Go to Dana Petcu's homepageDANA PETCU'S LECTURE DESCRIPTIONS:
For: PhD students, Semester 1
Lectures: 3h/week
What is grid computing. Its benefits. Grid applications. Architecture and standards: SOA, SOAP, OGSA, OGSI, WSDL, WSRF. Globus components. MDS, GRIS, GRAM, GIIS, GASS, RSL, GridFTP. Getting started with developments in C++. Programming examples using Java. Grid service development: specifying, coding, building, packing, deploying, testing. Major features of grid services: factory, service data elements, life cycle, notification. Designing grid applications. Application examples: lottery, small blue, hello-world. Case study: a bulletin service application. Developing a portal. Overview of grid computing products and tools. Special issues: grid security, data management, information and workload virtualization. Grid projects in research and industry. Labs with Globus Toolkit 4
For: PhD students, Semester 1
Lectures: 1h/week
Stages of PhD studies. Departament's research projects. Journal classification, conference selection, textbooks vs. research books. Rules in writing scientific articles and reports. Research funding. Content and structure of a PhD thesis. Categories of research: fundamenta, applied, experimental. Researcher's activitis. Reviews.
For: Master students in Distributed and Parallel Computing, Semester 1
Lectures: 2h/week Labs: 1h/week
What is a Distributed System. Design and Middleware. Communication in Client-Server model. Remote Procedure Call. Group Communication. Clock synchronization. Distributed Mutual Exclusion. Deadlocks. Elections. Fault Tolerance. Atomic Transactions. Distributed Systems Models. Real Time Systems. Moden Technologies for Distributed Systems. P2P. Grids. Ubiquitous Computing. Virtualization. Service oriented architectures. Web Services. Web 2.0 and Web Services. Data and Services. Service Component Architecture. Google, Amazon and Public Web Service. Sockets in Java. Remote Methods Invocation. Java NIO. Web Services with Eclipse WTP, J2EE, Apache SOAP, Apache Axis, Ajax, PHP, JXTA, XFire and Spring.
For: Master students in Distributed and Parallel Computing, Semester 2
Lectures: 2h/week Labs: 1h/week
Hardware organization of a parallel computer. Why parallel computing. Architectural classification. Theory of parallel computing: performance measurements, paradigms and models -- shared memory and distributed memory. Inter-connection networks and message passing. Parallel algorithms: building parallel codes and examples -- sorting, linear algebra. Labs with PVM, MPI, OpenMP, mpC, and Pthreads.
For: Undergarduate students in Computer Science, Semester 6
Lectures: 2h/week Labs: 2h/week
Geometry of visualization techniques: projections, 3D transformations,visualization coordinate system. Drawing basic graphical elements: theprinciples of incremental drawing with applications to lines, circles, planecurves, spatial curves, polynomial surfaces. Clipping. Models of 3D objects.Rendering: algorithms for visible lines and surface drawing, illumination,shadows, filling, textures, fractals, colors. Ray tracing. Animation. Labs withOpenGL, VRML and PovRay.
Currently NOT ACTIVE
Lectures: 2h/week Labs: 2h/week
Classification. Short history of mathematical software. Scientific modeling,Software design. Numerical software: numerical analysis and numerical dataprocessing, numerical problems, errors, available and reusable software.Computer Algebra Systems: evaluation, data types, limitations. Expert systemsand problem solving environments. Educational software. Labs with Maple, Matlaband MathCad.