming, determinacy and nondeterminacy
• Debugging parallel programs
• Performance measurement, evaluation and tuning
• Application case studies, e.g., from computational fluid dynamics, computational biology, and operations research
• Optional topic selected, e.g., from discrete event simulation, grid-structured computations, tree-structured computations, sparse and dense linear systems, parallelizing compilers

Same as CS 420 and CSE 402

No text.