Microsoft
We evaluate the feasibility of PANAMA using an FPGA-based prototype with 10~Gbps transceivers and large-scale simulations. Our simulation results demonstrate that PANAMA decreases the average
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Panama Aggregation Switch
In-network Aggregation for Shared Machine Learning Clusters
This work co-designs the switch processing with the end-host protocols and ML frameworks to provide a robust, efficient solution that speeds up training by up to 300%, and at least by 20% for a number of
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In-network Aggregation for Shared Machine Learning Clusters
PANAMA: ProgrAmmable Network Architecture for ML Applications Bump-in-the-wire accelerator for line-rate aggregation Congestion control for fair sharing of network resources Load-balancing
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In-Network Aggregation for Shared Machine Learning Clusters
It presents an in-network aggregation framework called PANAMA for distributed ML training tasks. PANAMA has two components: (1) an in-network hardware accelerator with support
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In-network Aggregation for Shared Machine Learning Clusters
We evaluate the feasibility of P ANAMA using an FPGA-based prototype with 10 Gbps transceivers and large-scale simulations. Our simulation results demonstrate that P ANAMA decreases the average
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PANAMA: In-network Aggregation for Shared Machine Learning Clusters
We present PANAMA, a novel in-network aggregation framework for distributed machine learning (ML) training on shared clusters serving a variety of jobs.
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In-network Aggregation for Shared Machine Learning Clusters
To evaluate the feasibility of PANAMA, we build an FPGA-based prototype with 10 Gbps transceivers and show that our hardware datapath achieves line-rate aggregation.
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