New Benchmarking Methodology and Programming Model for Big Data Processing
Big data processing is becoming a reality in numerous real-world applications. With the emergence of new data intensive technologies and increasing amounts of data, new computing concepts are needed. The integration of big data producing technologies, such as wireless sensor networks, Internet of Th...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-08-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/271752 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547784773861376 |
|---|---|
| author | Anton Kos Sašo Tomažič Jakob Salom Nemanja Trifunovic Mateo Valero Veljko Milutinovic |
| author_facet | Anton Kos Sašo Tomažič Jakob Salom Nemanja Trifunovic Mateo Valero Veljko Milutinovic |
| author_sort | Anton Kos |
| collection | DOAJ |
| description | Big data processing is becoming a reality in numerous real-world applications. With the emergence of new data intensive technologies and increasing amounts of data, new computing concepts are needed. The integration of big data producing technologies, such as wireless sensor networks, Internet of Things, and cloud computing, into cyber-physical systems is reducing the available time to find the appropriate solutions. This paper presents one possible solution for the coming exascale big data processing: a data flow computing concept. The performance of data flow systems that are processing big data should not be measured with the measures defined for the prevailing control flow systems. A new benchmarking methodology is proposed, which integrates the performance issues of speed, area, and power needed to execute the task. The computer ranking would look different if the new benchmarking methodologies were used; data flow systems would outperform control flow systems. This statement is backed by the recent results gained from implementations of specialized algorithms and applications in data flow systems. They show considerable factors of speedup, space savings, and power reductions regarding the implementations of the same in control flow computers. In our view, the next step of data flow computing development should be a move from specialized to more general algorithms and applications. |
| format | Article |
| id | doaj-art-7414824972dd486c90b78316906b3197 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-7414824972dd486c90b78316906b31972025-02-03T06:43:17ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-08-011110.1155/2015/271752271752New Benchmarking Methodology and Programming Model for Big Data ProcessingAnton Kos0Sašo Tomažič1Jakob Salom2Nemanja Trifunovic3Mateo Valero4Veljko Milutinovic5 Faculty of Electrical Engineering, University of Ljubljana, Tržaška Cesta 25, 1000 Ljubljana, Slovenia Faculty of Electrical Engineering, University of Ljubljana, Tržaška Cesta 25, 1000 Ljubljana, Slovenia Mathematical Institute of the Serbian Academy of Sciences and Arts, Knez Mihailova 36, 11001 Belgrade, Serbia Maxeler Technologies Ltd., 1 Down Place, London W6 9JH, UK Barcelona Supercomputing Center, Carrer de Jordi Girona 29, 08034 Barcelona, Spain School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11000 Belgrade, SerbiaBig data processing is becoming a reality in numerous real-world applications. With the emergence of new data intensive technologies and increasing amounts of data, new computing concepts are needed. The integration of big data producing technologies, such as wireless sensor networks, Internet of Things, and cloud computing, into cyber-physical systems is reducing the available time to find the appropriate solutions. This paper presents one possible solution for the coming exascale big data processing: a data flow computing concept. The performance of data flow systems that are processing big data should not be measured with the measures defined for the prevailing control flow systems. A new benchmarking methodology is proposed, which integrates the performance issues of speed, area, and power needed to execute the task. The computer ranking would look different if the new benchmarking methodologies were used; data flow systems would outperform control flow systems. This statement is backed by the recent results gained from implementations of specialized algorithms and applications in data flow systems. They show considerable factors of speedup, space savings, and power reductions regarding the implementations of the same in control flow computers. In our view, the next step of data flow computing development should be a move from specialized to more general algorithms and applications.https://doi.org/10.1155/2015/271752 |
| spellingShingle | Anton Kos Sašo Tomažič Jakob Salom Nemanja Trifunovic Mateo Valero Veljko Milutinovic New Benchmarking Methodology and Programming Model for Big Data Processing International Journal of Distributed Sensor Networks |
| title | New Benchmarking Methodology and Programming Model for Big Data Processing |
| title_full | New Benchmarking Methodology and Programming Model for Big Data Processing |
| title_fullStr | New Benchmarking Methodology and Programming Model for Big Data Processing |
| title_full_unstemmed | New Benchmarking Methodology and Programming Model for Big Data Processing |
| title_short | New Benchmarking Methodology and Programming Model for Big Data Processing |
| title_sort | new benchmarking methodology and programming model for big data processing |
| url | https://doi.org/10.1155/2015/271752 |
| work_keys_str_mv | AT antonkos newbenchmarkingmethodologyandprogrammingmodelforbigdataprocessing AT sasotomazic newbenchmarkingmethodologyandprogrammingmodelforbigdataprocessing AT jakobsalom newbenchmarkingmethodologyandprogrammingmodelforbigdataprocessing AT nemanjatrifunovic newbenchmarkingmethodologyandprogrammingmodelforbigdataprocessing AT mateovalero newbenchmarkingmethodologyandprogrammingmodelforbigdataprocessing AT veljkomilutinovic newbenchmarkingmethodologyandprogrammingmodelforbigdataprocessing |