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John Thorpe

dblp:228/0264 · DBLP profile ↗
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4ranked-venue papers
2as first author
2since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1Computer networks · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Graph learning · 43% Efficient and distributed learning · 28% Multi-agent systems · 28%
Software engineering, system software, and programming languages
1 paper
Program verification · 67% Program analysis · 33%
Computer architecture, parallel and distributed computing, and storage systems
3 papers
Cloud and datacenter computing · 89% Distributed systems · 11%
Databases, data mining, and information retrieval
1 paper
Data mining · 50% Data stream processing · 50%

Topics — the 11 heaviest of 13, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
distributed training
0.712023
Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs · NSDI 2023
Knowledge, reasoning and agents › Multi-agent systems
resilience
0.712023
Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs · NSDI 2023
Machine learning › Graph learning › graph neural network training
distributed GNN training
0.512021
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads · OSDI 2021
Machine learning › Graph learning
graph neural network training
0.512021
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads · OSDI 2021
Cloud and datacenter computing
serverless computing
0.512021
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads · OSDI 2021
Program verification › model checking
finite-state verification
0.412019
Grapple: A Graph System for Static Finite-State Property Checking of Large-Scale Systems Code · EuroSys 2019
Program verification › model checking
software model checking
0.412019
Grapple: A Graph System for Static Finite-State Property Checking of Large-Scale Systems Code · EuroSys 2019
Program analysis
static analysis
0.412019
Grapple: A Graph System for Static Finite-State Property Checking of Large-Scale Systems Code · EuroSys 2019
Data mining › structured data mining
graph mining
0.312018
RStream: Marrying Relational Algebra with Streaming for Efficient Graph Mining on A Single Machine · OSDI 2018
Cloud and datacenter computing › cluster resource management and scheduling
cluster resource management
0.212023
Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs · NSDI 2023
Cloud and datacenter computing › utility computing › cloud pricing
spot instance
0.212023
Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs · NSDI 2023

Methods — techniques the papers use, named apart from their topics

serverless computing · 1.0distributed training · 1.0graph-based analysis · 0.8relational algebra · 0.3
YearPublicationVenuePosition
2023 Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs
John Thorpe, Pengzhan Zhao, Jon Eyolfson, Yifan Qiao 0002, Minjia Zhang, Ravi Netravali, Guoqing Harry Xu
NSDI1
2021 Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads
John Thorpe, Yifan Qiao 0002, Jon Eyolfson, Shen Teng, Guanzhou Hu, Jinliang Wei, Keval Vora, Ravi Netravali, Miryung Kim, Guoqing Harry Xu
OSDI1
2019 Grapple: A Graph System for Static Finite-State Property Checking of Large-Scale Systems Code
abstract
Many real-world bugs in large-scale systems are related to object state that is supposed to obey a specified finite state machine (FSM). They are triggered when unexpected events occur on objects in certain states, making these objects transition in a way that violates their specifications. Detecting such FSM-related bugs with static analysis is challenging, especially in distributed systems that have large codebases.
Zhiqiang Zuo 0002, John Thorpe, Qiuhong Pan, Shenming Lu, Kai Wang 0029, Guoqing Harry Xu, Linzhang Wang, Xuandong Li
EuroSys2
2018 RStream: Marrying Relational Algebra with Streaming for Efficient Graph Mining on A Single Machine
Kai Wang 0029, Zhiqiang Zuo 0002, John Thorpe, Tien Quang Nguyen, Guoqing Harry Xu
OSDI3