With advances in semiconductor technologies, it has nowadays become economical to produce combinations of modern semiconductor storage (e.g., Non-Volatile Memories) and powerful compute-units (FPGA, GPU, many-core CPUs) co-located on, or close to, the same chip - yielding intelligent storage devices. Data movements have become a limiting factor in times of exponential data growth, since they are blocking, frequent, and impair scalability. However, existing solution approaches are mainly based on 40-year old architectures, following the paradigm of transporting data to the processing elements. This procedure has both time as well as energy penalties. The “memory wall” and the “von Neumann bottleneck” amplify the negative performance impact of those deficiencies. The present project proposal aims to explore new architectures, abstractions and algorithms for intelligent database storage capable of performing Near-Data Processing (NDP). We target intelligent storage devices, comprising Non-volatile Memories or next-generation 3D-DRAM (such as the HMC), as well as the use of FPGAs as computational-units. We intend to investigate the following research questions: 1) Support for NDP in update-environments and hybrid-workloads. 2) Support for NDP in DBMS on Non-volatile Memories and NDP-support for declarative data layouts. 3) NDP use of shared virtual memory.

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