150 citations · 178 across the 22 of their papers we have counts for
10 papers · 1 filter
JUNO: Optimizing High-Dimensional Approximate Nearest Neighbour Search with Sparsity-Aware Algorithm and Ray-Tracing Core Mapping
Zihan Liu, Wentao Ni, Jingwen Leng +6
Approximate nearest neighbor (ANN) search is a widely applied technique in modern intelligent applications, such as recommendation systems and vector databases. Therefore, efficien…
Adaptive CPU Resource Allocation for Emulator in Kernel-based Virtual Machine
Yecheng Yang, Pu Pang, Jiawen Wang +2
The technologies of heterogeneous multi-core architectures, co-location, and virtualization can be used to reduce server power consumption and improve system utilization, which are…
STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphs
Jiawen Wang, Quan Chen, Deze Zeng +3
Many emerging user-facing services adopt Graph Neural Networks (GNNs) to improve serving accuracy. When the graph used by a GNN model changes, representations (embedding) of nodes…
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design
Shuwen Lu, Zhihui Zhang, Cong Guo +3
Graph neural networks (GNNs) have shown significant accuracy improvements in a variety of graph learning domains, sparking considerable research interest. To translate these accura…
MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems
Guan Shen, Jieru Zhao, Zeke Wang +5
Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly. As a promising solution achieving high scalability and low manufacturing cost…
DFlow: Efficient Dataflow-based Invocation Workflow Execution for Function-as-a-Service
Xiaoxiang Shi, Chao Li, Zijun Li +5
The Serverless Computing is becoming increasingly popular due to its ease of use and fine-grained billing. These features make it appealing for stateful application or serverless w…
AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs
Yangjie Zhou, Yaoxu Song, Jingwen Leng +7
Graph neural networks (GNNs) are powerful tools for exploring and learning from graph structures and features. As such, achieving high-performance execution for GNNs becomes crucia…
DataFlower: Exploiting the Data-flow Paradigm for Serverless Workflow Orchestration
Zijun Li, Chuhao Xu, Quan Chen +3
Serverless computing that runs functions with auto-scaling is a popular task execution pattern in the cloud-native era. By connecting serverless functions into workflows, tenants c…
OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization
Cong Guo, Jiaming Tang, Weiming Hu +6
Transformer-based large language models (LLMs) have achieved great success with the growing model size. LLMs' size grows by every two years, which outpaces the hardware…
Async-fork: Mitigating Query Latency Spikes Incurred by the Fork-based Snapshot Mechanism from the OS Level
Pu Pang, Gang Deng, Kaihao Bai +12
In-memory key-value stores (IMKVSes) serve many online applications because of their efficiency. To support data backup, popular industrial IMKVSes periodically take a point-in-tim…