12 citations · 13 across the 6 of their papers we have counts for
6 papers
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…
Nanvix: A Multikernel OS Design for High-Density Serverless Deployments
Carlos Segarra, Pedro Henrique Penna, Enrique Saurez +4
Serverless providers strive for high resource utilization by optimizing deployment density: how many applications can be deployed per host server. However, achieving high deploymen…
ARCAS: Adaptive Runtime System for Chiplet-Aware Scheduling
Alessandro Fogli, Bo Zhao, Peter Pietzuch +1
The growing disparity between CPU core counts and available memory bandwidth has intensified memory contention in servers. This particularly affects highly parallelizable applicati…
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…