How to Write Fast Numerical Code 263-2300 (ETH, CS)

Basic Information

• Course number: 263-2300, 6 credits
• Spring 2013, lectures: M 10:15-12:00, RZ F21; W 13:15-14:00 RZ F21; occasional substitute lectures: F 14:15-16:00 IFW B42
• Instructor: Markus Püschel (RZ H18, pueschel at inf, 2-7303)
  TAs:
  • Georg Ofenbeck (RZ H1.1, ofgeorg at inf, 2-8414)
  • Daniele Spampinato (RZ H1.1, daniele.spampinato at inf), 2-8414)
    Admin: Franziska Mäder (RZ H14, maeder at inf, 2-7311)
• Office hours:
  • Markus Püschel: email me to fix a time
  • Daniele Spampinato: Mon 14:00-15:00
  • Georg Ofenbeck: Wed 14:00-15:00
• Maling lists:
  • Forum to find project partner: fastcode-forum@lists.inf.ethz.ch (emails go to all students who have no partner yet and to Georg)
  • For technical questions: fastcode@lists.inf.ethz.ch (emails to this address go to the lecturerer and all TAs)
    

Course Description

The fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture.

This interdisciplinary course aims to give the student an understanding of performance and introduces foundations and state-of-the-art techniques in high performance software development using important functionality such as linear algebra algorithms, transforms, filters, and others as examples. The course will focus on optimizing for the memory hierarchy and special instruction sets, thus complementing courses on parallel programming. Much of the material is based on recent research.

Further, a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. Finally, the course will introduce the students to the recent field of automatic performance tuning.

Prerequisites: solid C programming skills, matrix algebra, Master student or above

Topics Covered

• Algorithm analysis: Problem versus algorithm, complexity and cost (asymptotic, exact, measured), cost analysis
• Computer architecture (a software point of view): architecture and microarchitecture, memory hierarchy, special instruction sets
• Compilers: strengths, limitations, how to use
• Performance optimization: guide to benchmarking, finding hotspots, code analysis, performance optimization techniques (for memory hierarchy and vector instruction extensions); these techniques are studied using the examples in the next bullet
• Numerical functionality studied in detail (complexity, algorithms, how to write highest performance code): linear algebra kernels, transforms, filters, sparse linear algebra, others, your research project
• Automatic Performance Tuning: ATLAS, LAPACK, BeBOP, FFTW, SPIRAL, others

Goals of this Course

• Obtain an understanding of runtime performance and how to reason about it
• Learn a guideline how to write fast numerical code and apply it in homeworks and your research project
• Understand the connection between algorithms, implementations, and computer architecture

Background Material

• Prior versions of this course:
  • spring 2012 (ETH)
  • spring 2011 (ETH)
  • spring 2008 (CMU)
  • spring 2005 (CMU)
• Chapters 5 and 6 in Computer Systems: A Programmer's Perspective, 2nd edition (available in library)
• This small tutorial
• Research papers, manuals, and other material mentioned in the slides or linked in the table below

Academic Integrity

All homeworks in this course are single-student homeworks. The work must be all your own. Do not copy any parts of any of the homeworks from anyone including the web. Do not look at other students' code, papers, or exams. Do not make any parts of your homework available to anyone, and make sure noone can read your files. The university policies on academic integrity will be applied rigorously.

We will be using the Moss system to detect software plagiarism. This system is amazingly good, because it understands the programming language in question (C, in our case).

It is not considered cheating to clarify vague points in the assignments or textbook, or to give help or receive help in using the computer systems, compilers, debuggers, profilers, or other facilities.

Grading

• 40% research project
  • Topic: Very fast, ideally adaptive implementation of a numerical problem
  • Team up in pairs
  • March 7: find a partner, find a problem or I give you one (tip: look at the prior courses linked above for examples)
  • Show "milestones" during semester
  • Write 6 page standard conference paper (template will be provided)
  • Give short presentation end of semester
• 20% midterm
• 40% homework
  • Exercises on algorithms analysis
  • Implementation exercises
    • study the effect of program optimizations, compilers, special instructions, etc.
    • write and submit C code & create runtime/performance plots
  • Some templates will be provided
  • All homeworks are single-student homeworks
• There is no final Exam

Research Project

• How it works:
  • Weeks without homeworks should be used to work on the project
  • You select a numerical problem and create a correct (verified) implementation in C
  • You determine the arithmetic cost, measure the runtime and performance
  • You profile the implementation to find the parts in which most the runtime spent
  • Focussing on these you apply various optimization techniques from this class
  • You repeat the previous steps to create various versions with (hopefully) continuously better runtime
  • You write a paper about your work and give a presentation
• Paper:
  • Maximal 6 pages (hard limit), conference style, template and instructions below
  • Everybody reads this: report.pdf
  • For latex use: report.zip (start with reading the README file)
  • For Word (discouraged) use this: report-word.doc
• Presentation
  • Last week of classes (Wed and Fr lecture), each talk is 10 minutes
  • Template and guidelines (ppt is 2007 and later; the use is totally optional): presentation-template.pptx , presentation-template.pdf
  • The order will be determined randomly right before class
  • Who talks will be determined randomly right before class
• Projects (each one has a supervisor shown in brackets):
  1. Stefan B. & Tobias S.: Fast multigrid solver for biharmonic equation ()
  2. Rico H. & Donjan R.: Simplex ()
  3. Ria F. & Timon G.: Fluid dynamics ()
  4. Alexandros K. & Grzegorz M.: Miniball ()
  5. Julia P. & Pascal S.: & Adrian B. Domain Transform for Edge-Aware Image and Video Processing ()
  6. Fabian H. & Alex C.: ()
  7. Carl-Anton I. & Xavier L.: Tri-Cubic interpolation ()
  8. Severin W. & Lorenzo B..: Arithmetics of large numbers ()
  9. Filippo A. & Patrick S.: Image denoising ()
  10. Sebastian K. & Andri S.: Quantum informational entropy minimization ()
  11. Nedyalko P. & Denis P.: ()
  12. Alexandre C. & Fabian H.: Exposure Fusion ()
  13. Ivo S. & Tim Grabowski.: ()
  14. Pavol B. & Gadandeep S. & Vanya D.: Fast abstract domains ()
  15. Simon L. & Stefan L. & Markus A.: Binary feature detector ()
• One-on-one meetings, I: TBD
• One-on-one meetings, II: TBD

Midterm

Tentative: April 15th during class

Homework

Homework 0: due as soon as possible
Homework 1: due Th, 07.03., 17:00
Homework 2: due Th, 14.03., 17:00

Lectures (including pdfs)