Memory Management Strategies in CPU/GPU Database Systems: A Survey

Iya Arefyeva, David Broneske, Gabriel Campero Durand, Marcus Pinnecke, Gunter Saake

Abstract: GPU-accelerated in-memory database systems have gained a lot of popularity over the last several years. However, GPUs have limited memory capacity, and the data to process might not fit into the GPU memory entirely and cause a memory overflow. Fortunately, this problem has many possible solutions, like splitting the data and processing each portion separately, or storing the data in the main memory and transferring it to the GPU on demand. This paper provides a survey of four main techniques for managing GPU memory and their applications for query processing in cross-device powered database systems.