U.C. Berkeley CS267

Applications of Parallel Computers

Spring 2012

Syllabus

High-Level Description

This course teaches both graduate and advanced undergraduate students from diverse departments how use parallel computers both efficiently and productively, i.e. how to write programs that run fast while minimizing programming effort. The latter is increasingly important since essentially all computers are (becoming) parallel, from supercomputers to laptops. So beyond teaching the basics about parallel computer architectures and programming languages, we emphasize commonly used patterns that appear in essentially all programs that need to run fast. These patterns include both common computations (eg linear algebra, graph algorithms, structured grids,..) and ways to easily compose these into larger programs. We show how to recognize these patterns in a variety of practical problems, efficient (sometimes optimal) algorithms for implementing them, how to find existing efficient implementations of these patterns when available, and how to compose these patterns into larger applications. We do this in the context of the most important parallel programming models today: shared memory (eg PThreads and OpenMP on your multicore laptop), distributed memory (eg MPI and UPC on a supercomputer), GPUs (eg CUDA and OpenCL, which could be both in your laptop and supercomputer), and cloud computing (eg MapReduce and Hadoop). We also present a variety of useful tools for debugging correctness and performance of parallel programs. Finally, we have a variety of guest lectures by a variety of experts, including parallel climate modeling, astrophysics, and other topics.

  • Computer Architectures (at a high level, in order to understand what can and cannot be done in parallel, and the relative costs of operations like arithmetic, moving data, etc.).
  • Sequential computers, including memory hierarchies
  • Shared memory computers and multicore
  • Distributed memory computers
  • GPUs (Graphical Processing Units, eg NVIDIA cards)
  • Cloud and Grid Computing
  • Programming Languages and Models for these architectures
  • Threads
  • OpenMP
  • Message Passing (MPI)
  • UPC and/or Titanium
  • Communication Collectives (reduce, broadcase, etc.)
  • CUDA/OpenCL etc. (for GPUs)
  • Cilk
  • Sources of parallelism and locality in simulation: The two most important issues in designing fast algorithms are (1) identifying enough parallelism, and (2) minimizing the movement of data between memories and processors (moving data being much slower than arithmetic or logical operations. We discuss how simulations of real-world processes have naturally exploitable parallelism and "locality" (i.e. data than needs to be combined can naturally be stored close together, to minimize its movement).
  • Programming "Patterns": It turns out that there is a relatively short list of basic computing problems that appear over and over again. Good ways to solve these problems exist, and so it is most productive to be able to recognize these "patterns" when they appear, and use the best available algorithms and software to implement them. The list of patterns continues to evolve, but we will present the most common ones, and also illustrate how they arise in a variety of applications.

    Originally, there were 7 such patterns that were identified by examining a variety of high performance computational science problems. Since there were 7, they were called the "7 dwarfs" of high performance computing. For each one, we will discuss its structure and usage, algorithms, measuring and tuning its performance (automatically when possible), and available software tools and libraries.

  • Dense linear algebra (matrix multiply, solving linear systems of equations, etc.)
  • Sparse linear algebra (similar to the dense case, but where the matrices have mostly zero entries and the algorithms neither store nor operate on these zero entries).
  • Structured Grids (where the data is organized to lie on a "grid", eg a 2-dimensional mesh, and the basic operations are the same at each mesh point (eg "average the value at each mesh point with its neighbors").
  • Unstructured Grids (similar to the above, but where "neighbor" can be defined by an arbitrary graph)
  • Spectral Methods (the FFT, or Fast Fourier Transform, is typical).
  • Particle Methods (where many "particles" (eg atoms, planets, people,...) are updated (eg moved) depending on the values of some or all other particles (eg by electrostatic forces, gravity, etc.)
  • Monte Carlo, sometimes also called MapReduce (as used by Google), where every task is completely independent, but may finish at a different time and require different resources, and where the results of all the tasks may be combined ("reduced") to a single answer.
  • The next 6 patterns of parallel computing were identified by examining a broad array of nonscientific applications that require higher performance via parallelism; not only did the above "7 dwarfs" appear, but 6 other computational patterns, that we will probably only have time to partially cover: (see here for details):
  • Finite State Machines, where the "state" is updated using rules based on the current state and most recent input
  • Combinational Logic, performing logical operations (Boolean Algebra) on large amounts of data
  • Graph traversal, traversing a large graph and performing operations on the nodes
  • "Graphical models" involve special graphs representing random variables and probabilities, and are used in machine learning techniques
  • Dynamic Programming, an algorithmic technique for combining solutions of small subproblems into solutions of larger problems
  • Branch-and-Bound search, a divide-and-conquer technique for searching extremely large search spaces, like those arising in games like chess
  • More Patterns - there are various other structural patterns that are useful for organizing software (parallel or sequential) that we will cover as well.
  • Measuring performance and finding bottlenecks
  • Load balancing techniques, both dynamic and static
  • Parallel Sorting
  • Assorted possible guest lectures (some repeats, some new; depends on availability of lecturers)
  • Performance Measuring and Debugging Tools
  • Parallel Debugging Tools
  • Climate Modeling
  • Computational Astrophysics
  • Computational Biology
  • Computational Nanoscience
  • Volunteer Computing (eg how seti@home etc work)
  • Simulating the Human Brain
  • Musical performance and delivery (ParLab application)
  • Image Processing (ParLab application)
  • Speech Recognition (ParLab application)
  • Modeling Circulatory System of Stroke Victims (ParLab application)
  • Parallel Web Browers (ParLab application)

    • Detailed Schedule of Lectures (lecturers shown in parentheses)

  • Jan 17 (Tuesday): Introduction: Why Parallel Computing? (Jim Demmel)
  • Jan 19 (Thursday): Single processor machines: Memory hierarchies and processor features (Jim Demmel)
  • Jan 24 (Tuesday): Introduction to parallel machines and programming models (Jim Demmel)
  • Jan 26 (Thursday): Sources of parallelism and locality in simulation: Part 1 (Jim Demmel)
  • Jan 31 (Tuesday): Sources of parallelism and locality in simulation: Part 2 (Jim Demmel)
  • Feb 2 (Thursday): Shared memory machines and programming: OpenMP and Threads; Tricks with Trees (Jim Demmel)
  • Feb 7 (Tuesday): Distributed memory machines and programming in MPI (Jim Demmel)
  • Feb 9 (Thursday): Partitioned Global Address Space Programming with Unified Parallel C (UPC)(Kathy Yelick)
  • Feb 14 (Thursday): GPUs, and programming with with CUDA and OpenCL (Bryan Catanzaro)
  • Feb 16 (Tuesday): Performance and Debugging Tools (David Skinner, Richard Gerber)
  • Feb 21 (Tuesday): Dense Linear Algebra: Part 1 (Jim Demmel)
  • Feb 23 (Thursday): Dense Linear Algebra: Part 2 (Jim Demmel)
  • Feb 28 (Tuesday): Graph Partitioning: Part 1 (Jim Demmel)
  • Mar 1 (Thursday): Graph Partitioning: Part 2, and Sparse-Matrix-Vector-Multiply (Jim Demmel)
  • Mar 6 (Tuesday): Sparse-Matrix-Vector-Multiply and Autotuning (Jim Demmel)
  • Mar 8 (Thursday): Particle (N-Body) methods (Jim Demmel); Efficient Data Race Detection for Distributed Memory Parallel Programs (Chang-Seo Park)
  • Mar 13 (Tuesday): Structured grids and multigrid (Jim Demmel)
  • Mar 15 (Tuesday): Cloud computing with MapReduce and Hadoop (Matei Zaharia)
  • Mar 20 (Tuesday): Patterns of Parallel Programming (Kurt Keutzer)
  • Mar 22 (Thursday): Structured grids(Jim Demmel)
  • Mar 26-30: Spring Break
  • Apr 3 (Tuesday): Frameworks for Structured Software Development (John Shalf)
  • Apr 5 (Thursday): Exascale Computing (Katherine Yelick)
  • Apr 10 (Tuesday): Parallel Graph Algorithms (Aydin Buluc)
  • Apr 12 (Thursday): Parallel Climate Modeling (Michael Wehner)
  • Apr 17 (Tuesday): Parallel Fast Fourier Transform (FFT) (Jim Demmel)
  • Apr 19 (Thursday): Dynamic Load Balancing (Jim Demmel)
  • Apr 24 (Tuesday): Big Bang, Big Data, Big Iron: High Performance Computing and the Cosmic Microwave Background (Julian Borrill)
  • Apr 26 (Thursday): Accelerated Materials Design through High Throughput First-Principles Calculations and Data Mining (Kristin Persson)
  • May 3 (Thursday): Student Poster Session