ESP: A System Utilization Benchmark

Wong, Alvin; Oliker, Leonid; Kramer, Williams T. C.; Kaltz, Teresa L.; Bailey, David H.

doi:10.1109/sc.2000.10056

Cited by 24 publications

(31 citation statements)

References 5 publications

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“…The ESP Benchmark [19] is an approach for evaluating HPC performance. It evaluates system utilization and effectiveness by executing a medium length workload of 82 jobs, that varies in type of applications and requested resources, from a small job to jobs that take all system resources.…”

Section: Related Workmentioning

confidence: 99%

DJSB: Dynamic Job Scheduling Benchmark

López

Jokanović

D'Amico

et al. 2018

Job Scheduling Strategies for Parallel Processing

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Abstract. High-performance computing (HPC) systems are very big and powerful systems, with the main goal of achieving maximum performance of parallel jobs. Many dynamic factors influence the performance which makes this goal a non-trivial task. According to our knowledge, there is no standard tool to automatize performance evaluation through comparing different configurations and helping system administrators to select the best scheduling policy or the best job scheduler. This paper presents the Dynamic Job Scheduler Benchmark (DJSB). It is a configurable tool that compares performance metrics for different scenarios. DJSB receives a workload description and some general arguments such as job submission commands and generates performance metrics and performance plots. To test and present DJSB, we have compared three different scenarios with dynamic resource management strategies using DJSB experiment-driven tool. Results show that just changing some DJSB arguments we can set up and execute quite different experiments, making easy the comparison. In this particular case, a cooperative-dynamic resource management is evaluated compared with other resource management approaches.

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Section: Related Workmentioning

confidence: 99%

DJSB: Dynamic Job Scheduling Benchmark

López

Jokanović

D'Amico

et al. 2018

Job Scheduling Strategies for Parallel Processing

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“…We are not aware of any benchmark with malleable or evolving jobs. Therefore, we modified the well-known ESP benchmark [30] to contain various percentages of rigid, malleable, and evolving jobs. The original ESP benchmark is composed of 230 jobs with 14 different job types running the same synthetic application.…”

Section: A Experimentation Setupmentioning

confidence: 99%

A Batch System with Efficient Adaptive Scheduling for Malleable and Evolving Applications

Prabhakaran

Neumann

Rinke

et al. 2015

2015 IEEE International Parallel and Distributed Processing Symposium

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Abstract-The throughput of supercomputers depends not only on efficient job scheduling but also on the type of jobs that form the workload. Malleable jobs are most favorable for a cluster as they can dynamically adapt to a changing allocation of resources. The batch system can expand or shrink a running malleable job to improve system utilization, throughput, and response times. In the past, however, the rigid nature of commonly used programming models like MPI made writing malleable applications a daunting task, which is why it remained largely unrealized. This is now changing. To improve fault tolerance, load imbalance, and energy efficiency in emerging exascale systems, more adaptive programming paradigms such as Charm++ enter the scene. Although they offer better support for malleability, current batch systems still lack management facilities for malleable jobs and are therefore incapable of leveraging their potential. In this paper, we present an extension of the Torque/Maui batch system for malleability. We propose a novel malleable job scheduling strategy and show the first batch system capable of efficiently managing rigid, malleable, and evolving jobs together. We demonstrate that our strategy achieves consistently superior performance in comparison to every other state-of-the-art malleable job scheduling strategy under varying dynamics of the workload.

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“…The light-ESP benchmark [32], described in section 3, is used in these experiments. This synthetic workload of 230 jobs with different sizes and target run times is run on different emulated clusters from 1,024 nodes to 16,384 nodes.…”

Section: Scalability In Terms Of Number Of Resourcesmentioning

confidence: 99%

Evaluating Scalability and Efficiency of the Resource and Job Management System on Large HPC Clusters

Georgiou¹,

Hautreux

2013

Job Scheduling Strategies for Parallel Processing

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Abstract. The Resource and Job Management System (RJMS) is the middleware in charge of delivering computing power to applications in HPC systems. The increasing number of computational resources in modern supercomputers brings new levels of parallelism and complexity. To maximize the global throughput while ensuring good efficiency of applications, RJMS must deal with issues like manageability, scalability and network topology awareness. This paper is focused on the evaluation of the so-called RJMS SLURM regarding these issues. It presents studies performed in order to evaluate, adapt and prepare the configuration of the RJMS to efficiently manage two Bull petaflop supercomputers installed at CEA, Tera-100 and Curie. The studies evaluate the capability of SLURM to manage large numbers of compute resources and jobs as well as to provide an optimal placement of jobs on clusters using a tree interconnect topology. Experiments presented in this paper are conducted using both real-scale and emulated supercomputers using synthetic workloads. The synthetic workloads are derived from the ESP benchmark and adapted to the evaluation of the RJMS internals. Emulations of larger supercomputers are performed to assess the scalability and the direct eligibility of SLURM to manage larger systems.

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ESP: A System Utilization Benchmark

Cited by 24 publications

References 5 publications

DJSB: Dynamic Job Scheduling Benchmark

DJSB: Dynamic Job Scheduling Benchmark

A Batch System with Efficient Adaptive Scheduling for Malleable and Evolving Applications

Evaluating Scalability and Efficiency of the Resource and Job Management System on Large HPC Clusters

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