Supercomputer
Education and
Research Centre
Course Credits
Course Title
Hard Core: 28 Credits (All courses are compulsory)
SE 284 2:1 Numerical Linear Algebra
SE 286
2:1 Data Structures and
Programming
SE 288
3:1 Numerical Methods
SE 289
3:1 Numerical Solutions of
Differential Equations
SE 290
3:0 Modelling and Simulation
SE 292
3:0 High Performance Computing
SE 294
3:1 Data Analysis and
Visualization
SE 295
3:1 Parallel Programming
Project: 24 Credits
SE 299
0:24 Dissertation Project
0:8 For Aug-Dec Term
0:16 For Jan-Apr Term
Electives: The balance of credits to make up the
minimum of 64 required for completing the programme (all at 200 level or
higher). Electives from within/outside the department to be
taken with the approval of the DCC/Faculty Advisor.
SE 260 (JAN) 3:0
Medical Imaging
X-ray Physics, interaction of radiation with matter, X-ray
production, X-ray tubes, dose, exposure, screen-film radiography, digital
radiography, X-ray mammography, X-ray Computed Tomography (CT). Basic
principles of CT, single and multi-slice CT. Tomographic image reconstruction,
filtering, image quality, contrast resolution, CT artifacts. Magnetic Resonance
Imaging (MRI): brief history, MRI major components. Nuclear Magnetic Resonance:
basics, localization of MR signal, gradient selection, encoding of MR signal,
T1 and T2 relaxation, k-space filling, MR artifacts. Ultrasound basics,
interaction of ultrasound with matter, generation and detection of ultrasound,
resolution. Doppler ultrasound, nuclear medicine (PET/SPECT), multi-modal
imaging, PET/CT, SPECT/CT, oncological imaging, medical image processing and
analysis, image fusion, contouring, segmentation, and registration.
P K Yalavarthy
Prerequisites: Basic knowledge of system theory and Consent
from the instructor.
Bushberg, J.T., Seibert, J.A., Leidholdt, E.M. Jr., and
Boone, J.M., The Essential Physics of Medical Imaging, Second Edn, Lippincott
Williams and Wilkins Publishers, Philiadelphia, 2002.
Wolbarst, A.B., Physics of Radiology, Second Edn, Medical
Physics Publishing, Madison, WI, 2005.
Current Literature
SE 261 (AUG) 2:1
Numerical Methods in Biomedical Engineering
Modeling biosystems, role of computers in biomedical
engineering, linear biological systems, simultaneous linear algebraic
equations, Gaussian-elimination, iterative methods, examples: force balance in
biomechanics and biomedical image processing, non-linear biological systems,
Newton’s method for simultaneous non-linear equations, examples: friction
factor in catheter and receptor-ligand dynamics, dynamical biosystems,
Eigenvalue methods, numerical stability, examples: pharmacokinetics: the drug
absorption problem and laser ablation, basics of numerical solutions of ordinary
differential equations (ODE), finite difference schemes for solving partial
differential equations, initial and boundary conditions, Applications: modeling
of glucose regulation, diabetes and insulin regulation, motion of rigid body,
analysis of mass-spectra data, and separating EEG frequency components.
P K Yalavarthy
Prerequisites: Basic knowledge of numerical analysis along
with basic MATLAB programming background and consent from the instructor.
Dunn, S.M., Constantinides, A., and Moghe, P.V., Numerical
Methods in Biomedical Engineering, Academic Press, 2006.
Semmlow, J., Circuits, signals and systems for
bioengineering, Academic Press, 2005.
Current Literature.
SE 262 (JAN) 3:0
Applied and Computational Photonics
Introduction to the analytical regimes in optics, ray
tracing, electromagnetic fields, Stokes parameters and Mueller matrices,
charges and radiation damping, Maxwell’s equations and boundary conditions.
Eigenfunctions and orthogonality, solutions to boundary value problems, Method
Of Moments (MOM), Finite Difference Time Domain method (FDTD), Discrete Dipole
Approximation (DDA), T-matrix method, quantum harmonic oscillator, photon
states. Coherence, ‘how a laser works’. Introduction to nanophotonics and
applications.
Murugesan Venkatapathi
Prerequisites: PH 206 or E8 201 or SE 289 or instructor’s
consent
Ramo, Whinnery, and Van Duzer, Fields and waves in
communication electronics, Third Edn,
Jackson, Classical electrodynamics, Third edition,
Born and Wolf, Principles of optics,
Bohren and Huffman, Absorption and scattering of light by
small particles, Publishers?
Scully, M.O., and Zubairy, M.S., Quantum Optics,
Cambridge University Press, 1997.
Loudon, R., The quantum theory of light, Oxford Science
Publications, Second Edn.
SE-273 (JAN) 3:1
Processor Design
Introduction to Verilog HDL and logic synthesis. CISC
Processor Design: defining microprocessor, hardware flowchart, implementing
from flowchart, exception, control store. Microcode design RISC Processor
Design: Building datapath and controller, single cycle implementation, multi
cycle implementation, pipelined implementation, exception and hazards handling
(example: DLX Processor). Superscalar processors design: superscalar
organization, superscalar pipeline overview, VLSI implementation of dynamic
pipelines, register renaming, reservation station, re-ordering buffers, branch
predictor, and dynamic instruction scheduler etc. Simultaneous multi-threading
(SMT) design (example: Open SPARC T1). Impact of physical technology, trends in
power consumption, low power techniques, low voltage techniques, clock
distribution. Verification and test issues.
Virendra Singh
Pre-requisite: Knowledge of Digital System Design and
Computer Architecture is desirable. Consent of the Instructor is required.
Tredennick, N., Microprocessor Logic Design, Digital
Press, 1987.
Patterson, D.A., and Hennessy, J.L., Computer
Organization and Design, Morgan Kaufman Pub., N. Delhi, 2005.
Shen, J.P., and Lipasti, M.H., Modern Processor Design,
McGraw Hill, Crowfordsville, 2005.
Johnson, M., Superscalar Microprocessor Design, Prentice
Hall, Englewood Cliffs, NJ, 1991.
Chandrakasan, Bowhill, W.J., and Fox, F., Design of High
Performance Microprocessor Circuits, IEEE Press.
OpenSparc T1 manual, http://www.opensparc.net/
Current Literature
SE 284 (AUG) 2:1
Numerical Linear Algebra
Matrix Analysis: Vector and matrix norms, orthogonality,
Singular Value Decomposition, projections, CS Decomposition. Solution of
equations: Gaussian Elimination, pivoting, LU and Cholesky factorizations, LDM'
and LDL' factorizations, positive definite systems, banded systems, block
systems, Vandermonde systems and the FFT, Toeplitz systems. Orthogonalization
and Least Squares: Householder and Givens Matrices, QR factorizations, Full
Rank Least Squares(LS) Problem, Rank Deficient LS Problem. Unsymmetric
Eigenvalue problem: power methods, Hessenberg and real Schur Forms, invariant
subspace computations, QZ method. Symmetric Eigenvalue Problem: power
iterations, symmetric QR algorithm, Jacobi methods, tridiagonal methods, SVD,
Lanczos and Arnoldi methods. Iterative methods for linear systems: Jacobi and
Gauss-Seidel iterations, SOR methods, Conjugate Gradient method, Preconditioned
Conjugate Gradients. Sparse matrix methods: ordering, symbolic factorization,
numerical factorization, triangular solvers, multifrontal method, iterative
methods.
S Raha, Murugesan Venkatapathi
Golub, G., Van Loan C.F., Matrix Computations, John
Hopkins, 1996.
Saad, Y., Iterative Methods for Sparse Linear Systems,
Second Edition, SIAM, 2003.
Press, W.H., Teukolsky, S.A., Vetterling, W.T., and
Flannery, B.P., Numerical Recipes in C/FORTRAN, Prentice Hall of India, New
Delhi, 1994.
SE 286 (AUG) 2:1
Data Structures and Programming
Review of Programming with introduction to OOP with C++,
time and space complexity. Elementary data structures: Arrays, Stack, Queues,
Heaps, Priority Queues, Vectors and Sparse Matrices and related algorithms.
Usage and concepts of frequently used sorting, searching, merging, Hashing Techniques.
Introductory graph algorithms, trees including AVL, B+, Red-Black Trees, Tries and Suffix trees: usage
and application, Usage/Application of String Algorithms. Introduction to Greedy
Algorithms, introduction to Spatial Data Structures.
Virendra Singh
Cormen, T.H., Leiserson, C.E., and Rivest, R.L.,
Introduction to Algorithms, The MIT Press and McGraw-Hill Book Company. (Indian
Edition Available)
Stroustrup, B., C++ Programming Language, Addison Wesley.
(Indian Edition Available)
Sahni Sartaj K., Data Structures, Algorithms, and
Applications in C++, McGraw Hill.
Kruse, R.L., and Tondo, C.L., Data Structures and Program
Design, Prentice Hall of India 1997.
Aho, A.V. Hopcroft and Ulman, J.D., Data Structurs and
Algorithms.
Heilerman, G.L., Data Structures, Algorithms and Object
oriented Programming, McGraw-Hill Intl Edn, 1996.
Samet Hanan, The Quadtree and Related Hierarchical Data
Structures, ACM Computing Surveys, Vol.16-2, pp.187-229, 1986.
SE 288 (AUG) 3:1
Numerical Methods
Root finding: Functions and polynomials, zeros of a
function, roots of a nonlinear equation, bracketing, bisection, secant, and
Newton-Raphson methods. Interpolation, splines, polynomial fits, Chebyshev
approximation. Numerical Integration and Differentiation: Evaluation of
integrals, elementary analytical methods, trapezoidal and Simpson's rules,
Romberg integration, Gaussian quadrature and orthogonal polynomials,
multidimensional integrals, summation of series, Euler-Maclaurin summation
formula, numerical differentiation and estimation of errors. Optimization:
Extremization of functions, simple search, Nelder-Mead simplex method, Powell's
method, gradient-based methods, simulated annealing. Complex analysis: Complex
numbers, functions of a complex variable, analytic functions, conformal
mapping, Cauchy's theorem. Calculus of residues. Fourier and Laplace
Transforms, Discrete Fourier Transform, z transform, Fast Fourier
Transform(FFT), multidimensional FFT.
A Mohanty and P K Yalavarthy
Kreyszig, E., Advanced Engineering Mathematics, John
Wiley and Sons, Seventh Edn, 1993
Press, W.H., Teukolsky, S.A., Vetterling, W.T., and
Flannery, B.P., Numerical Recipes in C/FORTRAN, Prentice Hall of India, New
Delhi, 1994.
Krishnamurthy, E.V., and Sen, S.K., Numerical Algorithms,
Affiliated East-West Press, New Delhi, 2001.
Borse, G.J., Numerical Methods with MATLAB: A Resource
for Scientists and Engineers, PWS Publishing Co., Boston, 1997.
SE 289 (JAN) 3:1
Numerical Solutions of Differential Equations
Ordinary differential equations: Lipschitz condition,
solutions in closed form, power series method. Numerical methods: error
analysis, stability and convergence, Euler and Runge-Kutta methods, multistep
methods, Adams-Bashforth and Adams-Moulton methods, Gear's open and closed
methods, predictor-corrector methods. Sturm-Liouville problem: eigenvalue
problems, special functions, Legendre, Bessel and Hermite functions. Partial
differential equations: classification, elliptic, parabolic and hyperbolic
PDEs, Dirichlet, Neumann and mixed boundary value problems, separation of
variables, Green's functions for inhomogeneous problems. Numerical solution of
PDEs: relaxation methods for elliptic PDEs, Crank-Nicholson method for
parabolic PDEs, Lax-Wendroff method for hyperbolic PDEs. Calculus of variations
and variational techniques for PDEs, integral equations. Finite element method
and finite difference time domain method, method of weighted residuals, weak
and Galerkin forms, ordinary and weighted/general least squares. Fitting models
to data, parameter estimation using PDEs.
A Patel, P K Yalavarthy and A Mohanty
Arfken, G.B., and Weber, H.J., Mathematical Methods for
Physicists, Sixth Edition, Academic Press, 2005.
Press, W.H., Teukolsky, S.A., Vetterling, W.T., and
Flannery, B.P., Numerical Recipes in C/FORTRAN – The art of Scientific
Computing, Second Edn, Cambridge University Press, 1998.
Lynch, D.R., Numerical Partial Differential Equations for
Environmental Scientists and Engineers – A First Practical Course, Springer,
New York, 2005.
SE 290 (JAN) 3:0
Modelling and Simulation
Statistical description of data, data-fitting methods,
regression analysis, analysis of variance, goodness of fit. Probability and
random processes, discrete and continuous distributions, Central Limit theorem,
measure of randomness, Monte Carlo methods. Stochastic Processes and Markov
Chains, Time Series Models. Modelling and simulation concepts, Discrete-event
simulation: Event scheduling/Time advance algorithms, verification and validation
of simulation models. Continuous Simulation: Modelling with differential
equations. Example models, Bond Graph Modelling, Population Dynamics Modelling,
System Dynamics PAC learning model.
S Raha and A Mohanty
Banks, J., Carson, J.S., and Nelson, B., Discrete-Event
System Simulation, Second Edn, Prentice Hall of India, 1996.
Winston, W.L., Operations Research: Applications and
Algorithms, Third Edn, Duxbury press, Belmont, California, 1994.
Cellier, F.E., Continuous System Modelling, Springer Verlag,
1991.
Vidyasagar, M., Theory of Learning and Generalization:
With Applications to Neural Network and Control Systems, Springer Verlag, 1997.
Peter E Kloeden Eckhard platen, Numerical Solution of
stochastic differential equations, Springer Verlog, 1999.
Peter
E Kloeden Eckhard platen, Henri Schurz, Numerical Solution of SDE through
Computer experiments Springer Verlog, 1994.
SE 292 (AUG) 3:0
High Performance Computing
Introduction to Computer Systems: Processors, Memory, I/O
Devices; Cost, timing, and scale (size) models. Program Execution: Process,
Virtual Memory, System Calls, Dynamic Memory Allocation. Machine-Level View of
a Program, typical RISC instruction set and execution, Pipelining. Performance
issues and Techniques, Cost and Frequency Models for I/O, paging, and caching.
Temporal and spatial locality. Typical Compiler Optimizations. Identifying
program bottlenecks – profiling, tracing. Simple high-level language
optimizations – locality enhancement, memory disambiguation. Choosing
Appropriate Computing Platforms: benchmarking, cost-performance issues, etc.
Parallel Computing: Introduction to parallel Architectures and Interconnection
Networks, communication latencies. Program parallelization: task partitioning
and mapping, data distribution, Message passing, synchronization and deadlocks.
Distributed memory programming using MPI/PVM. Shared memory parallel
programming. Multithreading.
M Jacob,
Dowd, K., High performance Computing, O’Reilly Series,
1993.
Culler, D., and Singh, J.P., Parallel Computer
Architecture: A Hardware/Software Approach. Morgan Kaufmann Pub., 1999.
Gropp, W., Lusk, E., and Skjellum, A., Using MPI:
Portable Parallel Programming with the Message-passing Interface, MIT Press,
1997.
SE 293 (AUG) 3:1
Topics in Grid Computing
Introduction: Motivation, definitions, evolution of the
grid, differences with similar efforts (Meta, cluster, heterogeneous,
Internet). Examples of usage. The Earliest Grid Motivations: High Throughput
computing using non-dedicated workstations – Condor. The Building Blocks of
Grid: The Globus toolkit, Security - Kherberos vs Globus GSI, Information
Services – NWS. HPC and Grids: Scheduling HPC applications in Grids -
Scheduling Parameter sweep applications, Metascheduling, Rescheduling. Advanced
topics: Data Management in Grids, fault tolerance and detection, grid
applications, grid simulation, grid economy, grid RPC, others.
Extensive Literature Study.
S Vadhiyar
Prerequisite: At least basic level courses on operating
system and architecture. Instructor’s approval is needed.
Foster, I., and Kesselman, C. (Eds), The Grid: Blueprint
for a New Computing Infrastructure Second Edn, Morgan Kaufmann, 2003. ISBN:
1-558-60933-4.
Berman, F., Fox, G., Hey, T. (Eds), Grid Computing:
Making The Global Infrastructure a Reality, John Wiley and Sons, 2003. ISBN:
0-470-85319-0.
Nabrzyski, J., Schopf, J.M., Weglarz, J. (Eds), Grid
Resource Management: State of the Art and Future Trends, Kluwer Academic
Publishers, 2003. ISBN: 1-402-07575-8.
SE 294 (JAN) 3:1
Data Analysis and Visualization
Data pre-processing, data representation, data reconstruction, visualization pipeline, isosurfaces, volume rendering, vector field visualization, applications in biology and medicine, OpenGL, visualization toolkit, linear models-estimation and testing, principal components, clustering, multidimensional scaling, mining on large data sets, information visualization. Vijay Natarajan
Hansen, C.D., and Johnson, C.R., Visualization Handbook,
Academic Press, 2004.
Ware, C., Information Visualization: Perception for
Design, Morgan Kaufmann, Second Edn, 2004.
Current literature
SE 295 (JAN) 3:1
Parallel Programming
Introduction: Scope of parallel computing, challenges, performance
metrics, parallel architecture models, parallel programming paradigms,
algorithm models. Principles of parallel algorithm design: decomposition
techniques, data distribution methods, mapping techniques for load balancing.
Programming using the message passing paradigm: Principles of message-passing
programming, The Message Passing Interface (MPI): MPI-1, Collective
communications, MPI-2, Parallel I/O; Shared memory programming: OpenMP;
Parallel applications: Laplace equation, molecular dynamics. Parallel dense
linear algebra: Gaussian elimination, iterative methods. Parallel sparse linear
algebra: Cholesky factorization, graph
partitioning, sparse iterative methods, graph coloring
and others. Other topics: Parallel FFT. Parallelism in Bioinformatics and other
Applications, Scheduling on parallel systems and other advanced topics.
S Vadhiyar and
Pre-requisite(s): High Performance Computing and
preferably Numerical Linear Algebra and Numerical Methods.
Grama, Gupta, A., Karypis, G., Kumar, V., Introduction to
Parallel Computing, Addison Wesley, 2003. ISBN: 0-201-64865-2
Dongarra, J., Foster, I., Fox, G., Kennedy, K., White,
A., Torczon, L., Gropp, W. (Eds), The Sourcebook of Parallel Computing, Morgan
Kaufmann, 2002. ISBN: 1-558-60871-0.
Dongarra, J., Duff, I., Sorensen, D.C., Van der Vorst,
H.A., Numerical Linear algebra for High Performance Computers, 1998. ISBN
–0-89871-428-1.
SE 297 (JAN) 2:1
Topics in Embedded Computing
Introduction to embedded processing, dataflow
architectures, architecture of embedded SoC platforms, dataflow process
networks, compiling techniques/optimizations for stream processing,
architecture of runtime reconfigurable SoC platforms, simulation, design space
exploration and synthesis of applications on runtime reconfigurable SoC
platforms.
S K Nandy
Pre-requisites: Basic knowledge of digital electronics,
computer organization and design, computer architecture, data structures and
algorithms, and consent of instructor.
Current literature.
IEEE transactions in VLSI systems.
IEEE transactions on Multimedia Systems.
ACM Transactions on embedded computing systems.
Technical reports and design notes from micro-electronics
industries and other academic institutions.
SE 299 (AUG) 0:24
Dissertation Project
This includes the analysis, design of hardware/software
construction of an apparatus/instruments and testing and evaluation of its
performance. The project work is usually based on a scientific/engineering
problem of current interest. Every student has to complete the work in the
specified period and should submit the Project Report for final evaluation.
Faculty
SE 301 (AUG) 2:0
Bioinformatics
Biological Databases: Organisation, searching and
retrieval of information, accessing global bioinformatics resources using
internet links. Introduction to Unix operating system and network
communication. Nucleic acids sequence assembly, restriction mapping, finding
simple sites and transcriptional signals, coding region identification, RNA
secondary structure prediction. Similarity and Homology, dotmatrix methods,
dynamic programming methods, scoring systems, multiple sequence alignments,
evolutionary relationships, genome analysis. Protein physical properties,
structural properties – secondary structure prediction, hydrophobicity
patterns, detection of motifs, structural database (PDB). Genome databases,
Hands on experience will be provided.
S Ramakumar and K Sekar
Gribkov, M., and Devereux, J. (Eds), Sequence Analysis
Primer, Stockton Press, 1991.
Mount, D.W., Bioinformatics: Sequence and Genome
Analysis, Cold.
Baxevanis, A.D., and Ouellette, B.F.F. (Eds),
Bioinformatics: A practical guide to the analysis of the genes and proteins,
Wiley-Interscience, 1998.
SE 302 (JAN) 2:0
Computational Approaches to Drug Discovery
Introduction to the process of drug and vaccine
discovery, principles of drug action, drug and target structures, brief
introduction to systems biology, pharmacology and chemoinformatics. Use of
genomics and proteomics for understanding diseases at the molecular level.
Sequence-structure-function relationship in proteins, strategies for target
identification and validation, protein structure prediction, molecular modeling
protein-ligand interactions, structure-based ligand design. Lead
identification, design and lead optimization. Challenges in drug and vaccine
discovery. Relevant algorithms and topics from current literature.
Nagasuma Chandra
Mount, D.W., Bioinformatics – Sequence and Genome
Analysis, Cold Spring Harbor Laboratory Press, 2001.
Mannhold, R., Kubinyi, H., Timmerman, H. (Eds),
Bioinformatics – From Genomes to Drugs Vol.I & II, Wiley - VCH, 2002.
Flower, D.R. (ed.), Drug Design – Cutting Edges Approaches,
Royal Society of Chemistry, 2002.
SE 303 (AUG) 2:0
Chemoinformatics
Exploring current chemoinformatics resources for
synthetic polymers, pigments, pesticides, herbicides, diagnostic markers,
biodegradable materials, biomimetics. Primary, secondary and tertiary sources
of chemical information. Database search methods: chemical indexing, proximity
searching, 2D and 3D structure and substructure searching. Introduction to
quantum methods, combinatorial chemistry (library design, synthesis and
deconvolution), spectroscopic methods and analytical techniques. Analysis and
use of chemical reaction information, chemical property information,
spectroscopic information, analytical chemistry information, chemical safety
information. Representing intermolecular forces: ab initio potentials,
statistical potentials, forcefields, molecular mechanics.
Debnath Pal
Current Scientific Literature and Web lectures:
http://serc.iisc.ernet.in/~dpal/lec tures.html.
Maizell, R.E., How to find Chemical Information: A guide
for Practicing Chemists, Educators, and students, John Wiley and Sons, 1998.
ISBN 0-471-12579-2.
Gasteiger, J., and Engel, T., Chemoinformatics. A
Textbook, Wiley-VCH, 2003. ISBN: 3-527-30681-1
SE 384 / HE 384 (AUG) 3:0
Quantum Computation
Foundations of quantum theory. States, observables,
measurement and unitary evolution. Spin-half systems and photon polarisations,
qubits versus classical bits. Pure and mixed states, density matrices.
Extension to positive operator valued measures and superoperators. Decoherence
and master equation. Quantum entanglement and
Apoorva Patel
Nielsen, M.A., and Chuang, I.L., Quantum computation and
quantum information, Cambridge University Press, 2000.
Preskill, J., Lecture notes for the course on quantum
computation. http://www.theory.caltech.edu/people/preskill/ph229
Bouwmeester, D., Ekert, A., and Zeilinger, A. (Eds), The
physics of quantum information, Springer, 2000.
Peres, A., Quantum theory: Concepts and methods, Kluwer
Academic, 1993.