arXiv:2504.20854
Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning
원문 초록
This paper lays the foundation for Genie, a testing framework that captures the impact of real hardware network behavior on ML workload performance, without requiring expensive GPUs. Genie uses CPU-initiated traffic over a hardware testbed to emulate GPU to GPU communication, and adapts the ASTRA-sim simulator to model interaction between the network and the ML workload.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MathOptAI.jl: Embed trained machine learning predictors into JuMP models
ConsumerBench: Benchmarking Generative AI Applications on End-User Devices
Versatile and Fast Location-Based Private Information Retrieval with Fully Homomorphic Encryption over the Torus
SecONNds: Secure Outsourced Neural Network Inference on ImageNet
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration Models
FlashDMoE: Fast Distributed MoE in a Single Kernel
토론
이 논문과 연결된 포럼 글이 아직 없습니다.