Integrating　GPU　with　CPU　on　the　same　chip　is　increasingly　common　in　current　processor　architectures　for　high　performance.　CPU　and　GPU　share　on-chip　network,　last　level　cache,　memory.　Do　not　need　to　copy　data　back　and　forth　that　a　discrete　GPU　requires.　Shared　virtual　memory,　memory　coherence,　and　system-wide　atomics　are　introduced　to　heterogeneous　architectures　and　programming　models　to　enable　fine-grained　CPU　and　GPU　collaboration.　Programming　model　such　as　OpenCL　2.0,　CUDA　8.0,　and　C++　AMP　support　these　heterogeneous　architecture　features.　Data　partition　is　one　of　the　collaboration　patterns.　It　is　essential　for　improving　performance　and　energy-efficiency　to　balance　the　data　processed　between　CPU　and　GPU.　In　this　paper,　we　first　demonstrate　that　the　optimal　allocation　of　data　to　the　CPU　and　GPU　can　provide　20%　higher　performance　than　fixed　ratio　of　20%　for　one　application.　Second,　we　evaluate　another　5　heterogeneous　applications　covering　the　latest　architecture　features,　found　the　relation　of　the　data　partitioning　with　performance.　©　Springer　Nature　Singapore　Pte　Ltd.　2018.