A hierarchical approach to workload characterization for parallel systems

Calzarossa, Maria Carla; Merlo, Alessandro P.; Tessera, Daniele; Haring, Günter; Kotsis, Gabriele

doi:10.1007/bfb0046616

Cited by 18 publications

(9 citation statements)

References 7 publications

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“…For example, each job is composed of procedures that are built of instructions, and these procedures and instructions interact with the computer at different levels. One option is to model these levels explicitly, creating a hierarchy of interlocked models for the different levels [103,100,557] (an example of generative modeling). This has the obvious advantage of conveying a full and detailed picture of the structure of the workload.…”

Section: Degree Of Detailmentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

191

131

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Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.

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Section: Degree Of Detailmentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

191

131

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“…In other words, high-level workloads will be translated into a stream of low-level workloads. For that, Calzarossa et al define a layered framework for the modeling of user behavior models [86]. Markov chain models can be used to identify dependencies between user requests [87], [88], [76], [89].…”

Section: B Graph-basedmentioning

confidence: 99%

Capacity metric for chip heterogeneous multiprocessors

Otoom

Paul

2011

Proceedings of the Seventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis

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The primary contribution of this thesis is the development of a new performance metric, Capacity, which evaluates the performance of Chip Heterogeneous Multiprocessors (CHMs) that process multiple heterogeneous channels. Performance metrics are required in order to evaluate any system, including computer systems. A lack of appropriate metrics can lead to ambiguous or incorrect results, something discovered while developing the secondary contribution of this thesis, that of workload modes for CHMsor Workload Specific Processors (WSPs).For many decades, computer architects and designers have focused on techniques that reduce latency and increase throughput. The change in modern computer systems built around CHMs that process multi-channel communications in the service of single users calls this focus into question. Modern computer systems are expected to integrate tens to hundreds of processor cores onto single chips, often used in the service of single users, potentially as a way to access the Internet. Here, the design goal is to integrate as much functionality as possible during a given time window. Without the ability to correctly identify optimal designs, not only will the best performing designs not be found, but resources will be wasted and there will be a lack of insight to what leads to better performing designs. To address performance evaluation challenges of the next generation of computer systems, such as multicore computers inside of cell phones, we found that a structurally different metric is needed and proceeded to develop such a metric.In contrast to single-valued metrics, Capacity is a surface with dimensionality related to the number of input streams, or channels, processed by the CHM. We develop some fundamental Capacity curves in two dimensions and show how Capacity shapes reveal interaction of not only programs and data, but the interaction of multiple data streams as they compete for access to resources on a CHM as well. For the analysis of Capacity surface shapes, we propose the development of a demand characterization method in which its output is in the form of a surface. By overlaying demand surfaces over Capacity iii surfaces, we are able to identify when a system meets its demands and by how much.Using the Capacity metric, computer performance optimization is evaluated against workloads in the service of individual users instead of individual applications, aggregate applications, or parallel applications. Because throughput was originally derived by drawing analogies between processor design and pipelines in the automobile industry, we introduce our Capacity metric for CHMs by drawing an analogy to automobile production, signifying that Capacity is the successor to throughput. By developing our Capacity metric, we illustrate how and why different processor organizations cannot be understood as being better performers without both magnitude and shape analysis in contrast to other metrics, such as throughput, that consider only magnitude.In this work, we make the following major ...

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“…Feitelson has presented several works concerning this topic, among others, he has published papers on log analysis for specific centers [10], general job and workload modeling [8][11] [9], and, together with Tsafrir, papers on detecting workload anomalies and flurries [25]. Calzarossa has also contributed with several workload modellization surveys [1] [2]. Workload models for moldable jobs have been described in works of authors like Cirne et al in [5] [6], by Sevcik in [18] or by Downey in [7].…”

Section: Related Workmentioning

confidence: 99%

The Resource Usage Aware Backfilling

Guim

Rodero

Corbalan

2009

Job Scheduling Strategies for Parallel Processing

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Abstract. Job scheduling policies for HPC centers have been extensively studied in the last few years, especially backfilling based policies. Almost all of these studies have been done using simulation tools. All the existent simulators use the runtime (either estimated or real) provided in the workload as a basis of their simulations. In our previous work we analyzed the impact on system performance of considering the resource sharing (memory bandwidth) of running jobs including a new resource model in the Alvio simulator. Based on this studies we proposed the LessConsume and LessConsume Threshold resource selection policies. Both are oriented to reduce the saturation of the shared resources thus increasing the performance of the system. The results showed how both resource allocation policies shown how the performance of the system can be improved by considering where the jobs are finally allocated. Using the LessConsume Threshold Resource Selection Policy, we propose a new backfilling strategy : the Resource Usage Aware Backfilling job scheduling policy. This is a backfilling based scheduling policy where the algorithms which decide which job has to be executed and how jobs have to be backfilled are based on a different Threshold configurations. This backfilling variant that considers how the shared resources are used by the scheduled jobs. Rather than backfilling the first job that can moved to the run queue based on the job arrival time or job size, it looks ahead to the next queued jobs, and tries to allocate jobs that would experience lower penalized runtime caused by the resource sharing saturation. In the paper we demostrate how the exchange of scheduling information between the local resource manager and the scheduler can improve substantially the performance of the system when the resource sharing is considered. We show how it can achieve a close response time performance that the shorest job first Backfilling with First Fit (oriented to improve the start time for the allocated jobs) providing a qualitative improvement in the number of killed jobs and in the percentage of penalized runtime.

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A hierarchical approach to workload characterization for parallel systems

Cited by 18 publications

References 7 publications

Workload Modeling for Computer Systems Performance Evaluation

Workload Modeling for Computer Systems Performance Evaluation

Capacity metric for chip heterogeneous multiprocessors

The Resource Usage Aware Backfilling

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