Gemini: A Computation-Centric Distributed Graph Processing System
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Summary
Gemini is a distributed graph processing system that applies multiple optimizations targeting computation performance to build scalability on top of efficiency, and significantly outperforms all well-known existing distributed graph processing systems.
- Type
- article
- Published
- 2016-11-02
- Cited by
- 445
- References
- 63
- OpenAlex
- https://openalex.org/W2524623326
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3625061
Keywords
Computer science, Scalability, Parallel computing, Distributed computing, Load balancing (electrical power)
References
- PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs
- Large-scale Graph Computation on Just a PC
- Scalability! But at what COST?
- Making Sense of Performance in Data Analytics Frameworks
- GraphMat: High performance graph analytics made productive
- GraphChi: Large-Scale Graph Computation on Just a PC
- GridGraph: Large-Scale Graph Processing on a Single Machine Using 2-Level Hierarchical Partitioning
- The PageRank Citation Ranking : Bringing Order to the Web
- The Anatomy of the Facebook Social Graph
- LLAMA: Efficient graph analytics using Large Multiversioned Arrays
- GPS: a graph processing system
- Streaming graph partitioning for large distributed graphs
- GraM: scaling graph computation to the trillions
- On power-law relationships of the Internet topology
- A large time-aware web graph
- TurboGraph: a fast parallel graph engine handling billion-scale graphs in a single PC
- The webgraph framework I: compression techniques
- Arabesque: a system for distributed graph mining
- Ligra: a lightweight graph processing framework for shared memory
- Graph Compression by BFS
Cited by
- Big Graph Analytics Platforms
- ForeGraph: Exploring Large-scale Graph Processing on Multi-FPGA Architecture
- Evolution of Cloud Operating System: From Technology to Ecosystem
- ANG: a combination of Apriori and graph computing techniques for frequent itemsets mining
- Mosaic: Processing a Trillion-Edge Graph on a Single Machine
- Everything you always wanted to know about multicore graph processing but were afraid to ask
- To Push or To Pull: On Reducing Communication and Synchronization in Graph Computations
- On Achieving Efficient Data Transfer for Graph Processing in Geo-Distributed Datacenters
- Towards Dataflow-Based Graph Accelerator
- Analysis and Evaluation of the GAS Model for Distributed Graph Computation
- GraphA: Adaptive Partitioning for Natural Graphs
- Garaph: Efficient GPU-accelerated Graph Processing on a Single Machine with Balanced Replication
- Squeezing out All the Value of Loaded Data: An Out-of-core Graph Processing System with Reduced Disk I/O
- Data provenance to audit compliance with privacy policy in the Internet of Things
- FBSGraph: Accelerating Asynchronous Graph Processing via Forward and Backward Sweeping
- Realizing Memory-Optimized Distributed Graph Processing
- GraphA: Efficient Partitioning and Storage for Distributed Graph Computation
- Distributed PathGraph: A Cluster Centric Framework for Distributed Processing Graph
- Puffin: Graph Processing System on Multi-GPUs
- Towards memory and computation efficient graph processing on spark
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