2022 SYLLABUS for course PHYD57,  Advanced Computational Methods in Physics 
Lecturer: prof. Pawel Artymowicz (pawel@utsc.utoronto.ca; please put PHYD57 in
the subject line and make sure the address is as shown, otherwise mail may be 
misplaced and not answered)
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Lectures  (L1-L12, 2 hrs with 10 min break) on Tuesdays 14:00-16:00
Tutorials (T1-T10) on Tuesdays, 17:00-18:00, on days listed below.
Meetings on zoom, login via Quercus.
Deadlines for 4 sets of assignments/projects are denoted A1-A4 at 2pm. 
Expect to see them posted 7-14 days before the deadline.
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   Jan                Feb                     Mar                   Apr  
 11  L1            1  L4, T3, A1          1  midterm*, L7     5 L12, T10, A4 
 18  L2, T1        8  L5, T4,             8  L8, T6          29 Fri 2:00-5:00
 25  L3, T2       15  L6, T5             15  L9, T7             Final Exam**
                  17  A2                 20  A3 
                  22  reading week       22  L10, T8   
                                         29  L11, T9   
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*) midterm in class, 1st hour of lecture 7 (14:05-15:00) on 1 March
**) due time for writeups & 30 min exam presentations (zoom) on 29 Apr.  Notify 
	prof. of any time conflict. 
 
Syllabus is subject to small changes. Please download the updates every week. 

1	Structure and scope of the course
	Syllabus of PHYD57
	Numerical Comp. in Physical Sciences: History and Contemporary efforts
	
2	HPC: need for speed, why and how
	History and modernity: microprocessors, Unix, Linux, and Internet
	Intro to Linux (CentOS)
	Command line interface and shells (bash and tcsh)
	Text editors: vi, nano, gedit, micro, ...

3	Connectivity (ssh & sftp, traceroute & ping)
	Securing your system against break-ins via /etc/hosts.deny
	Basic Linux commands (cd, ls, ps, cd, &, bg, fg, alias, setenv,
		output redirection to file via > , rm)
	Getting more info: manual pages (man), -h, --help, or -help modifiers
	Recommended compilers: GNU: gcc, gfortran; Intel: icc, ifort; 
		PGI: pgcc, pgf95
	Simple program in C, Fortran95, Python, Matlab and IDL 
		(Schoerghofer book p.30)
	More complex program, example of HPC:
		2nd order Laplace operator stencil for diffusion equation
	Speed comparison of C/F95 with Python & Numpy: why we learn HPC

4	C and Fortran 95 - compilers, basic usage  
	Numerical puzzle of 711 - learning C and Fortran
	Kruskal counts and their connection to linked lists
	Coding Kruskal counts trick in Python and Fortran 
	C: Language overview, compilers
	Integration with Python: calling C from Python
	examples of programs
		
5	More Fortran
	Examples of programs: Init. value problems for ODEs
	More C 
	Parallel execution of programs on CPU and MIC
 	Parallel implementations: diffusion and wave equation
	Modern computing and the success of Integrated Circuits
	Barriers to single-thread comuting and Moore's law

6-7	Multi-dimensional arrays in C vs. Fortran
	Bottlenecks: Computation vs. RAM bandwidth (on CPU)
	An example program in C and Fortran. 	
	Parallelization via OpenMP in C and F
	Segmentation faults due to limited stack

8	Brief intro to a choice of Final Projects
	Intro to parallellism of supercomputing clusters
	CPU and MIC (many integrated cores, Xeon Phi)
	Calling C functions from Python
	Graphics in Fortran and C: DISLIN library 

9 	Parallel implementations: diffusion and wave equation
	Linked lists, nearest neighbor search 
	Computations with CPU vs. MIC vs. CUDA 
	Supercomputing 
	Particle disks on MIC cluster
	Optically thick disk calculation (IRI) 
	N-body integration methods and implementations

10	Interaction of protoplanets with 3D particle and gas disks
	Fluids by Eulerian vs. Lagrangian methods: CPU and GPU. 
	Incompressible flow alorithm (fluidsGL)
	Fourier integral & discrete transform. FFT, N lnN scaling.  
	Gravitational potential by FFT convolution
	Aliasing and zero-padding
	Examples of simple GPU programs written in CUDA Fortran
	
11	Discussion of chosen assignment problem solutions 
	Examples of simple GPU programs in CUDA C/C++
	CUDA computation of tetrahedrons puzzle & Laplacians
	Introduction to MPI 	
	Smoothed Particle Hydrodynamics implementation 
	
12	Machine Learning, Artificial Intelligence, Neural Networks
	Gershenfeld "The Nature of Mathematical Modeling" (1999)
	Papers on NNs in spectra classifications, exoplanet research
	Optimum Search: Simplex (Nader-Mead). Overfitting problem.
	What are NNs and why do they work despite dimensionality curse
	Programming: Automatic vectorization and compiler reports
	Further details and hints on four final projects