Com-CAS

Chatterjee, Bodhisatwa; Khan, Sharjeel; Pande, Santosh

doi:10.1145/3559009.3569645

Cited by 2 publications

(1 citation statement)

References 46 publications

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“…Like [54], LFOC+ targets system fairness. Chatterjee et al [56] use compiler-generated information to collect information about the memory footprint, cache sensitivity, reuse behavior, and phase timing to estimate the applications' memory requirements. With this information, they propose a proactive cache partitioning scheme that dynamically partitions the cache and schedules processes.…”

Section: Resource Management Approachesmentioning

confidence: 99%

Interference Analysis and Resource Management in Server Processors: from HPC to Cloud Computing

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Finalmente, quiero agradecer a las personas más importantes de mi vida, mi familia. A mis padres, gracias por inculcarme el valor del esfuerzo y enseñarme a sobreponerme a las adversidades. Pero sobretodo, por apoyarme en cada paso que doy y animarme en los momentos más difíciles. Vuestra sonrisa de orgullo es la mejor recompensa. A mis abuelos, por su amor infinito que siempre me da fuerzas. A mis hermanos mayores, con los que puedo contar siempre. A Julio y Miljana, por su apoyo y cariño, y al pequeño Julio que ha llenado la familia de felicidad. A Alfonso, por ser mi fiel compañero y estar siempre a mi lado. Y también a mi novio, Mario, por sacarme una sonrisa cuando más lo necesito. Gracias por motivarme a ser mejor cada día y perseguir mis sueños.

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Section: Resource Management Approachesmentioning

confidence: 99%

Interference Analysis and Resource Management in Server Processors: from HPC to Cloud Computing

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Beacons: An End-to-End Compiler Framework for Predicting and Utilizing Dynamic Loop Characteristics

Mururu,

Khan,

Chatterjee

et al. 2023

Proc. ACM Program. Lang.

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Efficient management of shared resources is a critical problem in high-performance computing (HPC) environments. Existing workload management systems often promote non-sharing of resources among different co-executing applications to achieve performance isolation. Such schemes lead to poor resource utilization and suboptimal process throughput, adversely affecting user productivity. Tackling this problem in a scalable fashion is extremely challenging, since it requires the workload scheduler to possess an in-depth knowledge about various application resource requirements and runtime phases at fine granularities within individual applications. In this work, we show that applications’ resource requirements and execution phase behaviour can be captured in a scalable and lightweight manner at runtime by estimating important program artifacts termed as “ dynamic loop characteristics ”. Specifically, we propose a solution to the problem of efficient workload scheduling by designing a compiler and runtime cooperative framework that leverages novel loop-based compiler analysis for resource allocation . We present Beacons Framework , an end-to-end compiler and scheduling framework, that estimates dynamic loop characteristics, encapsulates them in compiler-instrumented beacons in an application, and broadcasts them during application runtime, for proactive workload scheduling. We focus on estimating four important loop characteristics : loop trip-count , loop timing , loop memory footprint , and loop data-reuse behaviour , through a combination of compiler analysis and machine learning. The novelty of the Beacons Framework also lies in its ability to tackle irregular loops that exhibit complex control flow with indeterminate loop bounds involving structure fields, aliased variables and function calls , which are highly prevalent in modern workloads. At the backend, Beacons Framework entails a proactive workload scheduler that leverages the runtime information to orchestrate aggressive process co-locations, for maximizing resource concurrency, without causing cache thrashing . Our results show that Beacons Framework can predict different loop characteristics with an accuracy of 85% to 95% on average, and the proactive scheduler obtains an average throughput improvement of 1.9x (up to 3.2x ) over the state-of-the-art schedulers on an Amazon Graviton2 machine on consolidated workloads involving 1000-10000 co-executing processes, across 51 benchmarks.

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Com-CAS

Cited by 2 publications

References 46 publications

Interference Analysis and Resource Management in Server Processors: from HPC to Cloud Computing

Interference Analysis and Resource Management in Server Processors: from HPC to Cloud Computing

Beacons: An End-to-End Compiler Framework for Predicting and Utilizing Dynamic Loop Characteristics

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