Time Series Queries Processing with GPU Support

Przymus, Piotr; Kaczmarski, Krzysztof

doi:10.1007/978-3-319-01863-8_6

Cited by 5 publications

(8 citation statements)

References 7 publications

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“…However, variable number of exceptions lead to many branches in code and decrease efficiency of parallel threads. Various solutions have been proposed to cope with this problem, such as reducing the frame size [22], avoiding too many exceptions [4,21] or separating decoding and patching processes [14,17].…”

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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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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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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“…Last et al [12] described a general methodology for knowledge discovery in TSDB. Przymus and Kaczmarski [22] proposed query engine for time series data based on GPGPU and NoSQL databases. While such researches are promising, not enough mature implementations of GPGPUs are available.…”

Section: Introductionmentioning

confidence: 99%

Fast Knowledge Discovery in Time Series with GPGPU on Genetic Programming

Moon

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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We tackle the problem of knowledge discovery in time series data using genetic programming and GPGPUs. Using genetic programming, various precursor patterns that have certain attractive qualities are evolved to predict the events of interest. Unfortunately, evolving a set of diverse patterns typically takes huge execution time, sometimes longer than one month for this case. In this paper, we address this problem by proposing a parallel GP framework using GPGPUs, particularly in the context of big financial data. By maximally exploiting the structure of the nVidia GPGPU platform on stock market time series data, we were able see more than 250-fold reduction in the running time.

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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%

“…In case of database systems current research focuses on using GPU to support general tasks in database engines [7,14,15,5,4], like relational query processing, query optimization, database compression and other. 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%

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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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Time Series Queries Processing with GPU Support

Cited by 5 publications

References 7 publications

Improving High-Performance GPU Graph Traversal with Compression

Improving High-Performance GPU Graph Traversal with Compression

Fast Knowledge Discovery in Time Series with GPGPU on Genetic Programming

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

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