GPUKV: An Integrated Framework with KVSSD and GPU Through P2P Communication Support

When data is loaded from a key-value store to the GPU in a conventional GPU-driven computing model, it entails the overhead of all the heavy I/O stacks of the key-value store and file system. This paper presents GPUKV, a GPU-driven computing framework that eliminates the aforementioned overhead with less host-side usage of resources such as CPU and memory. GPUKV has the following three features: (i) GPUKV provides a key-value store abstraction to the GPU; (ii) In GPUKV, when loading data from the key-value store to the GPU, it is performed through PCIe peer-to-peer (P2P) communication without copying to the user and kernel space memory; and (iii) GPUKV uses KVSSD, which implements a key-value store inside an SSD, completely eliminating the interaction with the key-value store and file system for P2P communication. We have developed GPUKV with a KVSSD implemented on the Cosmos+ OpenSSD platform in a Linux environment. Our extensive evaluations demonstrate that GPUKV improves execution time by up to 18.7 times and reduces host CPU cycle usage by up to 175 times compared to conventional CPU-based GPU computing models.