Teaching: Master Class M2R Parallel System

Where and When: These lectures take place from 9:45 to 12:45 every Monday in room H203, or H204. The schedule is also available online here (look for Ensimag, Semester 9, Parallel System). To find the building:  campus map. 

Credits & Exam:  The course gives 6 credits (ECTS).  There will be an exam at the end of the year.

Today, parallel computing is omnipresent across a large spectrum of computing platforms. At the “microscopic” level, processor cores have used multiple functional units in concurrent and pipelined fashions for years, and multiple-core chips are now commonplace with a trend toward rapidly increasing numbers of cores per chip. At the “macroscopic” level, one can now build clusters of hundreds to thousands of individual (multi-core) computers. Such distributed-memory systems have become mainstream and affordable in the form of commodity clusters. Furthermore, advances in network technology and infrastructures have made it possible to aggregate parallel computing platforms across wide-area networks in so-called “grids.” The popularization of virtualization has allowed to consolidate workload and resource exploitation in “clouds” and raise many energy and efficiency issues.

An efficient exploitation of such platforms requires a deep understanding of both architecture, software and infrastructure mechanisms and of advanced algorithmic principles. The aim of this course is thus twofold. It aims at introducing the main trends and principles in the area of high performance computing infrastructures, illustrated by examples of the current state of the art. It intends to provide a rigorous yet accessible treatment of parallel algorithms, including theoretical models of parallel computation, parallel algorithm design for homogeneous and heterogeneous platforms, complexity and performance analysis, and fundamental notions of scheduling and work-stealing. These notions will always be presented in connection with real applications and platforms.

Homework and Programming Project

You first have to study the course material. The entry point are the slides we use for our classes that are made available on this web page every week).  You are also expected to go beyond these slides looking by yourself for extra materials.

You will also have to  undertake a parallel programming a and performance analysis project (one by student). We let you choose the topic that you need to validate to us. Just speak to us at the class or send us an email with a short description of what you intend to do. If you have no idea with can propose you a few topics. Just speak to us.  You will have to return us a short report about your work and  expose your work during the last  lectures.

• You can run parallel code on your own machine. You have several cores and so can get some acceleration
• SimGrid:  SimGrid is a good way  to experiment with MPI at large scale on your own machine
  • MPI tutorial with SimGrid
• Grid’5000:  you can get an account of Grid’5000 to get access to a real parallel machine
  • Get an account  (and  put my login  braffin as manager)
  • MPI tutorial with Grid’5000