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The fast development of acceleration architectures and applications has made heterogeneous computing the norm for high- performance computing. The cost of high volume data movement to the accelerators is an important bottleneck both in terms of application performance and developer productivity. Memory management is still a manual task performed tediously by expert programmers. In this paper, we develop a compiler analysis to automate memory management for heterogeneous computing. We propose an optimization framework that casts the problem of detection and removal of redundant data move- ments into a partial redundancy elimination (PRE) problem and applies the lazy code motion technique to optimize these data movements. We chose OpenMP as the underlying parallel programming model and imple- mented our optimization framework in the LLVM toolchain. We evalu- ated it with ten benchmarks and obtained a geometric speedup of 2.3×, and reduced on average 50% of the total bytes transferred between the host and GPU.