5 citations · 7 across the 10 of their papers we have counts for
5 papers · 1 filter
Towards Fast Setup and High Throughput of GPU Serverless Computing
Han Zhao, Weihao Cui, Quan Chen +6
Integrating GPUs into serverless computing platforms is crucial for improving efficiency. However, existing solutions for GPU-enabled serverless computing platforms face two signif…
Accelerating Sparse DNNs Based on Tiled GEMM
Cong Guo, Fengchen Xue, Jingwen Leng +5
Network pruning can reduce the computation cost of deep neural network (DNN) models. However, sparse models often produce randomly-distributed weights to maintain accuracy, leading…
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…
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…