activity
20172026
most citedDynamic Load Balancing Strategies for Graph Applications on GPUs

4 citations · 6 across the 13 of their papers we have counts for

collaborators

15 papers

cs.DC2026

HARP-ME: Closure-Driven Exact Induced Motif Enumeration on GPUs

Ashwina Kumar, Rupesh Nasre

Exact induced motif enumeration is a fundamental operation in graph mining, but it remains challenging on GPUs because candidate expansion is irregular, repeated set intersections…

cs.LG2025

Morphling: Fast, Fused, and Flexible GNN Training at Scale

Anubhab, Rupesh Nasre

Graph Neural Networks (GNNs) present a fundamental hardware challenge by fusing irregular, memory-bound graph traversals with regular, compute-intensive dense matrix operations. Wh…

cs.DC2025

StarDist: A Code Generator for Distributed Graph Algorithms

Barenya Kumar Nandy, Rupesh Nasre

We introduce StarDist, a Domain Specific Language for generating high-performant distributed graph algorithms in the message passing model. Our analysis-transformation framework op…

cs.DS2025

Efficient Dynamic MaxFlow Computation on GPUs

Shruthi Kannappan, Ashwina Kumar, Rupesh Nasre

Maxflow is a fundamental problem in graph theory and combinatorial optimisation, used to determine the maximum flow from a source node to a sink node in a flow network. It finds ap…

cs.DC2025

Scalable Maxflow Processing for Dynamic Graphs

Shruthi Kannappan, Ashwina Kumar, Rupesh Nasre

The Maximum Flow (Max-Flow) problem is a cornerstone in graph theory and combinatorial optimization, aiming to determine the largest possible flow from a designated source node to…

cs.DC2025

Generating Dynamic Graph Algorithms for Multiple Backends for a Graph DSL

Nibedita Behera, Ashwina Kumar, Atharva Chougule +3

With the rapid growth of unstructured and semistructured data, parallelizing graph algorithms has become essential for efficiency. However, due to the inherent irregularity in comp…

cs.DC2024

Code Generation for a Variety of Accelerators for a Graph DSL

Ashwina Kumar, M. Venkata Krishna, Prasanna Bartakke +4

Sparse graphs are ubiquitous in real and virtual worlds. With the phenomenal growth in semi-structured and unstructured data, sizes of the underlying graphs have witnessed a rapid…

cs.DS2023

GraphVine: A Data Structure to Optimize Dynamic Graph Processing on GPUs

Rohith Krishnan S, Venkata Kalyan Tavva, Rupesh Nasre

Graph processing on GPUs is gaining momentum due to the high throughputs observed compared to traditional CPUs, attributed to the vast number of processing cores on GPUs that can e…

cs.DC2023

Meerkat: A framework for Dynamic Graph Algorithms on GPUs

Kevin Jude Concessao, Unnikrishnan Cheramangalath, MJ Ricky Dev +1

Graph algorithms are challenging to implement due to their varying topology and irregular access patterns. Real-world graphs are dynamic in nature and routinely undergo edge and ve…

cs.DC2023

StarPlat: A Versatile DSL for Graph Analytics

Nibedita Behera, Ashwina Kumar, Ebenezer Rajadurai T +3

Graphs model several real-world phenomena. With the growth of unstructured and semi-structured data, parallelization of graph algorithms is inevitable. Unfortunately, due to inhere…

cs.DC2020★ 2 cited

A Fine-Grained Hybrid CPU-GPU Algorithm for Betweenness Centrality Computations

Ashirbad Mishra, Sathish Vadhiyar, Rupesh Nasre +1

Betweenness centrality (BC) is an important graph analytical application for large-scale graphs. While there are many efforts for parallelizing betweenness centrality algorithms on…

cs.SE2020

BOLD: An Ontology-based Log Debugger for C Programs

Dileep Kumar P, Rupesh Nasre, Sreenivasa Kumar P

The different activities related to debugging such as program instrumentation, representation of execution trace and analysis of trace are not typically performed in an unified fra…

cs.DC2019

GPU Algorithm for Earliest Arrival Time Problem in Public Transport Networks

Chirayu Anant Haryan, G. Ramakrishna, Rupesh Nasre +1

Given a temporal graph G, a source vertex s, and a departure time at source vertex t_s, the earliest arrival time problem EAT is to start from s on or after t_s and reach all the v…

cs.PL2019

Custom Code Generation for a Graph DSL

Bikash Gogoi, Unnikrishnan Cheramangalath, Rupesh Nasre

Graph algorithms are at the heart of several applications, and achieving high performance with them has become critical due to the tremendous growth of irregular data. However, irr…

cs.DC2017★ 4 cited

Dynamic Load Balancing Strategies for Graph Applications on GPUs

Ananya Raval, Rupesh Nasre, Vivek Kumar +3

Acceleration of graph applications on GPUs has found large interest due to the ubiquitous use of graph processing in various domains. The inherent \textit{irregularity} in graph ap…