CS 479 : Programming Parallel Systems ( NOTE CHANGE IN ROOM BELOW )

Fall 2012
Instructor Arrvindh Shriraman
Office : 4712 Surrey Campus, 9213 Burnaby Campus
Email :
Office hours : Monday (4-5:30 starting Sep 20th) or Friday (4-5) @ Surrey.
T.A : This offering of 479 has no TA.

What is the class about?

Are you interested in understanding the architecture of cutting-edge systems that you will be programming in the future and what challenges confront software? In parallel computing, multiple processors compute sub-tasks simultaneously so that work can be completed faster. Early parallel computing has focused on solving large computation-intensive problems like scientific simulations. With the increasingly available commodity multiprocessors (like multicores), parallel processing has penetrated into many areas of general computing. In distributed computing, a set of autonomous computers are coordinated to achieve unified goals. More interestingly the community is increasingly turning to parallel programming models (e.g., Google's Mapreduce, Microsoft's Dryad Linq) to harness the capabilities of these large scale distributed systems.

This course explores the paradigms of parallel and distributed computing, their applications, and the systems/architectures supporting them. This course is a hands-on, programming-heavy course. Expect to get down and get your hands dirty with concurrent C++ programming. We will discuss the fundamental design and engineering trade-offs in parallel systems at every level. We will study not only how they work today, but also try to understand the design decisions and how they are likely to evolve in the future. We will draw examples from real-world parallel systems in this course. We will begin with a primer on multicore manycore chips: chips that integrate multiple CPUs on the same die. Currently, Intel is shipping 10 core (20 thread chips) and our own AMD servers are 2 socket 24 thread systems. Harnessing these new compute engines is an open challenge. We will focus on their architectures and different programming models that empower us to tap into this fountain of CPUs. We will study the system and architectural support for shared memory parallel computing, including support for synchronization, cache coherence, and memory consistency. We will look at programming abstractions such as locks, mutexes, conditional synchronization, and transactional memory. At this stage we will also be exposed to the manycore accelerators such as gaming processors and GPUs which promise 100x performance improvement for "data parallel" workloads.

We will examine the parallel computing models like MPI and Mapreduce customized to distributed systems. Finally, we will perform case studies of practical distributed systems and devote significant attention to cluster-based server systems in large Internet data centers (e.g., those running Google services).