Cyberinfrastructure Usage Modalities on the TeraGrid

Hart, David; Jordan, Chris; Majumdar, Amitava; Navarro, John-Paul; Smith, Warren; Towns, John; Welch, Von; Wilkins‐Diehr, Nancy

doi:10.1109/ipdps.2011.239

Cited by 13 publications

(13 citation statements)

References 10 publications

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“…Further aspects of supercomputing usage have been examined, which may contribute to understanding of impact. How users intend to use the resources (Katz et al, ) or what software elements of the environment are used in practice (Hadri, Fahey, Robinson, & Renaud, ) may serve as metrics for measuring how well the system provider's plans and expectations for proposed use cases match the eventual users' reality.…”

Section: Proxy Metrics: Downloads and Usage Datamentioning

confidence: 99%

Assessing and tracing the outcomes and impact of research infrastructures

Mayernik

Hart

Maull

et al. 2016

Asso for Info Science & Tech

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Recent policy shifts on the part of funding agencies and journal publishers are causing changes in the acknowledgment and citation behaviors of scholars. A growing emphasis on open science and reproducibility is changing how authors cite and acknowledge "research infrastructures"-entities that are used as inputs to or as underlying foundations for scholarly research, including data sets, software packages, computational models, observational platforms, and computing facilities. At the same time, stakeholder interest in quantitative understanding of impact is spurring increased collection and analysis of metrics related to use of research infrastructures. This article reviews work spanning several decades on tracing and assessing the outcomes and impacts from these kinds of research infrastructures. We discuss how research infrastructures are identified and referenced by scholars in the research literature and how those references are being collected and analyzed for the purposes of evaluating impact. Synthesizing common features of a wide range of studies, we identify notable challenges that impede the analysis of impact metrics for research infrastructures and outline key open research questions that can guide future research and applications related to such metrics.

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Section: Proxy Metrics: Downloads and Usage Datamentioning

confidence: 99%

Assessing and tracing the outcomes and impact of research infrastructures

Mayernik

Hart

Maull

et al. 2016

Asso for Info Science & Tech

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“…As demonstrated by D. Katz et al [1], the usage modes of applications leveraging the TeraGrid (in its final year, before transitioning to XSEDE) are narrow, and in many cases limited to single localized resources.…”

Section: Introductionmentioning

confidence: 99%

“…This is evidenced by the fact that supercomputing centers have their user and runtime environments tuned to support mostly single-job oriented workloads, and thus still present a batch-queue, job-centric and static perspective of resources. The requirements and world view of applications however, have changed significantly [2][3][4], e.g., applications comprising multiple simulations such as those composed of a set of ensemble-members [1] and variants thereof such as multiple replica-based [5], now account for a non-negligible fraction of demand.…”

Section: Introductionmentioning

confidence: 99%

SAGA: A standardized access layer to heterogeneous Distributed Computing Infrastructure

2015

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Distributed Computing Infrastructure is characterized by interfaces that are heterogeneous-syntactically and semantically. SAGA represents the most comprehensive community effort to date to address the heterogeneity by defining a simple, uniform access layer. In this paper, we describe the basic concepts underpinning its design and development. We also discuss RADICAL-SAGA which is the most widely used implementation of SAGA.

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“…Moreover, the usage of many HPC systems is dominated by a few very large allocations/users. However, the science objectives of many researchers do not necessarily require capability computing, and TeraGrid has recently begun trying to categorize and quantify a much wider range of usage modalities [1]. Ensemble simulations of molecular dynamics trajectories and other parameter sweeps, long-running jobs for optimization problems, science gateways for phylogenetics and nanomaterials needing fast turnaround for users Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Trestles

Moore

Hart

Pfeiffer

et al. 2011

Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery

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Trestles is a new 100TF HPC resource at SDSC designed to enhance scientific productivity for modest-scale and gateway users within the TeraGrid. This paper discusses the Trestles hardware and user environment, as well as the rationale for targeting this user base and the planned operational policies and procedures to optimize scientific productivity, including a focus on turnaround time in addition to the traditional system utilization. A surprisingly large fraction of TeraGrid users run modest-scale jobs (e.g. <1K cores), and an increasing fraction of TeraGrid users access HPC resources via gateways; while these users represent a large percentage of the user base, they consume a smaller fraction of the TeraGrid resources. Thus, while Trestles is not the largest HPC resource in TeraGrid, it will be able to support this large class of TeraGrid users in an environment designed to enhance their productivity. This targeted usage model also frees up other TeraGrid systems for users/jobs that require large-scale, SMP or other specific resource features. One of the key differentiators for Trestles is that it will be allocated and scheduled to optimize queue wait times and expansion factors, as well as the traditional system utilization metric. In addition, the node design, with 32 cores and 64GB DRAM, will accommodate many jobs without inter-node communications, while the 120GB local flash memory will speed up many applications. A robust set of application software, including Gaussian, BLAST, Abaqus, GAMESS, Amber and NAMD, is installed on the system. Standard job limits are 32 nodes (1K cores) and 48 hours runtime, but exceptions can be made, particularly for long jobs up to 2 weeks. Standing system reservations ensure that some nodes are always set aside for shorter, smaller jobs, and user-settable reservations are available to ensure users predictable access to the system. Nodes can be accessed in exclusive or shared mode. Finally, Trestles is the only TeraGrid resource with automatic ondemand access; a limited number of nodes is configured for jobs to "run at risk" (with a discount in the usage rate charged) and be subject to being pre-emptively killed by on-demand jobs (which carry a premium in the usage rate). The allocation, scheduling and software environments will be adjusted and tuned over time as usage patterns emerge and users provide feedback to further enhance their productivity.

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Cyberinfrastructure Usage Modalities on the TeraGrid

Cited by 13 publications

References 10 publications

Assessing and tracing the outcomes and impact of research infrastructures

Assessing and tracing the outcomes and impact of research infrastructures

SAGA: A standardized access layer to heterogeneous Distributed Computing Infrastructure

Trestles

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