Parallelization and Monte Carlo Techniques in Data Processing

Matrix multiplication and singular value decomposition can be parallelized using the map-reduce framework./nNew techniques like Monte Carlo techniques allow for parallelization of complex problems by dividing the data among multiple nodes./nBoosting techniques and particle filter approaches are examples of parallelization methods for complex stochastic problems./nExplaining complex concepts in simpler terms can often reveal their underlying simplicity.