Quick Overview

GPU's higher processing power compared to a standard CPU comes at the cost of reduced data caching and flow control logic as more transistors have to be devoted to data processing. This imposes certain limitations in terms of how an application may access memory and implement flow control. As a result, implementation of certain algorithms (even trivial ones) on the GPU may be difficult or may not be computationally justified. Histogram has been traditionally difficult to compute efficiently on the GPU. Lack of an efficient histogram method on the GPU, often requires the programmer to move the data back from the device (GPU) memory to the host (CPU), resulting in costly data transfers and reduced efficiency. A simple histogram computation can indeed become the bottleneck of an otherwise efficient application. Download the Code

You can find the source code for two efficient histogram computation methods for CUDA compatible GPUs .

The methods are described in the following publications:

and . Download Source Code Quick Overview

Mutual information of two random variables is the amount of information that each carries about the other and is defined as



where H(X|Y) is the information content of random variable X if Y is known, H(X,Y) is the joint entropy of the two random variables and is a measure of combined information of the two random variables. I(X;Y) can be thought of as the reduction in uncertainty of random variable X as a result of knowing Y. The uncertainty is maximally reduced, when there is a one-to-one mapping between the two random variables and is not reduced at all if the two random variables are independent and do not provide any information about one another.

The concept of mutual information has its origins in information theory and is widely used in other disciplines. In medical image analysis, mutual information is used as a similarity measure for multi-modal registration. Download the Code

You can find the source code for efficient MI computation methods for CUDA compatible GPUs .

The methods are described in the following publication:

. Download Source Code