10 citations · 18 across the 6 of their papers we have counts for
7 papers · 1 filter
ADARES: Adaptive Resource Management for Virtual Machines
Ignacio Cano, Lequn Chen, Pedro Fonseca +5
Virtual execution environments allow for consolidation of multiple applications onto the same physical server, thereby enabling more efficient use of server resources. However, use…
A Hardware-Software Blueprint for Flexible Deep Learning Specialization
Thierry Moreau, Tianqi Chen, Luis Vega +8
Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance…
Revisiting Network Support for RDMA
Radhika Mittal, Alexander Shpiner, Aurojit Panda +4
The advent of RoCE (RDMA over Converged Ethernet) has led to a significant increase in the use of RDMA in datacenter networks. To achieve good performance, RoCE requires a lossless…
Learning to Optimize Tensor Programs
Tianqi Chen, Lianmin Zheng, Eddie Yan +5
We introduce a learning-based framework to optimize tensor programs for deep learning workloads. Efficient implementations of tensor operators, such as matrix multiplication and hi…
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training
Liang Luo, Jacob Nelson, Luis Ceze +2
Distributed deep neural network (DDNN) training constitutes an increasingly important workload that frequently runs in the cloud. Larger DNN models and faster compute engines are s…
Volur: Concurrent Edge/Core Route Control in Data Center Networks
Qiao Zhang, Danyang Zhuo, Vincent Liu +4
A perennial question in computer networks is where to place functionality among components of a distributed computer system. In data centers, one option is to move all intelligence…