Pulasthi Wickramasinghe

Orcid: 0000-0002-9145-1151

According to our database1, Pulasthi Wickramasinghe authored at least 14 papers between 2016 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2022
High-performance iterative dataflow abstractions in Twister2: TSet.
Concurr. Comput. Pract. Exp., 2022

Twister2 Cross-platform resource scheduler for big data.
Concurr. Comput. Pract. Exp., 2022

Stochastic gradient descent-based support vector machines training optimization on Big Data and HPC frameworks.
Concurr. Comput. Pract. Exp., 2022

2021
Multidimensional Scaling for Gene Sequence Data with Autoencoders.
CoRR, 2021

2020
A Fast, Scalable, Universal Approach For Distributed Data Reductions.
CoRR, 2020

Twister2: Design of a big data toolkit.
Concurr. Comput. Pract. Exp., 2020

High Performance Data Engineering Everywhere.
Proceedings of the IEEE International Conference on Smart Data Services, 2020

Data Engineering for HPC with Python.
Proceedings of the 9th IEEE/ACM Workshop on Python for High-Performance and Scientific Computing, 2020

A Fast, Scalable, Universal Approach For Distributed Data Aggregations.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019
Streaming Machine Learning Algorithms with Big Data Systems.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018
Anatomy of machine learning algorithm implementations in MPI, Spark, and Flink.
Int. J. High Perform. Comput. Appl., 2018

Twister: Net - Communication Library for Big Data Processing in HPC and Cloud Environments.
Proceedings of the 11th IEEE International Conference on Cloud Computing, 2018

2016
TSmap3D: Browser visualization of high dimensional time series data.
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

Java thread and process performance for parallel machine learning on multicore HPC clusters.
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016


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