Supercomputer Education and
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
SE 288 3:1 Numerical Methods
SE 289 3:1 Numerical Solutions of
SE 290 3:0 Modelling and Simulation
SE 292 3:0 High Performance Computing
SE 294 3:1 Data Analysis and
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
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.
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.
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.
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
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.
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/
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.
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
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.
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.
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.
Hansen, C.D., and Johnson, C.R., Visualization Handbook, Academic Press, 2004.
Ware, C., Information Visualization: Perception for Design, Morgan Kaufmann, Second Edn, 2004.
SE 295 (JAN) 3:1
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.
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
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.
SE 301 (AUG) 2:0
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
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.
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
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.
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
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
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.