26 citations · 39 across the 5 of their papers we have counts for
7 papers
DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models
Jinpeng Li, Zhongyi Pei, Huaze Xue +3
Time-series foundation models (TSFMs) have achieved strong univariate forecasting through large-scale pre-training, yet effectively extending this success to multivariate forecasti…
Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization
Zhanda Zhu, Christina Giannoula, Muralidhar Andoorveedu +4
Various parallelism, such as data, tensor, and pipeline parallelism, along with memory optimizations like activation checkpointing, redundancy elimination, and offloading, have bee…
Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction
Muralidhar Andoorveedu, Zhanda Zhu, Bojian Zheng +1
Training deep learning models can be computationally expensive. Prior works have shown that increasing the batch size can potentially lead to better overall throughput. However, th…
Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs
Yaoyao Ding, Cody Hao Yu, Bojian Zheng +3
As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide effic…
Automatic Horizontal Fusion for GPU Kernels
Ao Li, Bojian Zheng, Gennady Pekhimenko +1
We present automatic horizontal fusion, a novel optimization technique that complements the standard kernel fusion techniques for GPU programs. Unlike the standard fusion, whose go…
Echo: Compiler-based GPU Memory Footprint Reduction for LSTM RNN Training
Bojian Zheng, Abhishek Tiwari, Nandita Vijaykumar +1
The Long-Short-Term-Memory Recurrent Neural Networks (LSTM RNNs) are a popular class of machine learning models for analyzing sequential data. Their training on modern GPUs, howeve…