activity
20202023
most citedHigh Performance Dataframes from Parallel Processing Patterns

7 citations · 7 across the 7 of their papers we have counts for

collaborators

10 papers

cs.DC2023

In-depth Analysis On Parallel Processing Patterns for High-Performance Dataframes

Niranda Perera, Arup Kumar Sarker, Mills Staylor +7

The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intel…

cs.DC2023

Supercharging Distributed Computing Environments For High Performance Data Engineering

Niranda Perera, Kaiying Shan, Supun Kamburugamuwe +7

The data engineering and data science community has embraced the idea of using Python & R dataframes for regular applications. Driven by the big data revolution and artificial inte…

cs.DC2022

Hybrid Cloud and HPC Approach to High-Performance Dataframes

Kaiying Shan, Niranda Perera, Damitha Lenadora +6

Data pre-processing is a fundamental component in any data-driven application. With the increasing complexity of data processing operations and volume of data, Cylon, a distributed…

cs.DC2022★ 7 cited

High Performance Dataframes from Parallel Processing Patterns

Niranda Perera, Supun Kamburugamuve, Chathura Widanage +7

The data science community today has embraced the concept of Dataframes as the de facto standard for data representation and manipulation. Ease of use, massive operator coverage, a…

cs.DC2021

HPTMT Parallel Operators for High Performance Data Science & Data Engineering

Vibhatha Abeykoon, Supun Kamburugamuve, Chathura Widanage +5

Data-intensive applications are becoming commonplace in all science disciplines. They are comprised of a rich set of sub-domains such as data engineering, deep learning, and machin…

cs.DC2021

HPTMT: Operator-Based Architecture for Scalable High-Performance Data-Intensive Frameworks

Supun Kamburugamuve, Chathura Widanage, Niranda Perera +5

Data-intensive applications impact many domains, and their steadily increasing size and complexity demands high-performance, highly usable environments. We integrate a set of ideas…

cs.CR2021

HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework

Chathura Widanage, Weijie Liu, Jiayu Li +4

Trusted execution environments (TEE) such as Intel's Software Guard Extension (SGX) have been widely studied to boost security and privacy protection for the computation of sensiti…

cs.DC2020

Data Engineering for HPC with Python

Vibhatha Abeykoon, Niranda Perera, Chathura Widanage +6

Data engineering is becoming an increasingly important part of scientific discoveries with the adoption of deep learning and machine learning. Data engineering deals with a variety…

cs.DC2020

A Fast, Scalable, Universal Approach For Distributed Data Aggregations

Niranda Perera, Vibhatha Abeykoon, Chathura Widanage +7

In the current era of Big Data, data engineering has transformed into an essential field of study across many branches of science. Advancements in Artificial Intelligence (AI) have…

cs.DC2020

High Performance Data Engineering Everywhere

Chathura Widanage, Niranda Perera, Vibhatha Abeykoon +7

The amazing advances being made in the fields of machine and deep learning are a highlight of the Big Data era for both enterprise and research communities. Modern applications req…