26 citations · 47 across the 7 of their papers we have counts for
9 papers
Towards Distributed 2-Approximation Steiner Minimal Trees in Billion-edge Graphs
Tahsin Reza, Geoffrey Sanders, Roger Pearce
Given an edge-weighted graph and a set of known seed vertices, a network scientist often desires to understand the graph relationships to explain connections between the seed verti…
Metall: A Persistent Memory Allocator For Data-Centric Analytics
Keita Iwabuchi, Karim Youssef, Kaushik Velusamy +2
Data analytics applications transform raw input data into analytics-specific data structures before performing analytics. Unfortunately, such data ingestion step is often more expe…
TriPoll: Computing Surveys of Triangles in Massive-Scale Temporal Graphs with Metadata
Trevor Steil, Tahsin Reza, Keita Iwabuchi +3
Understanding the higher-order interactions within network data is a key objective of network science. Surveys of metadata triangles (or patterned 3-cycles in metadata-enriched gra…
Scalable Pattern Matching in Metadata Graphs via Constraint Checking
Tahsin Reza, Hassan Halawa, Matei Ripeanu +2
Pattern matching is a fundamental tool for answering complex graph queries. Unfortunately, existing solutions have limited capabilities: they do not scale to process large graphs a…
UMap: Enabling Application-driven Optimizations for Page Management
Ivy B. Peng, Marty McFadden, Eric Green +5
Leadership supercomputers feature a diversity of storage, from node-local persistent memory and NVMe SSDs to network-interconnected flash memory and HDD. Memory mapping files on di…
Scalable Breadth-First Search on a GPU Cluster
Yuechao Pan, Roger Pearce, John D. Owens
On a GPU cluster, the ratio of high computing power to communication bandwidth makes scaling breadth-first search (BFS) on a scale-free graph extremely challenging. By separating h…
On Large-Scale Graph Generation with Validation of Diverse Triangle Statistics at Edges and Vertices
Geoffrey Sanders, Roger Pearce, Timothy La Fond +1
Researchers developing implementations of distributed graph analytic algorithms require graph generators that yield graphs sharing the challenging characteristics of real-world gra…
Design, Generation, and Validation of Extreme Scale Power-Law Graphs
Jeremy Kepner, Siddharth Samsi, William Arcand +18
Massive power-law graphs drive many fields: metagenomics, brain mapping, Internet-of-things, cybersecurity, and sparse machine learning. The development of novel algorithms and sys…
Large-Scale Deep Learning on the YFCC100M Dataset
Karl Ni, Roger Pearce, Kofi Boakye +4
We present a work-in-progress snapshot of learning with a 15 billion parameter deep learning network on HPC architectures applied to the largest publicly available natural image an…