This thesis is about general purpose computing on the graphics processor. The reason why this is important is because of the performance advantages that can be achieved in ordinary applications by using the GPUs programmability and performance. The problem investigated is the use of a complex data-structure, namely linked lists, and what their possible benefits are when run on the GPU. I also wanted to investigate if it was viable to implement a complex data-structure on a GPU. Implementations was made of the linked list both on the GPU and on the CPU and then measurements of the performance of doing different linked list operations was conducted. Also tests was made to measure the quality of the output. I was surprised to see that the GPU performed bad when compared to the CPU on all of the linked-list operations but the quality testing showed that the GPUs and CPUs output were the same. Testing of what parts of the GPU application that caused the bad performance showed that it was the initiation of the application. I also found out that it was not that hard to learn how to program applications for the GPU except for when learning the new programming model. To conclude it can be said that my first investigation showed that linked lists does not run faster on the GPU than on the CPU but the quality is sufficient. My second investigation about the viability to use data-structures on the GPU showed that it was much easier than I expected and therefore viable if you can tolerate the bad performance.