1 citations · 2 across the 6 of their papers we have counts for
7 papers
KREX: Concurrent Kernel Benchmarking on Shared GPUs via Region-Granular Exclusivity
Tianyu Feng, Haoxuan Yu, Tianyuan Wu +8
LLM agents automate GPU kernel optimization by repeatedly composing candidates and measuring their duration on real GPUs. Existing systems preserve measurement fidelity by reservin…
ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training
Wenxiang Lin, Xinglin Pan, Ruibo Fan +2
Communication has emerged as a critical bottleneck in the distributed training of large language models (LLMs). While numerous approaches have been proposed to reduce communication…
ZipServ: Fast and Memory-Efficient LLM Inference with Hardware-Aware Lossless Compression
Ruibo Fan, Xiangrui Yu, Xinglin Pan +5
Lossless model compression holds tremendous promise for alleviating the memory and bandwidth bottlenecks in bit-exact Large Language Model (LLM) serving. However, existing approach…
Dissecting Outlier Dynamics in LLM NVFP4 Pretraining
Peijie Dong, Ruibo Fan, Yuechen Tao +11
Training large language models using 4-bit arithmetic enhances throughput and memory efficiency. Yet, the limited dynamic range of FP4 increases sensitivity to outliers. While NVFP…
Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis
Weile Luo, Ruibo Fan, Zeyu Li +4
This study presents a comprehensive multi-level analysis of the NVIDIA Hopper GPU architecture, focusing on its performance characteristics and novel features. We benchmark Hopper'…
Benchmarking and Dissecting the Nvidia Hopper GPU Architecture
Weile Luo, Ruibo Fan, Zeyu Li +3
Graphics processing units (GPUs) are continually evolving to cater to the computational demands of contemporary general-purpose workloads, particularly those driven by artificial i…
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
Longteng Zhang, Xiang Liu, Zeyu Li +8
Large Language Models (LLMs) have seen great advance in both academia and industry, and their popularity results in numerous open-source frameworks and techniques in accelerating L…