LIMITLESS — LIght-weight MonItoring Tool for LargE Scale Systems

Cascajo, Alberto; Singh, David E.; Carretero, Jesús

doi:10.1016/j.micpro.2022.104586

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…A prominent example, currently deployed on production systems at LRZ and extended in the projects DEEP-SEA [37] and RE-GALE [38], is the Data Center Data Base (DCDB) [39], [40], which is capable of routinely tracking millions of sensors on large scale production systems, such as SuperMUC-NG, using technologies from the IoT space combined with a federation of time series databases built in top of Cassandra. Similarly, the ADMIRE project [41] is building an entirely new measurement infrastructure relying on the Prometheus time-series database (TSDB) connected to a node-level aggregating push gateway coupled with LIMITLESS [42] for node-level monitoring and high-speed spatial reduction based on a tree-based overlay network (TBON). Although such comprehensive monitoring capabilities are not yet commonly used in all parallel systems, previous experiments have demonstrated the usefulness of malleability at the application level.…”

Section: B Monitoring and Modelingmentioning

confidence: 99%

Malleability in Modern HPC Systems: Current Experiences, Challenges, and Future Opportunities

Tarraf,

Schreiber,

Cascajo

et al. 2024

IEEE Trans. Parallel Distrib. Syst.

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With the increase of complex scientific simulations driven by workflows and heterogeneous workload profiles, managing system resources effectively is essential for improving performance and system throughput, especially due to trends like heterogeneous HPC and deeply integrated systems with on-chip accelerators. For optimal resource utilization, dynamic resource allocation can improve productivity across all system and application levels, by adapting the applications' configurations to the system's resources. In this context, malleable jobs, which can change resources at runtime, can increase the system throughput and resource utilization while bringing various advantages for HPC users (e.g., shorter waiting time). Malleability has received much attention recently, even though it has been an active research area for almost two decades [1]. This paper presents the state-of-the-art of malleable implementations in HPC systems, targeting mainly malleability in compute and I/O resources. Based on our experiences, we state our current concerns and list future opportunities for research.

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Section: B Monitoring and Modelingmentioning

confidence: 99%

Malleability in Modern HPC Systems: Current Experiences, Challenges, and Future Opportunities

Tarraf,

Schreiber,

Cascajo

et al. 2024

IEEE Trans. Parallel Distrib. Syst.

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show abstract

“…In addition, this period can be modified online without the necessity of restarting the system or the monitor and it is possible to have one different for each node. The monitor overhead in the compute nodes is low (less than 1% on CPU consumption and a memory footprint of 3890 KB in resident memory (Cascajo et al, 2021(Cascajo et al, , 2022, which means that the monitoring has a minimal impact on the applications that are being executed in the same compute nodes.…”

Section: System Monitormentioning

confidence: 99%

“…The second alternative was implemented in Cascajo et al (2021) and uses the generated application models to predict the future performance of the applications in order support the decision-making process related to the application scheduling. This alternative does not produce good results if the accuracy of the predictor is poor.…”

Section: Building Synthetic Micro-benchmarks: Application Clonesmentioning

confidence: 99%

“…By comparing the exclusively-collected and the shared-collected metrics, the system is able to identify if there is performance degradation. Using this information, the scheduler can make decisions about the scheduling (Aupy et al, 2019; Carretero et al, 2020; Cascajo et al, 2019, 2021, 2022; Ekanadham et al, 1995; Sing David, 2019), for instance migrating one of them to avoid the interference, or evaluating if that interference is mitigated when the number of processes is increased or decreased for one of them.…”

Section: Building Synthetic Micro-benchmarks: Application Clonesmentioning

confidence: 99%

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Detecting interference between applications and improving the scheduling using malleable application clones

Cascajo,

Singh,

Carretero

2023

The International Journal of High Performance Computing Applica

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This paper presents a novel feature for improving the scheduling process based on the performance prediction and the detection of CPU and I/O interference between applications. This feature consists of using malleable synthetic benchmarks – called clones – that reproduce the behaviour of applications executed in a cluster. These proxies can be used with two objectives: to build large and representative datasets that can be used to train the machine learning algorithms for modelling the platform workload, and to evaluate in advance if two executing applications can potentially produce contention hazards related to the shared use of the system resources like CPU, cache memory or I/O bandwidth. The proposed framework generates application clones based on generic-purpose performance information collected from monitoring. Unlike other works based on the use of micro-architectures or metrics obtained from simulators, in the approach presented in this work, the application clones generate similar behaviour without extracting data from the binaries, avoiding the necessity of managing code or data from the applications. One advantage of this approach is that they can be shared securely because they have not been generated using any piece of the original code. In addition, the proposed clones are malleable, so they also able to model the application behaviour under a different number of processes. In this work, we show how the use of clones contributes to improving the application scheduling by reducing the number of evaluations that is necessary to perform, and to detect performance degradation (interference) without the necessity of involving the execution of the actual applications. We evaluate the proposed clone generation approach on two sets of benchmarks (CPU and I/O oriented) and several applications. We also compare the performance obtained during the execution of the proxies and the applications to show their similarity. Finally, we include an evaluation of the interference detection using this novel approach.

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Towards Smarter Schedulers: Molding Jobs into the Right Shape via Monitoring and Modeling

Besnard,

Tarraf,

Barthélemy

et al. 2023

Lecture Notes in Computer Science

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LIMITLESS — LIght-weight MonItoring Tool for LargE Scale Systems

Cited by 6 publications

References 18 publications

Malleability in Modern HPC Systems: Current Experiences, Challenges, and Future Opportunities

Malleability in Modern HPC Systems: Current Experiences, Challenges, and Future Opportunities

Detecting interference between applications and improving the scheduling using malleable application clones

Towards Smarter Schedulers: Molding Jobs into the Right Shape via Monitoring and Modeling

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