In solving advanced computational problems, GP-GPUs (General-Purpose Graphics Processing Units) have gained prominence in recent years. The adoption of GPUs not only for computer graphics allows to exploit their huge compute performance and high level of parallelism to lighten the CPU from burdensome executions. Aim of this thesis is to analyze automatic methodologies to help developers to find code for acceleration, studying ways to identify functions amenable to GPU acceleration without prior knowledge or assumption on the code-base. The first parameter useful in identifying such loops is Arithmetic Intensity (AI). The higher is the value, the most likely the code will benefit from GPU offloading.