6 citations · 11 across the 5 of their papers we have counts for
5 papers
MSCCL++: Rethinking GPU Communication Abstractions for AI Inference
Changho Hwang, Peng Cheng, Roshan Dathathri +12
AI applications increasingly run on fast-evolving, heterogeneous hardware to maximize performance, but general-purpose libraries lag in supporting these features. Performance-minde…
ForestColl: Throughput-Optimal Collective Communications on Heterogeneous Network Fabrics
Liangyu Zhao, Saeed Maleki, Yuanhong Wang +4
As modern DNN models grow ever larger, collective communications between the accelerators (allreduce, etc.) emerge as a significant performance bottleneck. Designing efficient comm…
Tessel: Boosting Distributed Execution of Large DNN Models via Flexible Schedule Search
Zhiqi Lin, Youshan Miao, Guanbin Xu +4
Increasingly complex and diverse deep neural network (DNN) models necessitate distributing the execution across multiple devices for training and inference tasks, and also require…
Splitwise: Efficient generative LLM inference using phase splitting
Pratyush Patel, Esha Choukse, Chaojie Zhang +4
Recent innovations in generative large language models (LLMs) have made their applications and use-cases ubiquitous. This has led to large-scale deployments of these models, using…
Look-Up mAI GeMM: Increasing AI GeMMs Performance by Nearly 2.5x via msGeMM
Saeed Maleki
AI models are increasing in size and recent advancement in the community has shown that unlike HPC applications where double precision datatype are required, lower-precision dataty…