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

Basic Information

• Course number: 263-2300, 6 credits
• Spring 2014, lectures: M 10:15-12:00, CAB G59; W 9:15-10:00 CAB G52; occasional substitute lectures: F 14:15-16:00 HG E22
• Instructor: Markus Püschel (CAB H69.3, pueschel at inf, 2-7303)
  TAs:
  • Daniele Spampinato (CAB H83.1, daniele.spampinato at inf)
  • Alen Stojanov (CAB 81.2, astojanov at inf)
  • Only for project supervision: Georg Ofenbeck (CAB H83.1, ofgeorg at inf)
    
• Office hours:
  • Markus Püschel: Tues 2-3pm
  • Daniele Spampinato: Mon 3-4pm
  • Alen Stojanov: Wed 2-3pm
• Mailing lists:
  • Forum to find project partner: fastcode-forum@lists.inf.ethz.ch (emails go to all students who have no partner yet and to Daniele)
  • 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 2013 (ETH)
  • 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)
• Research papers, manuals, and other material mentioned in the slides or linked in the table below
• This small tutorial

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)
  • Complete "milestones" during semester and enter them into the online check list
  • 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

• All projects have to be registered at https://medellin.inf.ethz.ch/courses/263-2300-ETH/. This site is also used later for updates.
• 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
  • Due date: Friday, June 13 (as final-report.pdf in your svn)
• Presentation
  • Last week of classes
  • Template (the use is totally optional) and some guidelines (ppt is 2007 and later): 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. Mario K. & Dominik G.: MC/Metropolis for the Potts-Model (GO)
  2. Erik H. & Boris P.: Pentagon Abstract Domain (MP)
  3. Matteo P. & Alessandro D. & Martynas P.: Viterbi algorithms for speech recognition (MP)
  4. Matthias S. & Julien D.: Spectral clustering (GO)
  5. Jeremie M.: Exposure fusion (MP)
  6. Benedikt B. & Simon K.: Optimizing query time in a bounding volume hierarchy (MP)
  7. An-phi N. & Danieli F. & Matthias H.: QP solvers for MPC based on Pontryagin's minimum principle (DS & AS)
  8. Robin R. & Federico A.: Kalman filter on ARM Cortex (DS & AS)
  9. Matthias G. & Silvio T.: Efficient Particle Trajectory Calculation for the Detection of Langrangian Coherent Structures (MP)
  10. Lukas V. & Lysgaard M. O. & Beat K.: SEEDS image segmentation (MP)
  11. James G. & Julian V.: Optimal Binary Search Trees (GO)
  12. Manuel K. & Benjamin S.: Particle Strength Exchange for Reaction Diffusion (MP)
  13. Benjamin F. & Simon. L & Jakob P.: Dynamically Controlled Particle Systems (DS & AS)
  14. Pascal G. & Jörn R.: Bundle Adjustment on an ARM Cortex (DS & AS)
  15. Michael B. & Sammy O.: Stereo Image Block Matching on ARM Cortex (DS & AS)
  16. Stefan D. & Jeremia B.: Optimal Binary Search Trees (GO)

Tips & Tricks (From Students)

• Apparently the only way to disable TurboBoost on OS X is via kernel extensions. I found one here: https://github.com/nanoant/DisableTurboBoost.kext which seems to work well.

Midterm

April 14th: 10:15-12:00 in HG D7.1 (solution, without solution)

Homework

No deadline extensions, but you have 3 late days. You can use at most 2 on one homework. For example, submitting 7 hours late costs one late day.

• Homework 0: due as soon as possible.
• Homework 1: due Th, 06.03., 5pm. Solution.
• Homework 2: due Th, 13.03., 5pm. Solution.
• Homework 3: due Th, 20.03., 5pm. Solution.
• Homework 4 (start project): check point Th, 27.03., 5pm.
• Homework 5: due Th, 03.04., 5pm. Solution.
• Homework 6 (continue project): check point Th, 17.04., 5pm.
• Homework 7 (continue project): check point Th, 08.05., 5pm.

Lectures (including pdfs)