Dynamic Compression Strategy for Time Series Database Using GPU

Przymus, Piotr; Kaczmarski, Krzysztof

doi:10.1007/978-3-319-01863-8_26

Cited by 12 publications

(17 citation statements)

References 7 publications

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“…We have shown that finding the optimal compression plan by a dynamic data analysis may significantly improve results without sacrificing the decompression speed [16].…”

Section: Lightweight Compression Methodsmentioning

confidence: 98%

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Improving High-Performance GPU Graph Traversal with Compression

Kaczmarski

Przymus

Rzążewski

2015

Advances in Intelligent Systems and Computing

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Abstract. Traversing huge graphs is a crucial part of many real-world problems, including graph databases. We show how to apply Fixed Length lightweight compression method for traversing graphs stored in the GPU global memory. This approach allows for a significant saving of memory space, improves data alignment, cache utilization and, in many cases, also processing speed. We tested our solution against the state-of-the-art implementation of BFS for GPU and obtained very promising results.

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“…We have shown that finding the optimal compression plan by a dynamic data analysis may significantly improve results without sacrificing the decompression speed [16].…”

Section: Lightweight Compression Methodsmentioning

confidence: 98%

“…Their main purpose is to increase a data throughput by a reduction of a data volume. A detailed description of presented compression algorithms may be found in [6,22,4,15].…”

Section: Lightweight Compression Methodsmentioning

confidence: 99%

Improving High-Performance GPU Graph Traversal with Compression

Kaczmarski

Przymus

Rzążewski

2015

Advances in Intelligent Systems and Computing

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“…They developed a planner for cascading compression techniques, which decides on a suitable subset and order of available compression techniques. Przymus and Kaczmarski focused on compression for time-series databases on the GPU [50]. Andrzejewski and Wrembel discussed compression of bitmap indexes on the GPU [4].…”

Section: Potential Optimizations For Gdbmssmentioning

confidence: 99%

GPU-Accelerated Database Systems: Survey and Open Challenges

Breß

Heimel

Siegmund

et al. 2014

Lecture Notes in Computer Science

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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 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.

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“…It may diminish the gain of the acceleration credited to GPU. In our previous works we showed that this data transfer cost can be reduced using lightweight compression methods [12,13,14]. However, this does not solve all the problems since not all tasks may be effectively faster on GPU.…”

Section: Introductionmentioning

confidence: 99%

“…Also specialized systems, for example time series databases, may benefit from parallel execution of numerical computations like interpolation, approximation, pattern matching or compression [7,1,12,5,13,14].…”

Section: Introductionmentioning

confidence: 99%

A Bi-objective Optimization Framework for Heterogeneous CPU/GPU Query Plans

Przymus

Kaczmarski

Stencel

2014

Fundamenta Informaticae

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Graphics Processing Units (GPU) have significantly more applications than just rendering images. They are also used in general-purpose computing to solve problems that can benefit from massive parallel processing. However, there are tasks that either hardly suit GPU or fit GPU only partially. The latter class is the focus of this paper. We elaborate on hybrid CPU/GPU computation and build optimization methods that seek the equilibrium between these two computation platforms. The method is based on heuristic search for bi-objective Pareto optimal execution plans in presence of multiple concurrent queries. The underlying model mimics the commodity market where devices are producers and queries are consumers. The value of resources of computing devices is controlled by supply-and-demand laws. Our model of the optimization criteria allows finding solutions of problems not yet addressed in heterogeneous query processing. Furthermore, it also offers lower time complexity and higher accuracy than other methods.

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Dynamic Compression Strategy for Time Series Database Using GPU

Cited by 12 publications

References 7 publications

Improving High-Performance GPU Graph Traversal with Compression

Improving High-Performance GPU Graph Traversal with Compression

GPU-Accelerated Database Systems: Survey and Open Challenges

A Bi-objective Optimization Framework for Heterogeneous CPU/GPU Query Plans

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