Today's computer systems often contains several different processing units aside from the CPU. Among these the GPU is a very common processing unit with an immense compute power that is available in almost all computer systems. How do we make use of this processing power that lies within our machines? One answer is the OpenCL framework that is designed for just this, to open up the possibilities of using all the different types of processing units in a computer system. This thesis will discuss the advantages and disadvantages of using the integrated GPU available in a basic workstation computer for computation of image processing and sorting algorithms. These tasks are computationally intensive and the authors will analyze if an integrated GPU is up to the task of accelerating the processing of these algorithms. The OpenCL framework makes it possible to run one implementation on different processing units, to provide perspective we will benchmark our implementations on both the GPU and the CPU and compare the results. A heterogeneous approach that combines the two above mentioned processing units will also be tested and discussed. The OpenCL framework is analyzed from a development perspective and what advantages and disadvantages it brings to the development process will be presented.