12 citations · 13 across the 6 of their papers we have counts for
4 papers · 1 filter
Tangram: Hiding GPU Heterogeneity for Efficient LLM Parallelization
Yanda Tao, Pedro F. Silvestre, Marcel Wagenländer +1
The scale of LLM training jobs requires parallelization planning over large GPU clusters. Due to different GPU types and interconnects added over time, these GPU clusters are incre…
Scepsy: Serving Agentic Workflows Using Aggregate LLM Pipelines
Marcel Wagenländer, Otto White, Britannio Jarrett +9
Agentic workflows carry out complex tasks by orchestrating multiple large language models (LLMs) and tools. Serving them at a target throughput with low latency is hard because the…
Cloud abstractions for AI workloads
Marco Canini, Theophilus A. Benson, Ricardo Bianchini +4
AI workloads, often hosted in multi-tenant cloud environments, require vast computational resources but suffer inefficiencies due to limited tenant-provider coordination. Tenants l…
Tenplex: Dynamic Parallelism for Deep Learning using Parallelizable Tensor Collections
Marcel Wagenländer, Guo Li, Bo Zhao +2
Deep learning (DL) jobs use multi-dimensional parallelism, i.e. combining data, model, and pipeline parallelism, to use large GPU clusters efficiently. Long-running jobs may experi…