1. Summary Background Introduction in algorithms and applications
Why do we need parallel processing? Moore’s law. Applications.
Introduction in algorithms and applications
Methodology to develop efficient parallel (distributed-memory) algorithms 
Understand various forms of overhead (communication, load imbalance, search overhead, synchronization)
Understand various distributions (blockwise, cyclic)
Understand various load balancing strategies (static, dynamic master/worker model)
Understand correctness problems (e.g. message ordering)

2. Summary Parallel machines and architectures
Processor organizations, topologies, criteria
Types of parallel machines
arrays/vectors, shared-memory, distributed memory
Routing
Flynn’s taxonomy
What are cluster computers?
What networks do real machines (like the Blue Gene) use?
Speedup, efficiency (+ their implications), Amdahl’s law

3. Summary Programming methods, languages, and environments
Different forms of message passing
naming, explicit/implicit receive, synchronous/asynchronous sending
Select statement
SR primitives (not syntax)
MPI: message passing primitives, collective communication
Java parallel programming model and primitives
HPF: problems with automatic parallelization; division of work between programmer and HPF compiler; alignment/distribution primitives; performance implications

4. Summary Applications Grid computing
N-body problems: load balancing and communication (locality) optimizations, costzones, performance comparison
Search algorithm (TDS): use asynchronous communication + clever (transposition-driven) scheduling
Grid computing
What are grids? Why do we need them?
Application-level tools for grid programming and execution
Why is parallel computing on grids feasible?
Ibis: goals, applications, design, programming vs. deployment, communication primitives, divide&conquer parallelism, performance behavior on a grid

5. Summary Multimedia content analysis on Grids
What is Multimedia Content Analysis (MMCA) ? Imaging applications, feature vectors, low-level patterns
Why parallel computing in MMCA - and how? Need for speed and transparency
Software Platform: Parallel-Horus. Task/data parallelism, Separable Recursive Filtering, Lazy Parallelization
Example – Parallel Image Processing on Clusters
Grids and their specific problems. Promise and problems of the grid
Towards a Software Platform for MMCA on Grids. Problems with wide- area servers
Large-scale MMCA applications on Grids (TRECVID, object recognition)

6. Summary Multi/many-cores processors
Variety of platforms (core types & counts, memory architecture & sizes, parallelism layers & types, scheduling)
Performance evaluation (arithmetic intensity, Roofline model)
Programming models for multi/many-cores
Deal with performance, portability, programmability
The correlator application
Programming models for Cell, GPUs, Generic models