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Ookami is a computer technology testbed supported by the United States National Science Foundation. It provides researchers with access to the A64FX processor developed by Fujitsu in collaboration with RIK{\Xi}N for the Japanese path to exascale computing, as deployed in Fugaku, the fastest computer in the world. By focusing on crucial architectural details, the ARM-based, multi-core, 512-bit SIMD-vector processor with ultrahigh-bandwidth memory promises to retain familiar and successful programming models while achieving very high performance for a wide range of applications. We review relevant technology and system details, and the main body of the paper focuses on initial experiences with the hardware and software ecosystem for micro-benchmarks, mini-apps, and full applications, and starts to answer questions about where such technologies fit into the NSF ecosystem., Comment: 14 pages, 7 figures, PEARC '21: Practice and Experience in Advanced Research Computing, July 18--22, 2021, Boston, MA, USA

As part of the U.S. National Science Foundation (NSF) funded XD Metrics Service project, we are developing tools and techniques for the audit and analysis of High Performance Computing (HPC) and Cloud infrastructure. This includes a suite of tools for the analysis of HPC jobs, based on performance metrics collected from compute nodes. To date, we have developed two closely related utilities: XDMoD, which was designed to monitor usage and performance of NSF’s innovative HPC resources (known as XSEDE), and Open XDMoD, which was designed to monitor usage and performance in academic, governmental or commercial cyberinfrastructures. Considerable effort has been made to continually improve XDMoD, in order to capture the most important aspects of modern research computing.One area in which XDMoD is lacking is in tracking workflows, which are broadly designated as containing the elements of data transfer/input and one to many computational steps. As data sets have become larger, data movement has become more time and resource intensive, and hence more important to characterize. In addition, multiple step workflows, in which one input spawns a complex series of processes, are becoming more common. Although XDMoD currently captures some of the information required to properly track complex workflows, there are clearly some key data that are missing. In this paper, we discuss the existing state of workflow monitoring, and suggest strategies to improve on the information captured.

The XD Metrics on Demand (XDMoD) tool was designed to collect and present detailed utilization data from the XSEDE network of supercomputers. XDMoD displays a wealth of information on computational resources. Its metrics report accounting and performance figures for computational jobs, including resources consumed, wait times, and quality of service. XDMoD can aggregate this information over an ensemble of jobs, or report it for individual jobs. Its web-based interface supports charting, exploration, and reporting for any time range, across all computing resources. Over the last eight years, we have open-sourced XDMoD, generalizing and packaging it to produce Open XDMoD. Open XDMoD may be installed, configured, and run on any computing cluster, and we have extended and refined it to include support for storage, cloud resources, science gateways, and more. In this paper, we describe the federation of XDMoD instances. This new functionality makes it possible to associate and monitor networks of related resources, using the full power of XDMoD to aggregate and display data from disparate XDMoD installations. Charting and reports from XDMoD federations can encompass anything ranging from a single resource to the entire network. The federation capability is flexible; federated resources can be coupled loosely or tightly, and a variety of different information sharing and authentication paradigms are supported. We also highlight new functionality that incorporates storage and cloud facilities into XDMoD federations. Federation will enable XDMoD users to monitor and manage ever more diverse computing resources.

Application kernels are computationally lightweight benchmarks or applications run repeatedly on high performance computing HPC clusters in order to track the Quality of Service QoS provided to the users. They have been successful in detecting a variety of hardware and software issues, some severe, that have subsequently been corrected, resulting in improved system performance and throughput. In this work, the application kernels performance monitoring module of eXtreme Data Metrics on Demand XDMoD is described. Through the XDMoD framework, the application kernels have been run repetitively on the Texas Advanced Computing Center's Stampede and Lonestar4 clusters for a total of over 14,000 jobs. This provides a body of data on the HPC clusters operation that can be used to statistically analyze how the application performance, as measured by metrics such as execution time and communication bandwidth, is affected by the cluster's workload. We discuss metric distributions, carry out regression and correlation analyses, and use a PCA study to describe the variance and relate the variance to factors such as the spatial distribution of the application in the cluster. Ultimately, these types of analyses can be used to improve the application kernel mechanism, which in turn results in improved QoS of the HPC infrastructure that is delivered to the end users. Copyright © 2015 John Wiley & Sons, Ltd.

Blue Waters [4] is a petascale-level supercomputer whose mission is to greatly accelerate insight to the most challenging computational and data analysis problems. We performed a detailed workload analysis of Blue Waters [8] using Open XDMoD [10]. The analysis used approximately 35,000 node hours to process the roughly 95 TB of input data from over 4.5M jobs that ran on Blue Waters during the period that was studied (April 1, 2013 - September 30, 2016).This paper describes the work that was done to collate, process and analyze the data that was collected on Blue Waters, the design decisions that were made, tools that we created and the various software engineering problems that we encountered and solved. In particular, we describe the challenges to data processing unique to Blue Waters engendered by the extremely large jobs that it typically executed.

Machine learning techniques were applied to job accounting and performance data for application classification. Job data were accumulated using the XDMoD monitoring technology named SUPReMM, they consist of job accounting information, application information from Lariat/XALT, and job performance data from TACC_Stats. The results clearly demonstrate that community applications have characteristic signatures which can be exploited for job classification. We conclude that machine learning can assist in classifying jobs of unknown application, in characterizing the job mixture, and in harnessing the variation in node and time dependence for further analysis.

The Technology Audit Service has developed, XDMoD, a resource management tool. This paper utilizes XDMoD and the XDMoD data warehouse that it draws from to provide a broad overview of several aspects of XSEDE users and their usage. Some important trends include: 1) in spite of a large yearly turnover, there is a core of users persisting over many years, 2) user job submission has changed from primarily faculty members to students and postdocs, 3) increases in usage in Molecular Biosciences and Materials Research has outstripped that of other fields of science, 4) the distribution of user external funding is bimodal with one group having a large ratio of external funding to internal XSEDE funding (ie, CPU cycles) and a second group having a small ratio of external to internal (CPU cycle) funding, 5) user job efficiency is also bimodal with a group of presumably new users running mainly small inefficient jobs and another group of users running larger more efficient jobs, 6) finally, based on an analysis of citations of published papers, the scientific impact of XSEDE coupled with the service providers is demonstrated in the statistically significant advantage it provides to the research of its users.

This paper investigates the stability and large-displacement post-buckling behaviour of liquid-lined elastic rings. The fluid flow and the wall deformation are described by the free-surface Navier–Stokes equations and by geometrically nonlinear shell theory, respectively. The fluid–structure interaction problem is solved numerically by a finite element method. The compressive load on the ring is a combination of the external pressure and the effect of surface tension. Once this combined load exceeds a critical value, the subsequent non-axisymmetric collapse of the ring is controlled by the dynamics of the surface-tension-driven redistribution of fluid in the liquid lining. It is shown that, for sufficiently large surface tension, the ring can undergo a catastrophic collapse which leads to a complete occlusion of its lumen. A novel lubrication theory model, which ensures exact volume conservation for flows on strongly curved substrates, is developed and found to be capable of accurately describing the motion of the air–liquid interface and the fluid–structure interaction in the large-displacement regime, even in cases where the film thickness is large.The findings have important implications for the occurrence of airway closure in lung diseases (such as oedema) which cause an increase in the thickness of the airways' liquid lining. It is shown that under such conditions, airways can become occluded even if the volume of fluid in their liquid lining is much smaller than that required to occlude them in their axisymmetric state.

1. Marked hypertension was induced in adrenalectomized rats on a sodium-restricted intake by the administration of cortisone acetate; on a similar regimen, adrenalectomized rats receiving DCA failed to become hypertensive. 2. The incorporation of a liberal amount of sodium in the diet of adrenalectomized rats receiving cortisone acetate resulted in a lesser degree of hypertension; whereas on a similar regimen, DCA-injected animals became strikingly hypertensive, as has been previously reported. 3. The presence of hypertension in the cortisone acetate-treated rats was not associated with histological evidence of renal damage or serum electrolyte disturbance, such as was observed in hypertensive animals receiving DCA. 4. The hypertension in sodium-restricted normal rats receiving cortisone acetate was less striking than in similarly treated adrenalectomized animals. On a liberal sodium intake similar degrees of hypertension were noted in both intact and adrenalectomized groups. 5. To approximate the loss of weight which occurred in cortisone acetate-injected rats, it was necessary to curtail the food intake of partially fasted control groups to 50 to 75 per cent of the amount consumed by the animals receiving the steroid. 6. The serum cholesterol levels of the cortisone acetate-injected rats were consistently and strikingly elevated as contrasted with normal, partially fasted, or DCA-treated animals.