Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1905.10603

Propagation and Decay of Injected One-Off Delays on Clusters: A Case Study

Ayesha Afzal·Georg Hager·Gerhard Wellein
View original source ↗Read source document ↗Export metadata ↓

Abstract

Analytic, first-principles performance modeling of distributed-memory applications is difficult due to a wide spectrum of random disturbances caused by the application and the system. These disturbances (commonly called "noise") destroy the assumptions of regularity that one usually employs when constructing simple analytic models. Despite numerous efforts to quantify, categorize, and reduce such effects, a comprehensive quantitative understanding of their performance impact is not available, especially for long delays that have global consequences for the parallel application. In this work, we investigate various traces collected from synthetic benchmarks that mimic real applications on simulated and real message-passing systems in order to pinpoint the mechanisms behind delay propagation. We analyze the dependence of the propagation speed of idle waves emanating from injected delays with respect to the execution and communication properties of the application, study how such delays decay under increased noise levels, and how they interact with each other. We also show how fine-grained noise can make a system immune against the adverse effects of propagating idle waves. Our results contribute to a better understanding of the collective phenomena that manifest themselves in distributed-memory parallel applications.

Explore related subjects

cs.PF

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ayesha Afzal, Georg Hager, Gerhard Wellein. 2019-08-28. Propagation and Decay of Injected One-Off Delays on Clusters: A Case Study. https://doi.org/10.1109/cluster.2019.8890995

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Performance Analysis of Low-Order, GPU-accelerated Finite Element Kernels using Kokkos

We study performance portability for low-order, matrix-free finite element kernels, using the example of a vectorial, variable-coefficient PDE operator originating in geophysical models. Written in Kokkos, the kernel is compared on NVIDIA H100, AMD MI250X, AMD MI300A and Intel PVC Max 1550 GPUs. Owing to its low order and to optimizations that reduce the arithmetic, the kernel has a low arithmetic intensity, so that its performance is determined by how the finite element assembly is mapped onto the memory hierarchy. This is a dimension in which the architectures differ even within one vendor family, causing different performance characteristics. We examine how Kokkos' hierarchical parallelism and shared scratch memory, which are used for the shared degrees of freedom of the conforming discretization, behave on each device. Finally, we show how portability gaps can be narrowed with tuning levers such as the size of the thread groups, the balance between occupancy and register use, and the atomic accumulation strategy at the end of the kernel.

cs.PF↗
arXiv2026

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

Repeated prompt prefixes are increasingly common in LLM serving workloads, appearing in system prompts, templated retrieval-augmented generation pipelines, agent frameworks, and multi-turn conversations. Modern inference runtimes such as vLLM and TensorRT-LLM provide mechanisms for reusing previously computed KV-cache state across requests, yet it remains unclear when prefix reuse materially improves serving performance on contemporary accelerators and when its benefits are limited by scheduling, cache granularity, concurrency, or memory pressure. This paper presents PrefixBench-H100, a reproducible benchmark and measurement framework for characterizing prefix reuse on a single NVIDIA H100. PrefixBench-H100 combines controlled synthetic traces with chat-style and retrieval-style workloads, and evaluates two widely used LLM serving runtimes under matched workload conditions. The benchmark varies shared-prefix length, suffix diversity, request arrival pattern, concurrency, output length, and cache configuration, while collecting time-to-first-token, inter-token latency, end-to-end latency, throughput, cache-hit statistics, GPU memory usage, and selected profiling traces. The goal of PrefixBench-H100 is not to introduce a new caching algorithm, but to expose the practical operating envelope of prefix reuse for H100-class LLM serving. The study identifies the regime where prefix reuse provides substantial first-token latency reductions and the regime where cache pressure erodes them, while showing that cache effectiveness itself is largely insensitive to concurrency and output length; the cross-runtime differences that remain arise above the cache, in the scheduling layer.

cs.PF↗
arXiv2026

Whittle index approach to multi-server scheduling with convex delay costs and impatient customers

We consider the dynamic scheduling problem in a multi-class M/G/N + M queue with convex delay costs and impatient customers that have exponential abandonment times. We apply the Whittle index approach to find a reasonable heuristic solution for this tricky problem. By assuming exponential abandonment times, we are able to make a very straightforward use of the results of the corresponding problem with patient customers presented in Queueing Systems, Vol. 110, Article no. 2, 2026. Our main theoretical achievements are proving that the closed version of the corresponding discrete-time problem is indexable and deriving an explicit expression for the Whittle index. The discrete-time results are utilized to develop the Whittle index policy for the original continuous-time scheduling problem.

cs.PF↗