Supun Kamburugamuve

Cloud Computing and Resource Management Distributed and Parallel Computing Systems Scientific Computing and Data Management Advanced Data Storage Technologies Parallel Computing and Optimization Techniques

Publications

Citations

535

Est. group size

—

Recurring co-author estimate

Active years

Publishing since 2012

Research summary

AI-generated

Supun Kamburugamuve works in high-performance and distributed computing, focusing on systems and software that let large-scale data processing run efficiently across computing clusters and cloud environments. A central line of work involves building frameworks and tools (such as the Twister2 toolkit and high-performance dataframes) that combine big-data processing with supercomputing (HPC) techniques. The research aims to make data engineering and data science tasks faster and more scalable.

High-performance data engineeringDistributed and parallel computingBig data frameworks and toolkitsCloud and HPC integrationScalable dataframes and data processing

Publication activity was steady with a peak around 2021, and has slowed somewhat in the most recent years.

Generated by claude-opus-4-8 from public bibliographic data · Jul 11, 2026

Publication cadence

Publications per year over the last 10 years — averaging 1.6/year recently

Recent publications

Supercharging distributed computing environments for high-performance data engineering
Frontiers in High Performance Computing · 2024
In-depth Analysis On Parallel Processing Patterns for High-Performance Dataframes
arXiv (Cornell University) · 2023
In-depth analysis on parallel processing patterns for high-performance Dataframes
Future Generation Computer Systems · 2023
High Performance Dataframes from Parallel Processing Patterns
Lecture notes in computer science · 2023
HPTMT Parallel Operators for High Performance Data Science and Data Engineering
Frontiers in Big Data · 2022
Hybrid Cloud and HPC Approach to High-Performance Dataframes
arXiv (Cornell University) · 2022
Hybrid Cloud and HPC Approach to High-Performance Dataframes
2022 IEEE International Conference on Big Data (Big Data) · 2022
High Performance Dataframes from Parallel Processing Patterns
arXiv (Cornell University) · 2022
HPTMT Parallel Operators for High Performance Data Science & Data Engineering
arXiv (Cornell University) · 2021
Twister2 Cross‐platform resource scheduler for big data
Concurrency and Computation Practice and Experience · 2021
Stochastic gradient descent‐based support vector machines training optimization on Big Data and HPC frameworks
Concurrency and Computation Practice and Experience · 2021
HPTMT: Operator-Based Architecture for Scalable High-Performance Data-Intensive Frameworks
2021
HPTMT Parallel Operators for High Performance Data Science & Data Engineering.
arXiv (Cornell University) · 2021
HPTMT: Operator-Based Architecture for Scalable High-Performance\n Data-Intensive Frameworks
arXiv (Cornell University) · 2021
Data Engineering for HPC with Python
2020

Publishes in

arXiv (Cornell University)×6
Concurrency and Computation Practice and Experience×4
Lecture notes in computer science×2
The International Journal of High Performance Computing Applications×1
International Journal of Grid and Distributed Computing×1

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 11, 2026.

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