General-purpose computation on graphics processing units (GPGPU) has great potential to accelerate many scientific models and algorithms. However, since some problems are considerably more difficult to accelerate than others, ascertaining the effort required to accelerate a particular problem is challenging. Through the acceleration of three typical scientific problems, seven problem attributes have been identified to assist in the evaluation of the difficulty of accelerating a problem on a GPU. These attributes are inherent parallelism, branch divergence, problem size, required computational parallelism, memory access pattern regularity, data transfer overhead, and thread cooperation. Using these attributes as difficulty indicators, an initial problem difficulty classification framework has been created that aids in evaluating GPU acceleration difficulty. The difficulty estimates obtained by applying the classification framework to the three case studies correlate well with the actual effort expended in accelerating each problem. [ABSTRACT FROM AUTHOR]