High Performance Distributed Graph Analytics

High Performance Distributed Graph Analytics Information Guide

1. Background of High Performance Distributed Graph Analytics
2. Main Features
3. Recent Updates
4. Detailed Analysis
5. Final Thoughts

Background of High Performance Distributed Graph Analytics

Julian Shun is an Associate Professor at MIT in the EECS department and a principal investigator in CSAIL. He earned his Ph.D. In this video from the PASC16 conference, Andrew Lumsdaine from Indiana University presents: Context Matters: Hello everyone um I'm very honored to be here to introduce Atlas The minisymposium will be in two parts: first there will be three scientific presentations from HPC experts from different ... Joseph Gonzalez and Reynold Xin of the UC Berkeley AmpLab present the GraphX

Main Features

Recent Updates

Understanding The Importance Of Graph Analytics

High-Performance Matrix-Based Graph Analysis Minisymposium

Distributed temporal graph analytics with GRADOOP

Short - Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite

Context Matters: Distributed Graph Algorithms and Runtime Systems

Intel Graph Analytics & AI: An Efficient Way to Analyze Massive Datasets

AtlasGraph: A High-Performance Graph Database Solution for Complex Data Challenges

High Performance Graph Analytics

AmpLab GraphX: Graph Analytics on Spark

Detailed Analysis

Data is compiled from public records and verified media reports.

Last Updated: June 17, 2026

Final Thoughts

Disclaimer: Disclaimer: Details estimates are based on publicly available data, media reports, and financial analysis. Actual numbers may vary.