GPGPUs are now commonly used as co-processors of CPUs for the computation of data parallel and throughputintensive algorithms. However, memory available in GPGPUs is limited for many applications of interest; there is a continuous demand for increased memory of such applications. Several techniques like multi-steaming or pinned memory are frequently employed to mitigate these issues to some extent. However, these techniques either suffer from latency overhead or increase programming complexity. GPUdmm uses GPU DRAM as a cache of CPU; key problems in this design are inefficient memory access data-path and tag access overhead. In this context, we present CAMO, a novel cache memory organization for GPGPUs which addresses the limitations of pinned memory technique and GPUdmm. First, it uses GPU DRAM as a victim cache of LLC that improves the performance by delivering data faster to the SMs. Second, it uses ATCache, a CPU based DRAM cache tag management technique. ATCache reduces the number of DRAM cache accesses. We implement CAMO within the GPGPU-Sim framework and show that its average performance - when compared with pinned memory - increases by a factor of 1.87x and the peak performance growth being 4.67x. In addition, CAMO outperforms GPUdmm on an average by a factor of 15.9% and maximum speedup by a factor of 80%. © 2018 IEEE.