Effective parallel programming for GPUs requires careful attention to several factors, including ensuring coalesced access of data from global memory. There is a need for tools that can provide feedback to users about statements in a GPU kernel where non-coalesced data access occurs, and assistance in fixing the problem. In this paper, we address both these needs. We develop a two-stage framework where dynamic analysis is first used to detect and characterize uncoalesced accesses in arbitrary PTX programs. Transformations to optimize global memory access by introducing coalesced access are then implemented, using feedback from the dynamic analysis or using a model-driven approach. Experimental results demonstrate the use of the tools on a number of benchmarks from the Rodinia and Polybench suites.