The multi-core architectures of today's computer systems make parallelism a necessity for performance critical applications. Writing such applications in a generic, hardware-oblivious manner is a challenging problem: Current database systems thus rely on laborintensive and error-prone manual tuning to exploit the full potential of modern parallel hardware architectures like multi-core CPUs and graphics cards. We propose an alternative design for a parallel database engine, based on a single set of hardware-oblivious operators, which are compiled down to the actual hardware at runtime. This design reduces the development overhead for parallel database engines, while achieving competitive performance to hand-tuned systems.We provide a proof-of-concept for this design by integrating operators written using the parallel programming framework OpenCL into the open-source database MonetDB. Following this approach, we achieve efficient, yet highly portable parallel code without the need for optimization by hand. We evaluated our implementation against MonetDB using TPC-H derived queries and observed a performance that rivals that of MonetDB's query execution on the CPU and surpasses it on the GPU. In addition, we show that the same set of operators runs nearly unchanged on a GPU, demonstrating the feasibility of our approach.

Quickly and accurately estimating the selectivity of multidimensional predicates is a vital part of a modern relational query optimizer. The state-of-the art in this field are multidimensional histograms, which offer good estimation quality but are complex to construct and hard to maintain. Kernel Density Estimation (KDE) is an interesting alternative that does not suffer from these problems. However, existing KDE-based selectivity estimators can hardly compete with the estimation quality of state-of-the art methods.In this paper, we substantially expand the state-of-theart in KDE-based selectivity estimation by improving along three dimensions: First, we demonstrate how to numerically optimize a KDE model, leading to substantially improved estimates. Second, we develop methods to continuously adapt the estimator to changes in both the database and the query workload. Finally, we show how to drastically improve the performance by pushing computations onto a GPU.We provide an implementation of our estimator and experimentally evaluate it on a variety of datasets and workloads, demonstrating that it efficiently scales up to very large model sizes, adapts itself to database changes, and typically outperforms the estimation quality of both existing Kernel Density Estimators as well as state-of-the-art multidimensional histograms.

Accurately predicting the cardinality of intermediate plan operations is an essential part of any modern relational query optimizer. The accuracy of said estimates has a strong and direct impact on the quality of the generated plans, and incorrect estimates can have a negative impact on query performance. One of the biggest challenges in this field is to predict the result size of join operations. Kernel Density Estimation (KDE) is a statistical method to estimate multivariate probability distributions from a data sample. Previously, we introduced a modern, self-tuning selectivity estimator for range scans based on KDE that outperforms state-of-the-art multidimensional histograms and is efficient to evaluate on graphics cards. In this paper, we extend these bandwidth-optimized KDE models to estimate the result size of single and multiple joins. In particular, we propose two approaches: (1) Building a KDE model from a sample drawn from the join result. (2) Efficiently combining the information from base table KDE models. We evaluated our KDE-based join estimators on a variety of synthetic and real-world datasets, demonstrating that they are superior to state-of-the art join estimators based on sketching or sampling.

Abstract. The vast amount of processing power and memory bandwidth provided by modern graphics cards make them an interesting platform for data-intensive applications. Unsurprisingly, the database research community identified GPUs as effective co-processors for data processing several years ago. In the past years, there were many approaches to make use of GPUs at different levels of a database system. In this paper, we explore the design space of GPU-accelerated database management systems. Based on this survey, we present key properties, important trade-offs and typical challenges of GPU-aware database architectures, and identify major open challenges. Additionally, we survey existing GPU-accelerated DBMSs and classify their architectural properties. Then, we summarize typical optimizations implemented in GPU-accelerated DBMSs. Finally, we propose a reference architecture, indicating how GPU acceleration can be integrated in existing DBMSs.

Large-scale data analysis applications require processing and analyzing of Terabytes or even Petabytes of data, particularly in the areas of web analysis or scientific data management. This trend has been discussed as "web-scale data management" in a panel at VLDB 2009. Formerly, parallel data processing was the domain of parallel database systems. Today, novel requirements like scaling out to thousands of machines, improved fault-tolerance, and schema free processing have made a case for new approaches.

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Abstract. The vast amount of processing power and memory bandwidth provided by modern graphics cards make them an interesting platform for data-intensive applications. Unsurprisingly, the database research community has identified GPUs as effective co-processors for data processing several years ago. In the past years, there were many approaches to make use of GPUs at different levels of a database system. In this paper, we summarize the major findings of the literature on GPU-accelerated data processing. Based on this survey, we present key properties, important trade-offs and typical challenges of GPU-aware database architectures, and identify major open research questions.

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