Auflistung nach Autor:in "Karnagel, Tomas"
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- ZeitschriftenartikelHeterogeneity-Aware Operator Placement in Column-Store DBMS(Datenbank-Spektrum: Vol. 14, No. 3, 2014) Karnagel, Tomas; Habich, Dirk; Schlegel, Benjamin; Lehner, WolfgangDue 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.
- ZeitschriftenartikelHeterogeneous placement optimization for database query processing(it - Information Technology: Vol. 59, No. 5, 2017) Karnagel, Tomas; Habich, DirkComputing hardware is constantly evolving and database systems need to adapt to ongoing hardware changes to improve performance. The current hardware trend is heterogeneity, where multiple computing units like CPUs and GPUs are used together in one system. In this paper, we summarize our efforts to use hardware heterogeneity efficiently for query processing. We discuss different approaches of execution and investigate heterogeneous placement in detail by showing, how to automatically determine operator placement decisions according to the given hardware environment and query properties.
- KonferenzbeitragOverview on Hardware Optimizations for Database Engines(Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017) Ungethüm, Annett; Habich, Dirk; Karnagel, Tomas; Haas, Sebastian; Mier, Eric; Fettweis, Gerhard; Lehner, WolfgangThe key objective of database systems is to e ciently manage an always increasing amount of data. Thereby, a high query throughput and a low query latency are core requirements. To satisfy these requirements, database engines are highly adapted to the given hardware by using all features of modern processors. Apart from this software optimization, even tailor-made processing circuits running on FGPAs are built to run mostly stateless query plans with a high throughput. A similar approach, which was already investigated three decades ago, is to build customized hardware like a database processor. Tailor-made hardware allows to achieve performance numbers that cannot be reached with software running on general-purpose CPUs, while at the same time, addressing the dark silicon problem. The main disadvantage of custom hardware is the high development cost that comes with designing and verifying a new processor, as well as building respective drivers and the software stack. However, there is actually no need to build a fully-fledged processor from scratch. In this paper, we present our conducted as well as our ongoing research e orts in the direction of customizing hardware for databases. In detail, we illustrate the potential of instruction set extensions of processors as well as of optimizing memory access by o oading logic to the main memory controller.
- KonferenzbeitragStream join processing on heterogeneous processors(Datenbanksysteme für Business, Technologie und Web (BTW) 2013 - Workshopband, 2013) Karnagel, Tomas; Schlegel, Benjamin; Habich, Dirk; Lehner, WolfgangThe window-based stream join is an important operator in all data streaming systems. It has often high resource requirements so that many efficient sequential as well as parallel versions of it were proposed in the literature. The parallel stream join operators recently gain increasing interest because hardware is getting more and more parallel. Most of these operators, however, are only optimized for processors with homogeneous execution units (e.g., multi-core processors). Newly available processors with heterogeneous execution units cannot be exploited whereas such processors provide typically a very high peak performance. In this paper, we propose an initial variant of a window-based stream join operator that is optimized for processors with heterogeneous execution units. We provide an efficient load balancing approach to utilize all available execution units of a processor and further provide highly-optimized kernels that run on them. On our test machine with a 4-core CPU and an integrated graphics processor, our operator achieves a speedup of 69 2x compared to our single-threaded . implementation.