Yuxiong He

TPC

2016

In interactive services such as web search, recommendations, games and finance, reducing the tail latency is crucial to provide fast response to every user. Using web search as a driving example, we systematically characterize interactive …

Navigating with Graph Representations for Fast and Scalable Decoding of\n Neural Language Models

2018 · arXiv (Cornell University)

Neural language models (NLMs) have recently gained a renewed interest by\nachieving state-of-the-art performance across many natural language processing\n(NLP) tasks. However, NLMs are very computationally demanding largely due to\nthe computational cost of the softmax layer over …

Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers

2022 · arXiv (Cornell University)

Large-scale transformer models have become the de-facto architectures for various machine learning applications, e.g., CV and NLP. However, those large models also introduce prohibitive training costs. To mitigate this issue, we propose a novel random …

HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs

2023 · arXiv (Cornell University)

Collaborative filtering (CF) has been proven to be one of the most effective techniques for recommendation. Among all CF approaches, SimpleX is the state-of-the-art method that adopts a novel loss function and a proper number …

MCR-DL: Mix-and-Match Communication Runtime for Deep Learning

2023

In recent years, the training requirements of many state-of-the-art Deep Learning (DL) models have scaled beyond the compute and memory capabilities of a single processor, and necessitated distribution among processors. Training such massive models necessitates …

ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering

2025 · arXiv (Cornell University)

Retrieval-Augmented Generation (RAG) systems are increasingly diverse, yet many suffer from monolithic designs that tightly couple core functions like query reformulation, retrieval, reasoning, and verification. This limits their interpretability, systematic evaluation, and targeted improvement, especially …

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

2022 · arXiv (Cornell University)

Pretrained general-purpose language models can achieve state-of-the-art accuracies in various natural language processing domains by adapting to downstream tasks via zero-shot, few-shot and fine-tuning techniques. Because of their success, the size of these models has …