General-purpose computing on graphics processing units (GPGPU) is now widely used in many application domains. However, programming for GPGPU is challenging due to its peculiar performance characteristics and still being done either in low-level languages or through libraries (e.g., those for matrix computation and machine learning). This talk discusses the performance challenges of using objects in GPGPU programming from the viewpoint of memory management, and the efficient mechanisms to support objects.