The general purpose graphic computing units (GPGPUs) have become the new platform for high performance computing due to their massive parallel computing power, and in recent years more and more high performance database research has placed focus on GPU database development. However, today's GPU database researches commonly inherit ROLAP (relational OLAP) model, and mainly address how to realize relational operators in GPU platform and performance tuning, especially on GPU oriented parallel hash join algorithm. GPUs have higher parallel computing power than CPUs but less logical control and management capacity for complex data structure, therefore they are not adaptive for directly migrating the in-memory database query processing algorithms based on complex data structure and memory management. This paper proposes a GPU vectorized processing oriented hybrid OLAP model, semi-MOLAP, which combines direct array access and array computing of MOLAP with storage efficiency of ROLAP. The pure array oriented GPU semi-MOLAP model simplifies GPU data management, reduces complexity of GPU semi-MOLAP algorithms and improves their code efficiency. Meanwhile, the semi-MOLAP operators are divided into co-computing operators on CPU and GPU platforms to improve utilization of both CPUs and GPUs for higher query processing performance.