On parallel projection of orthonormal base [Lecture]

Geometrical Pattern Feature Extraction by Projection on Haar Orthonormal Basis

The c l a s s i f i c a t i o n o f a s e t o f p a t t e r n s i s a p rob lem t h a t appears i n v e r y many f i e l d s . I n g e n e r a l , the number o f p o s s i b l e c l asses i s unknown. To d e f i n e a d i s t a n c e ( o r s i m i l a r i t y ) m a t r i x on the s e t o f p a t t e r n s , we must summarize the a v a i l a b l e da te i n the fo rm o f a f i n i t e s e t o f ...

Lecture 2: Scheduling on Parallel Machines

Parallel environment (alpha in Graham’s notation): • P parallel identical • Q uniform machines: each machine has a given speed speed i, and all jobs have a size sizej, the processing time is given by sizej/speed i • R unrelated machines: the processing time of job j on machine i is given by pi,j, without any other constraints Sometimes the number of processors is fixed: for example, P2, F2, or Pm.

Lecture 1: Scheduling on Parallel Machines

Classes of scheduling problems can be specified in terms of the three-field classification α|β|γ where • α specifies the machine environment, – P parallel identical – Q uniform machines: each machine has a given speed speed i, and all jobs have a size sizej, the processing time is given by sizej/speed i – R unrelated machines: the processing time of job j on machine i is given by pi,j, without ...

Lecture Notes on Parallel Scientific Computing

3 Model-based Programming Methods 10 3.1 Embarrassingly Parallel Computations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.1 Geometrical Transformations of Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.2 Mandelbrot Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.3 Monte Carlo Met...

Parallel Algorithms Lecture Notes