Search Results for author: Kevin Barker

Found 4 papers, 2 papers with code

The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning

no code implementations5 Dec 2023 Omer Subasi, Oceane Bel, Joseph Manzano, Kevin Barker

With the advance of the powerful heterogeneous, parallel and distributed computing systems and ever increasing immense amount of data, machine learning has become an indispensable part of cutting-edge technology, scientific research and consumer products.

Distributed Computing Federated Learning

Evaluating Emerging AI/ML Accelerators: IPU, RDU, and NVIDIA/AMD GPUs

no code implementations8 Nov 2023 Hongwu Peng, Caiwen Ding, Tong Geng, Sutanay Choudhury, Kevin Barker, Ang Li

The relentless advancement of artificial intelligence (AI) and machine learning (ML) applications necessitates the development of specialized hardware accelerators capable of handling the increasing complexity and computational demands.

MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms

1 code implementation14 Sep 2022 yuke wang, Boyuan Feng, Zheng Wang, Tong Geng, Kevin Barker, Ang Li, Yufei Ding

For irregularly sparse and fine-grained GNN workloads, such solutions miss the opportunity to jointly schedule/optimize the computation and communication operations for high-performance delivery.

Layout Design Management

Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect

1 code implementation11 Mar 2019 Ang Li, Shuaiwen Leon Song, Jieyang Chen, Jiajia Li, Xu Liu, Nathan Tallent, Kevin Barker

High performance multi-GPU computing becomes an inevitable trend due to the ever-increasing demand on computation capability in emerging domains such as deep learning, big data and planet-scale simulations.

Hardware Architecture Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Performance

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