Implementation of probabilistic forecasts in a GPU for big data problems Article Swipe
High Performance Computing via General Purpose Graphical Processing Unit (GPU) is a potential instrument to speed up computational times. In a world where big data is becoming a revolution, GPU could play an important role. This work intends to analyze the performance of GPU by implementing the calculation of probabilistic forecasts based on single exponential smoothing in conjunction with simulated predictive distributions. Essentially, supply chain companies must deal with a high number of forecasts at SKU level. In this context, reducing the computational times can be a source of a competitive advantage. Since the forecasts are usually made independently between SKUs, this problem can be easily parallelized and GPU computing can exploit such parallelization. To the best of authors knowledge, this is the first time GPU is applied to a supply chain demand forecasting context. Firstly, we will show how to adapt the programming of probabilistic forecasts in a parallel fashion. Then, real data coming from a manufacturer company will be used to illustrate the differences between GPU and traditional CPU computing. The results show that GPU can significantly increase the computational speedup ratio more than 30 times with respect to traditional CPU computing.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://hdl.handle.net/10578/21986
- OA Status
- green
- Cited By
- 1
- References
- 4
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2980807623
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2980807623Canonical identifier for this work in OpenAlex
- Title
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Implementation of probabilistic forecasts in a GPU for big data problemsWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
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2019-09-30Full publication date if available
- Authors
-
Juan R. TraperoList of authors in order
- Landing page
-
https://hdl.handle.net/10578/21986Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/10578/21986Direct OA link when available
- Concepts
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Computer science, Big data, Probabilistic logic, Data science, Data mining, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2021: 1Per-year citation counts (last 5 years)
- References (count)
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4Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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