Application kernels: HPC resources performance monitoring and variance analysis

Simakov, Nikolay A.; White, John; DeLeon, Robert L.; Ghadersohi, Amin; Furlani, Thomas R.; Jones, Matthew D.; Gallo, Steven M.; Patra, Abani

doi:10.1002/cpe.3564

Cited by 9 publications

(2 citation statements)

References 23 publications

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“…Application Kernels enable quality-of-service monitoring for HPC resources. [8], [9] The application kernel remote runner (AKRR) periodically runs computationally lightweight benchmarks, thus establishing baselines for application performance. These benchmarks help center personnel pinpoint underlying problems when a resource performs poorly.…”

Section: A Application Kernels Modulementioning

confidence: 99%

Managing computational gateway resources with XDMoD

Sperhac

DeLeon

Furlani

et al. 2019

Future Generation Computer Systems

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Section: A Application Kernels Modulementioning

confidence: 99%

Managing computational gateway resources with XDMoD

Sperhac

DeLeon

Furlani

et al. 2019

Future Generation Computer Systems

Self Cite

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“…However, since late 2016, the I-TASSER (ZhangLab) Gateway, which conducts protein folding predictions, is now the leading provider of Science Gateway jobs. Third in this figure is the University Buffalo's TAS gateway which runs application kernels to provide provide quality of service metrics for XSEDE [19]. Since the application kernels are designed to be computationally lightweight, it is reassuring to note that the TAS Gateway does not appear in Figure 62 which shows the top ten Gateways in terms of XD SUs consumed.…”

Section: Science Gatewaysmentioning

confidence: 99%

A Workload Analysis of NSF's Innovative HPC Resources Using XDMoD

Simakov¹,

White²,

DeLeon³

et al. 2018

Preprint

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Workload characterization is an integral part of performance analysis of high performance computing (HPC) systems. An understanding of workload properties sheds light on resource utilization and can be used to inform performance optimization both at the software and system configuration levels. It can provide information on how computational science usage modalities are changing that could potentially aid holistic capacity planning for the wider HPC ecosystem. Here, we report on the results of a detailed workload analysis of the portfolio of supercomputers comprising the NSF Innovative HPC program in order to characterize its past and current workload and look for trends to understand the nature of how the broad portfolio of computational science research is being supported and how it is changing over time. The workload analysis also sought to illustrate a wide variety of usage patterns and performance requirements for jobs running on these systems. File system performance, memory utilization and the types of parallelism employed by users (MPI, threads, etc) were also studied for all systems for which job level performance data was available.Unless stated otherwise, the analysis covered the date range 2011-07-01 to 2017-09-30, the start date of which coincides with that of the XSEDE program. In addition to job accounting data for almost all of the production systems during this time period, job level performance data was available for TACC RANGER, TACC LONESTAR4, TACC STAMPEDE, TACC STAMPEDE2, CCT LSU SUPERMIC, NICS DARTER, SDSC COMET, and SDSC GORDON, as described in Appendix A.2. Highlights of the analysis are as follows. Trends in Utilization• Overall, the allocation utilization of NSF Innovative HPC resources is high, with only 10% of allocations going unused by researchers.• Since 2011, utilization over most NSF directorates in terms of XD SUs has increased by two orders of magnitude.• The Mathematical and Physical Sciences (MPS) and Biological Sciences (BIO) Directorates account for about 70% of XD SUs consumed and this percentage has remained constant over time.• Molecular Biosciences, Physics, and Materials Research account for half of all XD SUs consumed by Parent Science.• Behavioral and Neural Sciences and Integrative Biology and Neuroscience account for over 50% of all jobs run, with the bulk of those on Open Science Grid.• 27% of XSEDE projects are responsible for 73% of the utilization. Job Characteristics• Average job size decreased from a high of about 9000 cores in 2011 to about 1000 cores in 2017 (prior to the introduction of TACC STAMPEDE2). Several factors contributed to this decrease including, the retirement of NICS KRAKEN, the availability of Blue Waters for capability class computing, improved core performance, and resource policies limiting the maximum core count.

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First Impressions of the Sapphire Rapids Processor with HBM for Scientific Workloads

Siegmann,

Harrison,

Carlson

et al. 2024

SN COMPUT. SCI.

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The landscape of high performance computing (HPC) has witnessed exponential growth in processor diversity, architectural complexity, and performance scalability. With an ever-increasing demand for faster and more efficient computing solutions to address an array of scientific, engineering, and societal challenges, the selection of processors for specific applications becomes paramount. Achieving optimal performance requires a deep understanding of how diverse processors interact with diverse workloads, making benchmarking a fundamental practice in the field of HPC. Here, we present preliminary results observed over such benchmarks and applications and a comparison of Intel Sapphire Rapids and Skylake-X, AMD Milan, and Fujitsu A64FX processors in terms of runtime performance, memory bandwidth utilization, and energy consumption. The examples focus specifically on the Sapphire Rapids processor with and without high-bandwidth memory (HBM). An additional case study reports the performance gains from using Intel’s Advanced Matrix Extensions (AMX) instructions, and how they along with HBM can be leveraged to accelerate AI workloads. These initial results aim to give a rough comparison of the processors rather than a detailed analysis and should prove timely and relevant for researchers who may be interested in using Sapphire Rapids for their scientific workloads.

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Application kernels: HPC resources performance monitoring and variance analysis

Cited by 9 publications

References 23 publications

Managing computational gateway resources with XDMoD

Managing computational gateway resources with XDMoD

A Workload Analysis of NSF's Innovative HPC Resources Using XDMoD

First Impressions of the Sapphire Rapids Processor with HBM for Scientific Workloads

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