- Title
- SMOaaS: a Scalable Matrix Operation as a Service model in Cloud
- Creator
- Ujjwal, K. C.; Battula, Sudheer; Garg, Saurabh; Naha, Ranesh; Patwary, Md Anwarul; Brown, Alexander
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/196455
- Identifier
- vital:18703
- Identifier
-
https://doi.org/10.1007/s11227-020-03400-0
- Identifier
- ISSN:0920-8542
- Abstract
- Matrix operations are fundamental to a wide range of scientific applications such as Graph Theory, Linear Equation System, Image Processing, Geometric Optics, and Probability Analysis. As the workload in these applications has increased, the sizes of matrices involved have also significantly increased. Parallel execution of matrix operations in existing cluster-based systems performs effectively for relatively small matrices but significantly suffers as matrices become larger due to limited resources. Cloud Computing offers scalable resources to handle this limitation however, the benefits of having access to almost-infinite scalable resources in the Cloud also come with challenges of ensuring time and resource-efficient matrix operations. To the best of our knowledge, there is no specific Cloud service that optimizes the efficiency of matrix operations on Cloud infrastructure. To address this gap and offer convenient service of matrix operations, the paper proposes a novel scalable service framework called Scalable Matrix Operation as a Service. Our framework uses Dynamic Matrix Partition techniques, based on matrix operation and sizes, to achieve efficient work distribution, and scales based on demand to achieve time and resource-efficient operations. The framework also embraces the basic features of security, fault tolerance, and reliability. Experimental results show that the adopted dynamic partitioning technique ensures faster and better performance when compared to the existing static partitioning technique.
- Publisher
- Springer US
- Relation
- The Journal of supercomputing Vol. 77, no. 4 (2021), p. 3381-3401
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright Springer
- Subject
- Cloud computing; Compilers; Computer Science; Fault tolerance; Geometrical optics; Graph theory; High Performance Computing for Large Scale Data Analytics; Image processing; Interpreters; Linear equations; Partitioning; Processor Architectures; Programming Languages; System effectiveness; 46 Information and Computing Sciences
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