Scaling distributed machine learning with {In-Network} aggregation

A Sapio, M Canini, CY Ho, J Nelson, P Kalnis… - … USENIX Symposium on …, 2021 - usenix.org
Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …
被引用次数：417 相关文章
[PDF] kaust.edu.sa

[PDF][PDF] Scaling Distributed Machine Learning with In-Network Aggregation

A Sapio, M Canini, CY Ho, J Nelson, P Kalnis, C Kim… - sands.kaust.edu.sa
Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …
[PDF] researchgate.net

[PDF][PDF] Scaling Distributed Machine Learning with In-Network Aggregation

A Sapio, M Canini, CY Ho, J Nelson, P Kalnis, C Kim… - researchgate.net
Training complex machine learning models in parallel is an increasingly important workload.
We accelerate distributed parallel training by designing a communication primitive that uses …
[PDF] github.io

[PDF][PDF] Scaling Distributed Machine Learning with In-Network Aggregation

A Sapio, M Canini, CY Ho, J Nelson, P Kalnis, C Kim… - mcanini.github.io
Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …
[PDF] washington.edu

[PDF][PDF] Scaling Distributed Machine Learning with In-Network Aggregation

A Sapio, M Canini, CY Ho, J Nelson, P Kalnis, C Kim… - homes.cs.washington.edu
Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …
[PDF] drkp.com

[PDF][PDF] Scaling Distributed Machine Learning with In-Network Aggregation

A Sapio, M Canini, CY Ho, J Nelson, P Kalnis, C Kim… - drkp.com
Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …

Scaling Distributed Machine Learning with In-Network Aggregation

A Sapio, M Canini, CY Ho, J Nelson, P Kalnis, C Kim… - 2021 - repository.kaust.edu.sa
Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …
[PDF] drkp.com

[PDF][PDF] Scaling Distributed Machine Learning with In-Network Aggregation

A Sapio, M Canini, CY Ho, J Nelson, P Kalnis, C Kim… - drkp.com
Training complex machine learning models in parallel is an increasingly important workload.
We accelerate distributed parallel training by designing a communication primitive that uses …

Scaling Distributed Machine Learning with In-Network Aggregation

A Sapio, M Canini, CY Ho, J Nelson, P Kalnis… - arXiv e …, 2019 - ui.adsabs.harvard.edu
Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …

Scaling Distributed Machine Learning with {In-Network} Aggregation

A Sapio, M Canini, CY Ho, J Nelson, P Kalnis… - … USENIX Symposium on …, 2021 - usenix.org
Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …