Data Management Lab

RL@Cloud RL@Cloud is a BW-Invest funded project aiming to explore new cloud designs for the sustainable use of large datasets, containing structured high-resolution measurement data.

neoDBMS - aims to explore new architectures, abstractions and algorithms for intelligent database storage capable of performing Near-Data Processing (NDP)and executing data- or compute-intensive DBMS operations in-situ. 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.

HAWprom - is a project funded by the MWK, Baden-Wuerttemberg with the goal of pursuing successful dissertations in the field of near-data processing.

Graudierten Kolleg Services Computing - is funded by the MWK, Baden-Wuerttemberg with the goal of pursuing research in the field of data-intensive infrastructures for services computing.

pimDBMS - is an special purspose equipment grant from MWK, Baden-Wuerttemberg with the goal of Processing-In-Memory infrastrcutures.

PANDAS - Programmable Appliance for Near-Data Processing Accelerated Storage. Even today, transporting data between mass storage and servers is proving more problematic for many big data / cloud applications bottleneck. In PANDAS, both the computing power and the energy efficiency of these demanding applications should be realized through the implementation a novel intelligent mass storage and the development optimized on it software be increased. The technological core is to designed and manufacture a cascadable PCI Express expansion card, having a variety of parallel flash memory banks as mass storage which in turn very quickly adapts to a modern reconfigurable Multi-processor system-on-chip (MPSoC). By the Use of Programmable Logic (FPGA) on the MPSoC High throughput, low latency data processing operations are achieved directly within the mass storage devices (so-called Near-Data Processing, NDP ). The PANDAS card, whose architecture is a unique feature can then be flexible to expand commercial server in Data centers are used. Since the PANDAS platform can be used for a wide variety of applications should, a number of software components will be developed. In particular middleware and Programming tool flows, with which new NDP applications can be created by developers without in-depth Knowledge of the hardware design.

DBTechNet is an initiative of European universities and IT-companies to set up a transnational collaboration scheme of higher level educational establishments, IT enterprises, and vocational training centres who will collaborate in order to achieve a three-fold goal, namely: