131 citations · 326 across the 20 of their papers we have counts for
5 papers · 1 filter
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Mingchao Yu, Zhifeng Lin, Krishna Narra +6
Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation. To allev…
Amber: Enabling Precise Full-System Simulation with Detailed Modeling of All SSD Resources
Donghyun Gouk, Miryeong Kwon, Jie Zhang +5
SSDs become a major storage component in modern memory hierarchies, and SSD research demands exploring future simulation-based studies by integrating SSD subsystems into a full-sys…
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Youjie Li, Mingchao Yu, Songze Li +3
Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands. Classical dis…
CIAO: Cache Interference-Aware Throughput-Oriented Architecture and Scheduling for GPUs
Jie Zhang, Shuwen Gao, Nam Sung Kim +1
A modern GPU aims to simultaneously execute more warps for higher Thread-Level Parallelism (TLP) and performance. When generating many memory requests, however, warps contend for l…
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks
Amir Yazdanbakhsh, Hajar Falahati, Philip J. Wolfe +3
Generative Adversarial Networks (GANs) are one of the most recent deep learning models that generate synthetic data from limited genuine datasets. GANs are on the frontier as furth…