P331 - BTW2023- Datenbanksysteme für Business, Technologie und Web

Permanent URI for this collectionhttps://dl.gi.de/handle/20.500.12116/40312

Authors with most Documents  

König-Ries, Birgitta
6
Habich, Dirk
5
Lehner, Wolfgang
4
Binnig, Carsten
3
Hartmann, Claudio
3

Browse

Author: search.filters.author.Habich, Dirk

Search Results

1 - 5 of 5
  • Conference Paper
    Optimizing Query Processing in PostgreSQL Through Learned Optimizer Hints
    (Gesellschaft für Informatik e.V., 2023) Thiessat, Jerome; Woltmann, Lucas; Hartmann, Claudio; Habich, Dirk; König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried
    Query optimization in database systems is an important aspect and despite decades of research, it isstill far from being solved. Nowadays, query optimizers usually provide hints to be able to steer theoptimization on a query-by-query basis. However, setting the best-fitting hints is challenging. To tacklethat, we present a learning-based approach to predict the best-fitting hints for each incoming query. Inparticular, our learning approach is based on simple gradient boosting, where we learn one modelper query context for fine-grained predictions rather than a single global context-agnostic model asproposed in related work. We demonstrate the efficiency as well as effectiveness of our learning-basedapproach using the open-source database system PostgreSQL and show that our approach outperformsrelated work in that context.
  • Conference Paper
    Improving GPU Matrix Multiplication by Leveraging Bit Level Granularity and Compression
    (Gesellschaft für Informatik e.V., 2023) Fett, Johannes; Schwarz, Christian; Kober, Urs; Habich, Dirk; Lehner, Wolfgang; König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried
    In this paper we introduce BEAM as a novel approach to perform GPU based matrix multiplication on compressed elements. BEAM allows flexible handling of bit sizes for both input and output elements. First evaluations show promising speedups compared to an uncompressed state-of-the-art matrix multiplication algorithm provided by nvidia.
  • Conference Paper
    Working with Disaggregated Systems. What are the Challenges and Opportunities of RDMA and CXL?
    (Gesellschaft für Informatik e.V., 2023) Geyer, Andreas; Ritter, Daniel; Lee, Dong Hun; Ahn, Minseon; Pietrzyk, Johannes; Krause, Alexander; Habich, Dirk; Lehner, Wolfgang; König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried
    The usage of disaggregated systems in large scale data-centers offers a lot of flexibility and easy scalability in comparison to the traditional statically configured scale-up and scaleout systems. Disaggregated architectures allow for the creation of software composable systems in order to create a virtual machine by software out of the pool of available hardware resources. In this paper, we propose a memory disaggregation classification and applicable use cases. We would be delighted to present our ideas and the memory disaggregation classification at the workshop and discuss the presented ideas. The valuable feedback of the attendees will help us to further refine our classification both in terms of preciseness and applicability.
  • Conference Paper
    Second Workshop on Novel Data Management Ideas on Heterogeneous (Co-)Processors (NoDMC)
    (Gesellschaft für Informatik e.V., 2023) Broneske, David; Habich, Dirk; König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried
    The objective of this one-day workshop is to explore the challenges and opportuni-ties of data processing on existing and future heterogeneous hardware architectures.
  • Conference Paper
    PostBOUND: PostgreSQL with Upper Bound SPJ Query Optimization
    (Gesellschaft für Informatik e.V., 2023) Bergmann, Rico; Hertzschuch, Axel; Hartmann, Claudio; Habich, Dirk; Lehner, Wolfgang; König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried
    A variety of query optimization papers have shown the disastrous effect of poor cardinality estimates on the overall run time for arbitrary select-project-join (SPJ) queries.Especially, underestimating join cardinalities for multi-joins can lead to catastrophic join orderings. A promising solution to overcome this problem is query optimization based on upper bounds for the join cardinalities. In this domain, our proposed UES concept is presently the most efficient technique featuring a simple, yet effective upper bound for an arbitrary number of joins. To foster research in that direction, we introduce PostBOUND, our generalized framework making upper bound SPJ query optimization a first class citizen in PostgreSQL.PostBOUND provides abstractions to calculate arbitrary upper bounds, to model joins required by an SPJ query and to iteratively construct an optimized join order.To highlight the extensibility of PostBOUND and to show the research potential, we additionally present two tighter upper bound UES variants using top-k statistics in this paper.In our evaluation, we show the efficiency and applicability of PostBOUND on different workloads as well as using different PostgreSQL versions. Additionally, we evaluate both presented tighter upper bound variant ideas.
Load citations