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05/10/2023
P4SGD: Programmable Switch Enhanced Model-Parallel Training on Generalized Linear Models on Distributed FPGAs
Generalized linear models (GLMs) are a widely utilized family of machine...
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05/03/2023
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
Sharding a large machine learning model across multiple devices to balan...
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04/27/2023
Self-discipline on multiple channels
Self-distillation relies on its own information to improve the generaliz...
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04/21/2023
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
It is widely acknowledged that large models have the potential to delive...
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10/28/2021
Characterizing and Taming Resolution in Convolutional Neural Networks
Image resolution has a significant effect on the accuracy and computatio...
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05/28/2021
Cloud Collectives: Towards Cloud-aware Collectives forML Workloads with Rank Reordering
ML workloads are becoming increasingly popular in the cloud. Good cloud ...
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04/21/2021
Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks
Graph neural networks (GNNs), an emerging deep learning model class, can...
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04/12/2021
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Deep learning recommendation models (DLRMs) are used across many busines...
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11/29/2020
Srift: Swift and Thrift Cloud-Based Distributed Training
Cost-efficiency and training time are primary concerns in cloud-based di...
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05/21/2018
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training
Distributed deep neural network (DDNN) training constitutes an increasin...
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01/30/2018
