4 citations · 6 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
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…