6 citations · 7 across the 6 of their papers we have counts for
6 papers
Hardware-accelerated Aggregation: Unification and Specialization
Alireza Shateri, Hongshi Tan, Michael Ng +2
The high efficiency of domain-specific hardware has sparked substantial interest in adopting accelerators in data analytics systems. Among many choices, GPUs and FPGAs thrived as t…
RidgeWalker: Perfectly Pipelined Graph Random Walks on FPGAs
Hongshi Tan, Yao Chen, Xinyu Chen +4
Graph Random Walks (GRWs) offer efficient approximations of key graph properties and have been widely adopted in many applications. However, GRW workloads are notoriously difficult…
dpBento: Benchmarking DPUs for Data Processing
Jiasheng Hu, Chihan Cui, Anna Li +5
Data processing units (DPUs, SoC-based SmartNICs) are emerging data center hardware that provide opportunities to address cloud data processing challenges. Their onboard compute, m…
DDS: DPU-optimized Disaggregated Storage [Extended Report]
Qizhen Zhang, Philip Bernstein, Badrish Chandramouli +2
This extended report presents DDS, a novel disaggregated storage architecture enabled by emerging networking hardware, namely DPUs (Data Processing Units). DPUs can optimize the la…
DPDPU: Data Processing with DPUs
Jiasheng Hu, Philip A. Bernstein, Jialin Li +1
Improving the performance and reducing the cost of cloud data systems is increasingly challenging. Data processing units (DPUs) are a promising solution, but utilizing them for dat…
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
Yao Lu, Song Bian, Lequn Chen +19
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures. Recent large models such as ChatGPT, while revolutionary in t…