http://dl.gi.de/handle/20.500.12116/11569 Thu, 23 Jul 2026 04:07:42 GMT 2026-07-23T04:07:42Z http://dl.gi.de/handle/20.500.12116/11720 Dissertationen Wed, 01 Jan 2014 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11720 2014-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11726 WattDB - A Journey towards Energy Efficiency Schall, Daniel; Härder, Theo Due to their narrow power spectrum between idle and full utilization [2], satisfactory energy efficiency of servers can only be reached in the peak-performance range, whereas energy efficiency obtained for lower activity levels is far from being optimal. Hence, this hardware property obviates a desired energy proportionality or minimal energy use for the entire range of system utilization. To approximate energy proportionality for all activity levels, we developed various versions of WattDB, a distributed DBMS, which runs on a dynamic cluster of wimpy computing nodes. In this survey, we sketch important design decisions and implementation steps towards the final state of WattDB. For these reasons, we discuss our findings on a cluster with dedicated storage nodes and static data allocation, on dynamic data repartitioning and allocation, and on a dynamic cluster where each node can serve as storage and processing node in a symmetric way. Our experiments show that WattDB dynamically adjusts to the workload present and reconfigures itself to satisfy performance demands while keeping its energy consumption at a minimum. Finally, we compare the performance and energy results of the WattDB software running on the cluster of wimpy nodes with that of a brawny server. Wed, 01 Jan 2014 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11726 2014-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11727 The Design and Implementation of CoGaDB: A Column-oriented GPU-accelerated DBMS Breß, Sebastian Nowadays, the performance of processors is primarily bound by a fixed energy budget, the power wall. This forces hardware vendors to optimize processors for specific tasks, which leads to an increasingly heterogeneous hardware landscape. Although efficient algorithms for modern processors such as GPUs are heavily investigated, we also need to prepare the database optimizer to handle computations on heterogeneous processors. GPUs are an interesting base for case studies, because they already offer many difficulties we will face tomorrow.In this paper, we present CoGaDB, a main-memory DBMS with built-in GPU acceleration, which is optimized for OLAP workloads. CoGaDB uses the self-tuning optimizer framework HyPE to build a hardware-oblivious optimizer, which learns cost models for database operators and efficiently distributes a workload on available processors. Furthermore, CoGaDB implements efficient algorithms on CPU and GPU and efficiently supports star joins. We show in this paper, how these novel techniques interact with each other in a single system. Our evaluation shows that CoGaDB quickly adapts to the underlying hardware by increasing the accuracy of its cost models at runtime. Wed, 01 Jan 2014 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11727 2014-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11723 Heterogeneity-Aware Operator Placement in Column-Store DBMS Karnagel, Tomas; Habich, Dirk; Schlegel, Benjamin; Lehner, Wolfgang Due to the tremendous increase in the amount of data efficiently managed by current database systems, optimization is still one of the most challenging issues in database research. Today’s query optimizer determine the most efficient composition of physical operators to execute a given SQL query, whereas the underlying hardware consists of a multi-core CPU. However, hardware systems are more and more shifting towards heterogeneity, combining a multi-core CPU with various computing units, e.g., GPU or FPGA cores. In order to efficiently utilize the provided performance capability of such heterogeneous hardware, the assignment of physical operators to computing units gains importance. In this paper, we propose a heterogeneity-aware physical operator placement strategy (HOP) for in-memory columnar database systems in a heterogeneous environment. Our placement approach takes operators from the physical query execution plan as an input and assigns them to computing units using a cost model at runtime. To enable this runtime decision, our cost model uses the characteristics of the computing units, execution properties of the operators, as well as runtime data to estimate execution costs for each unit. We evaluated our approach on full TPC-H queries within a prototype database engine. As we are going to show, the placement in a heterogeneous hardware system has a high influence on query performance. Wed, 01 Jan 2014 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11723 2014-01-01T00:00:00Z