Search Results for author: Nicholas Meisburger

Found 4 papers, 1 papers with code

Distributed SLIDE: Enabling Training Large Neural Networks on Low Bandwidth and Simple CPU-Clusters via Model Parallelism and Sparsity

no code implementations29 Jan 2022 Minghao Yan, Nicholas Meisburger, Tharun Medini, Anshumali Shrivastava

We show that with reduced communication, due to sparsity, we can train close to a billion parameter model on simple 4-16 core CPU nodes connected by basic low bandwidth interconnect.

Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More

2 code implementations6 Mar 2021 Shabnam Daghaghi, Nicholas Meisburger, Mengnan Zhao, Yong Wu, Sameh Gobriel, Charlie Tai, Anshumali Shrivastava

Our work highlights several novel perspectives and opportunities for implementing randomized algorithms for deep learning on modern CPUs.

A Tale of Two Efficient and Informative Negative Sampling Distributions

no code implementations31 Dec 2020 Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava

Unfortunately, due to the dynamically updated parameters and data samples, there is no sampling scheme that is provably adaptive and samples the negative classes efficiently.

Information Retrieval Retrieval

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