1. FOUNDATIONS OF MODERN SCIENTIFIC PROGRAMMING
COMPENG 701
FOUNDATIONS OF MODERN SCIENTIFIC PROGRAMMING

2. Course Information Instructor: Yuriy Zinchenko,
Information Technology Building 221,
course syllabus at
Time and location:
Thursday, 10:30-13:30,
ITB 235

3. Course Goals introduce to high-performance/high-throughput computing
equip with basic knowledge of computing tools available
The course is self-contained, tutorial-based.
Focus: Basic Linear Algebra Subprograms

4. Tentative Layout introductory lecture
file systems, binary systems, computer and processor architecture
Linux/Unix and Windows operating systems
introduction to MATLAB and Octave
introduction to C and Fortran
BLAS
memory management and processor architecture for high-performance computing, compiler flags, tuning BLAS, Automatically Tuned Linear Algebra Software
writing fast code in MATLAB
external libraries with MATLAB/Octave, MEX files, BLAS interfacing
introduction to parallel computing, cluster and multi-processor system (MPS) environments
C/Fortran for high-performance computing on MPS, OpenMP,
using clusters (SHARCNET)

5. Typical Applications for BLAS
numerical PDE’s
image processing
core of most optimization engines
many others
Important: always exploit data structure!

6. Radiation Therapy for Cancer Treatment
An Example
Radiation Therapy for Cancer Treatment
Background:
About 1.3 million new cancer cases in the U.S. each year
Nearly 60% receive radiation therapy, in conjunction with surgery, chemotherapy, etc.

7. External beam radiation therapy
Radiation delivered by a linear accelerator
Cancer cells more susceptible than normal cells
Dose given in daily fractions for ~ 6 weeks
Overlay beams from different angles

8. Intensity Modulated Radiation Therapy
Block parts of the radiation beam – discretize the beam into smaller “beamlets”
Choose different intensities for each beamlet
Intensity Modulated Radiation Therapy Collaborative Working Group, 2001

9. Treatment planning
Goal: Choose beam angles and beamlet intensities that kill tumor and spare healthy tissues
Take CT scan
Delineate
Discretize body into “voxels”
Formulate & solve a mathematical program to find a “good” plan
Princess Margaret Hospital

10. One facet of RT treatment planning
Formulate and solve “inverse problem” to find beamlet intensities that satisfy clinical objectives
Assuming: all the input data are error-free
Common approach: use optimization

11. Calculating dose delivered
Dose delivered to voxel:
w(b) = intensity of
beamlet b
(decision variable)
Dose(b,v) = dose
to voxel v
from beamlet b
(parameter)

12. Types of models Common objectives:
Maximize the minimum dose to the target region
Minimize deviations from prescribed dose
Common constraints:
Homogeneity
Maximum dose constraints
Dose-volume constraints

13. How to deliver ?
Bad news: sensitive to errors

14. Uncertainties Setup errors + Patient motion
+ Structural changes during treatment
= uncertainty in geometry
A direction: rescan to eliminate some of these errors

15. What can go wrong? A simple LP problem: max x + y
s.t. x+.99 y  1 x, y  0

16. A new problem
Given image A and its perturbed copy B, how can we match those? ?

17. Assignment 1
MATLAB code and data are posted on the web. The image recovery is performed via naïve local search amongst the sequence of translations and rotations (e.g., lines of the code).
0. Fill in the survey form on the next page.
Would anything change if we replace line 26 with line 27? Which one would you prefer? Explain both parts.
The translation/rotation transformation is implemented as series of operations on the intensity matrix (e.g., scan2). Write this operation in matrix-vector form, i.e., as AB+C=D for some matrices A,B,C and D.
Are any of the data structures in 2 sparse, that is, have big blocks of zeros? Can one potentially take advantage of this?
Bonus question: implement the sparse matrix-vector multiplication routine in MATLAB (may use C but no external libraries). The fastest code gets bonus points.

18. Background survey Name: Degree you pursuing (MS/PhD): Department:
1. Rank your answer from 1-”no idea” to 5-”expert level” to the following questions: how familiar are you with
Linux/Unix OS
Windows OS
MATLAB
Octave
BLAS
ATLAS C
Fortran
OpenMP
MPI
2. Circle one: was today’s class too elementary just right too complicated?
3. Reason for taking the course: required interest in the subject.
4. What is the most important thing you would like to learn in this class?