112 citations · 370 across the 54 of their papers we have counts for
11 papers · 1 filter
Faster Parallel Batch-Dynamic Algorithms for Low Out-Degree Orientation
Guy Blelloch, Andrew Brady, Laxman Dhulipala +3
A low out-degree orientation directs each edge of an undirected graph with the goal of minimizing the maximum out-degree of a vertex. In the parallel batch-dynamic setting, one can…
TD-Orch: Efficient Task-Data Orchestration for Distributed Systems with Application to Graph Processing
Yiwei Zhao, Qiushi Lin, Hongbo Kang +5
We introduce a task-data orchestration abstraction that supports a range of distributed applications. Given a batch of lambda tasks each requesting a data item, where both tasks an…
The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering
Shangdi Yu, Jessica Shi, Jamison Meindl +7
We introduce the ParClusterers Benchmark Suite (PCBS) -- a collection of highly scalable parallel graph clustering algorithms and benchmarking tools that streamline comparing diffe…
Parallel Algorithms for Hierarchical Nucleus Decomposition
Jessica Shi, Laxman Dhulipala, Julian Shun
Nucleus decompositions have been shown to be a useful tool for finding dense subgraphs. The coreness value of a clique represents its density based on the number of other cliques i…
Theoretically and Practically Efficient Parallel Nucleus Decomposition
Jessica Shi, Laxman Dhulipala, Julian Shun
This paper studies the nucleus decomposition problem, which has been shown to be useful in finding dense substructures in graphs. We present a novel parallel algorithm that is effi…
Parallel Graph Algorithms in Constant Adaptive Rounds: Theory meets Practice
Soheil Behnezhad, Laxman Dhulipala, Hossein Esfandiari +3
We study fundamental graph problems such as graph connectivity, minimum spanning forest (MSF), and approximate maximum (weight) matching in a distributed setting. In particular, we…
Exploring the Design Space of Static and Incremental Graph Connectivity Algorithms on GPUs
Changwan Hong, Laxman Dhulipala, Julian Shun
Connected components and spanning forest are fundamental graph algorithms due to their use in many important applications, such as graph clustering and image segmentation. GPUs are…
ConnectIt: A Framework for Static and Incremental Parallel Graph Connectivity Algorithms
Laxman Dhulipala, Changwan Hong, Julian Shun
Connected components is a fundamental kernel in graph applications. The fastest existing parallel multicore algorithms for connectivity are based on some form of edge sampling and/…
Sage: Parallel Semi-Asymmetric Graph Algorithms for NVRAMs
Laxman Dhulipala, Charlie McGuffey, Hongbo Kang +4
Non-volatile main memory (NVRAM) technologies provide an attractive set of features for large-scale graph analytics, including byte-addressability, low idle power, and improved mem…
Massively Parallel Computation via Remote Memory Access
Soheil Behnezhad, Laxman Dhulipala, Hossein Esfandiari +3
We introduce the Adaptive Massively Parallel Computation (AMPC) model, which is an extension of the Massively Parallel Computation (MPC) model. At a high level, the AMPC model stre…
Low-Latency Graph Streaming Using Compressed Purely-Functional Trees
Laxman Dhulipala, Julian Shun, Guy Blelloch
Due to the dynamic nature of real-world graphs, there has been a growing interest in the graph-streaming setting where a continuous stream of graph updates is mixed with arbitrary…