As you want ME Syllabus from Pune University here I’m giving you ME Syllabus Pune University:

ME Syllabus Pune University
UNIT I. Physical database design & Tuning
Database workloads, physical design and tuning decisions, Need for Tuning
Index selection: Guideline for index selection, Clustering & Indexing Tools for index selection
Database Tuning: Tuning indexes, Tuning Conceptual schema Tuning Queries &views, Impact
of Concurrency, Benchmarking
UNIT II. Distributed Databases
Introduction, Design Framework, Design of database fragmentation, The Allocation of
Fragments, Translation of global queries to fragment queries, Optimization of access queries,
Distributed Transaction Management, Concurrency Control, Reliability.
UNIT III. Advance Transaction Processing
Transaction Processing Monitors, Transactional Workflow, Real time transaction System, Long
duration Transactions, Transaction Management in Multi-databases, Distributed Transaction
Management, Main Memory Databases, and Advanced Transaction Models.
UNIT IV. Semi-Structured Data and XML
Semi-Structured Data, Introduction to XML, XML hierarchical Model, DTD & XML schema,
XML Namespace, XML query & Transformation: Xpath, XSLT, XQuery, Storage of XML data,
XMLTechnologies : DOM &SAX Interfaces X pointer, Xlink, XHTML, SOAP, WSDL,
UDDI,XML database Application.
UNIT V. Emerging Trends in Databases
Introduction, Motivation, Temporal databases, Spatial & geographic databases, Multimedia
Databases, Mobility & personal Databases
UNIT VI. Advanced Application Development
Performance Tuning, Performance Benchmarks, Standardization, E-Commerce, Legacy Systems,
Large-scale Data Management with HADOOP, Semi structured database COUCHDB:
Introduction, Architecture and principles, features

Unit – I Introduction to architectures and Computing Models
Evolution in processor development, Generic computer architecture, Data representation,
Instruction sets, data path and control, memory management, Buses and peripherals, Networking
and communication, Multiprocessor and multicomputer, multivector and SIMD systems,
PRAM and VLSI models, network properties, conditions for parallelisms, partitioning and
scheduling, program flow mechanisms, system interconnect architectures
Unit –II Performance metrics
Metrics and measures for parallel programs, Speedup performance laws, scalability analysis
approaches, Amdahl’s law, limitation, Benchmark, SIMD, MIMD Performance.
Unit – III Hardware parallelism
Processor and memory hierarchy- Advanced processor technology, superscalar and vector
processors, memory hierarchy, virtual memory, shared memory organizations, bus systems,
consistency on shared data, Pipelining- Linear and non linear pipelines, Instruction pipelines,
instruction and arithmetic pipeline design
Unit – IV Parallel and Scalable architectures
Multiprocessor and system interconnects, cache coherence and synchronization
mechanisms, multicomputer generations, message passing paradigms, Multivector architectureprinciples
of vector processing, multivector multiprocessors, compound vector processing, SIMD
organization, MIMD organization, multithread and dataflow architectures: Multithreading, fine
grained multicomputers, dataflow and hybrid architectures, Single Program-Multiple
Data(SPMD), Multiple Program, Multiple Data(MPMD), Case study of non-coherent
multiprogramming in PRAM
Unit – V Parallel programming and program development environments
Parallel programming models, parallel languages and compilers, dependence analysis and
of data arrays, code optimization and scheduling, loop parallelism and pipelining, Parallel
programming environments, synchronization and multiprocessing modes, shared variable
programs, message passing programs, mapping programs on multi-computers.
Operating system support for parallel program execution, processes and threads, parallel
programming languages-C-Linda, Fortran-90, Programming with MPI. Introduction to mapreduce.
Unit – VI Advanced Computing Architectures
Quantum Computing, Bio/Molecular Computing, Grid Computing, Neuro Computing,
Cloud Computing, Introduction to GPU parallel architecture.


ME syllabus of Applied Algorithms:

Unit I. Analysis of Algorithms
Review of algorithmic strategies, Asymptotic analysis: upper and lower complexity bounds. Identifying differences among best, average and worst Case Behaviors. Big O, little O, omega and theta notations, Standard complexity classes. Empirical measurements of performance. Time and space trade-offs in algorithms. Analyzing recursive algorithms using recurrence relations.

Unit II. Fundamental Computing Algorithms
Numerical algorithms, Sequential and binary search algorithms. Quadratic sorting algorithms and O (n log n) sorting algorithms. Algorithms on graphs and their complexities using Greedy Approach for --- Prim’s and Krushkal’s Algorithm for minimum spanning tree, Single source shortest path Algorithm, all pair shortest paths in Graph

Unit III. Approximation Algorithms
Introduction, Absolute approximation, Epsilon approximation, Polynomial time Approximation schemes, probabilistically good algorithms.

Unit IV. Geometric Algorithms
Prerequisites – Basic properties of line, intersection of line, line segment, polygon,etc. Line segment properties, detaining segment intersection in time complexity (n log n),Convex full problem – formulation, solving by Graham scan algorithm, Jarvis march algorithm; closest pair of points – problem formulation, solving by divide & conquer method.

Unit V. Linear Programming
Standard and Slack forms, formulation of problems as linear programs, simplex algorithm, duality, initial basic feasible solution. Problem formulation for – single source shortest path, maximum flow problem, Vertex cover problem, Knapsack problem.

Unit VI. Probability Based Analysis
Expectations: Introduction, Moments, Expectations of functions of more than one random variable, transform methods, moments and transforms of distributions, computation of mean time to failure, inequalities and limit theorems

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