124 citations · 256 across the 12 of their papers we have counts for
13 papers
GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
Sandeep Polisetty, Juelin Liu, Kobi Falus +4
Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their superior performance in various graph analytical tasks. Mini…
Tuning the Tail Latency of Distributed Queries Using Replication
Nathan Ng, Hung Le, Marco Serafini
Querying graph data with low latency is an important requirement in application domains such as social networks and knowledge graphs. Graph queries perform multiple hops between ve…
Scalable Graph Neural Network Training: The Case for Sampling
Marco Serafini, Hui Guan
Graph Neural Networks (GNNs) are a new and increasingly popular family of deep neural network architectures to perform learning on graphs. Training them efficiently is challenging…
Accelerating Graph Sampling for Graph Machine Learning using GPUs
Abhinav Jangda, Sandeep Polisetty, Arjun Guha +1
Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and GraphSAGE, s…
Aion: Better Late than Never in Event-Time Streams
Sérgio Esteves, Gianmarco De Francisci Morales, Rodrigo Rodrigues +2
Processing data streams in near real-time is an increasingly important task. In the case of event-timestamped data, the stream processing system must promptly handle late events th…
PushdownDB: Accelerating a DBMS using S3 Computation
Xiangyao Yu, Matt Youill, Matthew Woicik +4
This paper studies the effectiveness of pushing parts of DBMS analytics queries into the Simple Storage Service (S3) engine of Amazon Web Services (AWS), using a recently released…
LiveGraph: A Transactional Graph Storage System with Purely Sequential Adjacency List Scans
Xiaowei Zhu, Guanyu Feng, Marco Serafini +5
The specific characteristics of graph workloads make it hard to design a one-size-fits-all graph storage system. Systems that support transactional updates use data structures with…
Scaling Out Acid Applications with Operation Partitioning
Habib Saissi, Marco Serafini, Neeraj Suri
OLTP applications with high workloads that cannot be served by a single server need to scale out to multiple servers. Typically, scaling out entails assigning a different partition…
Load Balancing for Skewed Streams on Heterogeneous Cluster
Muhammad Anis Uddin Nasir, Hiroshi Horii, Marco Serafini +4
Streaming applications frequently encounter skewed workloads and execute on heterogeneous clusters. Optimal resource utilization in such adverse conditions becomes a challenge, as…
Arabesque: A System for Distributed Graph Mining - Extended version
Carlos H. C. Teixeira, Alexandre J. Fonseca, Marco Serafini +3
Distributed data processing platforms such as MapReduce and Pregel have substantially simplified the design and deployment of certain classes of distributed graph analytics algorit…
When Two Choices Are not Enough: Balancing at Scale in Distributed Stream Processing
Muhammad Anis Uddin Nasir, Gianmarco De Francisci Morales, Nicolas Kourtellis +1
Carefully balancing load in distributed stream processing systems has a fundamental impact on execution latency and throughput. Load balancing is challenging because real-world wor…
The Power of Both Choices: Practical Load Balancing for Distributed Stream Processing Engines
Muhammad Anis Uddin Nasir, Gianmarco De Francisci Morales, David García-Soriano +2
We study the problem of load balancing in distributed stream processing engines, which is exacerbated in the presence of skew. We introduce Partial Key Grouping (PKG), a new stream…
On Barriers and the Gap between Active and Passive Replication (Full Version)
Flavio P. Junqueira, Marco Serafini
Active replication is commonly built on top of the atomic broadcast primitive. Passive replication, which has been recently used in the popular ZooKeeper coordination system, can b…