1.4k citations · 2k across the 34 of their papers we have counts for
34 papers
An In-Depth Study of Continuous Subgraph Matching (Complete Version)
Xibo Sun, Shixuan Sun, Qiong Luo +1
Continuous subgraph matching (CSM) algorithms find the occurrences of a given pattern on a stream of data graphs online. A number of incremental CSM algorithms have been proposed.…
A Simulation Platform for Multi-tenant Machine Learning Services on Thousands of GPUs
Ruofan Liang, Bingsheng He, Shengen Yan +1
Multi-tenant machine learning services have become emerging data-intensive workloads in data centers with heavy usage of GPU resources. Due to the large scale, many tuning paramete…
The Serverless Computing Survey: A Technical Primer for Design Architecture
Zijun Li, Linsong Guo, Jiagan Cheng +3
The development of cloud infrastructures inspires the emergence of cloud-native computing. As the most promising architecture for deploying microservices, serverless computing has…
ThunderRW: An In-Memory Graph Random Walk Engine (Complete Version)
Shixuan Sun, Yuhang Chen, Shengliang Lu +2
As random walk is a powerful tool in many graph processing, mining and learning applications, this paper proposes an efficient in-memory random walk engine named ThunderRW. Compare…
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
Zhaomin Wu, Qinbin Li, Bingsheng He
Federated learning is a learning paradigm to enable collaborative learning across different parties without revealing raw data. Notably, vertical federated learning (VFL), where pa…
Skew-Oblivious Data Routing for Data-Intensive Applications on FPGAs with HLS
Xinyu Chen, Hongshi Tan, Yao Chen +3
FPGAs have become emerging computing infrastructures for accelerating applications in datacenters. Meanwhile, high-level synthesis (HLS) tools have been proposed to ease the progra…
Cache-Efficient Fork-Processing Patterns on Large Graphs
Shengliang Lu, Shixuan Sun, Johns Paul +2
As large graph processing emerges, we observe a costly fork-processing pattern (FPP) that is common in many graph algorithms. The unique feature of the FPP is that it launches many…
Model-Contrastive Federated Learning
Qinbin Li, Bingsheng He, Dawn Song
Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to ha…
Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload
Johan Kok Zhi Kang, Gaurav, Sien Yi Tan +3
The use of deep learning models for forecasting the resource consumption patterns of SQL queries have recently been a popular area of study. With many companies using cloud platfor…
PathEnum: Towards Real-Time Hop-Constrained s-t Path Enumeration
Shixuan Sun, Yuhang Chen, Bingsheng He +1
We study the hop-constrained s-t path enumeration (HcPE) problem, which takes a graph , two distinct vertices and a hop constraint as input, and outputs all paths from…
Federated Learning on Non-IID Data Silos: An Experimental Study
Qinbin Li, Yiqun Diao, Quan Chen +1
Due to the increasing privacy concerns and data regulations, training data have been increasingly fragmented, forming distributed databases of multiple "data silos" (e.g., within d…
DBL: Efficient Reachability Queries on Dynamic Graphs (Complete Version)
Qiuyi Lyu, Yuchen Li, Bingsheng He +1
Reachability query is a fundamental problem on graphs, which has been extensively studied in academia and industry. Since graphs are subject to frequent updates in many application…
A Survey of Non-Volatile Main Memory Technologies: State-of-the-Arts, Practices, and Future Directions
Haikun Liu, Di Chen, Hai Jin +4
Non-Volatile Main Memories (NVMMs) have recently emerged as promising technologies for future memory systems. Generally, NVMMs have many desirable properties such as high density,…
Practical One-Shot Federated Learning for Cross-Silo Setting
Qinbin Li, Bingsheng He, Dawn Song
Federated learning enables multiple parties to collaboratively learn a model without exchanging their data. While most existing federated learning algorithms need many rounds to co…
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
Sixu Hu, Yuan Li, Xu Liu +3
This paper presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems. Previously available b…
Hardware-Conscious Stream Processing: A Survey
Shuhao Zhang, Feng Zhang, Yingjun Wu +2
Data stream processing systems (DSPSs) enable users to express and run stream applications to continuously process data streams. To achieve real-time data analytics, recent researc…
Practical Federated Gradient Boosting Decision Trees
Qinbin Li, Zeyi Wen, Bingsheng He
Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several rec…
Adaptive Kernel Value Caching for SVM Training
Qinbin Li, Zeyi Wen, Bingsheng He
Support Vector Machines (SVMs) can solve structured multi-output learning problems such as multi-label classification, multiclass classification and vector regression. SVM training…
Privacy-Preserving Gradient Boosting Decision Trees
Qinbin Li, Zhaomin Wu, Zeyi Wen +1
The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT whil…
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Qinbin Li, Zeyi Wen, Zhaomin Wu +5
Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As…
BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures
Shuhao Zhang, Jiong He, Amelie Chi Zhou +1
We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution…
Towards Concurrent Stateful Stream Processing on Multicore Processors (Technical Report)
Shuhao Zhang, Yingjun Wu, Feng Zhang +1
Recent data stream processing systems (DSPSs) can achieve excellent performance when processing large volumes of data under tight latency constraints. However, they sacrifice suppo…
A Survey on Graph Processing Accelerators: Challenges and Opportunities
Chuangyi Gui, Long Zheng, Bingsheng He +4
Graph is a well known data structure to represent the associated relationships in a variety of applications, e.g., data science and machine learning. Despite a wealth of existing e…
Efficient Memory Management for GPU-based Deep Learning Systems
Junzhe Zhang, Sai Ho Yeung, Yao Shu +2
GPU (graphics processing unit) has been used for many data-intensive applications. Among them, deep learning systems are one of the most important consumer systems for GPU nowadays…