Daily | Yixun Hong

Eidola: Modeling Multi-GPU Network Communication Traffic in Distributed AI Workloads

Ranganath R. Selagamsetty, Matthew Poremba, Bradford M. Beckmann, Joshua San Miguel 2026-06-14

Gem5 Interconnect Microarchitecture Simulation HPC Compiler Runtime × GPU

Eidola addresses the problem of modeling irregular and transient inter-GPU communication traffic in distributed AI workloads, which existing tools fail to capture due to fine-grained synchronization and peer-to-peer writes. The method introduces a scalable gem5 extension that uses annotated timing profiles from real applications to emulate peer-to-peer GPU writes with cycle-level precision. Experimental evidence demonstrates Eidola's effectiveness by reproducing variability in fused kernel execution and confirming reductions in polling-related memory traffic via a SyncMon-inspired mechanism. This matters because Eidola provides a flexible platform for architectural exploration of interconnect bandwidth and latency in modern multi-GPU systems.

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Characterizing Software Aging in GPU-Based LLM Serving Systems

Domenico Cotroneo, Bojan Cukic 2026-06-14

GPU LLM Serving

The paper addresses the problem of software aging in GPU-based LLM serving systems, which differ from traditional CPU-centric systems due to heterogeneous hardware and highly variable workloads. The method involves a 216-hour empirical campaign across six co-located deployments with identical stress, monitoring host, device, and client metrics and applying a statistical pipeline for autocorrelation and multiple testing. Experimental evidence shows statistically significant memory aging in all deployments, with leak rates strongly dependent on the serving runtime and configuration. This matters because it provides a reproducible framework bridging software aging and rejuvenation research with LLM serving, enabling future mitigation strategies.

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GF-DiT: Scheduling Parallelism for Diffusion Transformer Serving

Xinwei Qiang, Yifan Hu, Shixuan Sun, Jing Yang 2026-06-14

Transformer GPU Runtime × Scheduling Parallelism Serving

The problem is that existing Diffusion Transformer (DiT) serving systems use static parallelism for each request, which is inefficient due to heterogeneity across requests, execution stages, and system conditions. GF-DiT introduces a policy-programmable runtime that dynamically adapts parallelism via an asynchronous execution abstraction and group-free collectives for low-overhead online GPU reallocation. Experimental evaluation in vLLM-Omni shows GF-DiT improves throughput by up to 6.01×, reduces mean latency by up to 95%, and lowers SLO violation rates by up to 90% compared to fixed-pipeline execution. This matters because it enables efficient, elastic DiT serving that treats GPU parallelism as a schedulable resource, significantly improving performance and service quality for image and video generation workloads.

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Work Stealing for the 2D-Mesh Topology of Satellite Constellations in Low Earth Orbit

Mia Reitz, Dorian Chenet, Jonas Posner 2026-06-14

High Performance Computing HPC Compiler Runtime ×

The problem is that existing Asynchronous Many-Task (AMT) runtimes assume a fully connected network with low, uniform latency, which is invalid for satellite constellations in Low Earth Orbit (LEO) that communicate via a sparse mesh topology. The method proposes a neighbor-only work stealing strategy where workers steal exclusively from directly connected neighbors to avoid multi-hop communication. Experimental evidence on an HPC cluster with an emulated mesh shows the neighbor-only strategy performs within ~2.2% of global stealing on both balanced and irregular workloads, and an analytical model indicates a growing latency advantage with constellation size. This matters because it demonstrates that neighbor-only stealing can match global stealing performance in emulated settings, suggesting it is a viable and potentially preferable approach for adapting AMT to Space Edge Computing (SEC) at scale.

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