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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
3.
How much higher can HTCondor fly? Fajardo, E M; Dost, J M; Holzman, B ...
Journal of physics. Conference series,
12/2015, Volume:
664, Issue:
6
Journal Article
Peer reviewed
Open access
The HTCondor high throughput computing system is heavily used in the high energy physics (HEP) community as the batch system for several Worldwide LHC Computing Grid (WLCG) resources. Moreover, it is ...the backbone of GlidelnWMS, the pilot system used by the computing organization of the Compact Muon Solenoid (CMS) experiment. To prepare for LHC Run 2, we probed the scalability limits of new versions and configurations of HTCondor with a goal of reaching 200,000 simultaneous running jobs in a single internationally distributed dynamic pool. In this paper, we first describe how we created an opportunistic distributed testbed capable of exercising runs with 200,000 simultaneous jobs without impacting production. This testbed methodology is appropriate not only for scale testing HTCondor, but potentially for many other services. In addition to the test conditions and the testbed topology, we include the suggested configuration options used to obtain the scaling results, and describe some of the changes to HTCondor inspired by our testing that enabled sustained operations at scales well beyond previous limits.
Patient participation is recognized as an important element of rehabilitation. However, few studies have used a qualitative lens to specifically examine factors influencing patient participation in ...stroke rehabilitation.
The purpose of this study was to investigate patient perspectives of barriers and facilitators to participating in hospital-based stroke rehabilitation.
Semi-structured interviews were conducted with 11 patients, with confirmed diagnoses of stroke, recruited from three separate rehabilitation settings. Analysis of the interviews was guided by a process of interpretive description to identify key barriers and facilitators to participation in stroke rehabilitation.
Four main themes and corresponding sub-themes were constructed concerning participation in rehabilitation: (i) Environmental Factors, (ii) Components of Therapy, (iii) Physical and Emotional Well-Being, and (iv) Personal Motivators. An exploratory model of personalized rehabilitation emerged, integrating the themes emerging from the data.
Personalized rehabilitation can be considered in comparison to person-centred care principles. The barriers and enablers experienced by patients in this study contribute to the existing knowledge of the patient experience of stroke rehabilitation and may be used to inform clinical practices and future research.
Implications for Rehabilitation
The surrounding environments can facilitate participation in rehabilitation using strategies to reduce noise and disruption and also by encouraging social interactions among patients.
Increasing the frequency and consistency of communication with patients about rehabilitation goals and progress could enhance participation.
Designing interventions to include activities that are meaningful and focused on the resumption of valued life roles is key to participation.
Therapy intensity, time spent sedentary, and the emotional impact of stroke are aspects of rehabilitation patient's feel are neglected.
Full text
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IJS, NUK, UL, UM, UPUK, VSZLJ
Background
The COVID-19 pandemic has disrupted everyday rehabilitation research. Many academic institutions have halted in-person human research including rehabilitation sciences. Researchers are ...faced with several barriers to continuing their research programs. The purpose of this perspective article is to report the results of an interdisciplinary workshop aimed at understanding the challenges and corresponding strategies for conducting rehabilitation research during the COVID-19 pandemic.
Methods
Twenty-five rehabilitation researchers (17 trainees and eight faculty) attended a 2-h facilitated online workshop in to discuss challenges and strategies they had experienced and employed to conduct rehabilitation research during the COVID-19 pandemic.
Results
Rehabilitation researchers reported challenges with (1)
pandemic protocol adjustments
, (2)
participant accessibility
, and (3)
knowledge dissemination
, along with corresponding strategies to these challenges. Researchers experienced disruptions in study outcomes and intervention protocols to adhere to public health guidelines and have suggested implementing novel virtual approaches and study toolkits to facilitate offsite assessment. Participant accessibility could be improved by engaging community stakeholders in protocol revisions to ensure equity, safety, and feasibility. Researchers also experienced barriers to virtual conferences and publication, suggested opportunities for smaller networking events, and revisiting timeframes for knowledge dissemination.
Conclusion
This perspective article served as a catalyst for discussion among rehabilitation researchers to identify novel and creative approaches that address the complexities of conducting rehabilitation research during the COVID-19 pandemic and beyond.
In the present run of the LHC, CMS data reconstruction and simulation algorithms benefit greatly from being executed as multiple threads running on several processor cores. The complexity of the Run ...2 events requires parallelization of the code to reduce the memory-per- core footprint constraining serial execution programs, thus optimizing the exploitation of present multi-core processor architectures. The allocation of computing resources for multi-core tasks, however, becomes a complex problem in itself. The CMS workload submission infrastructure employs multi-slot partitionable pilots, built on HTCondor and GlideinWMS native features, to enable scheduling of single and multi-core jobs simultaneously. This provides a solution for the scheduling problem in a uniform way across grid sites running a diversity of gateways to compute resources and batch system technologies. This paper presents this strategy and the tools on which it has been implemented. The experience of managing multi-core resources at the Tier-0 and Tier-1 sites during 2015, along with the deployment phase to Tier-2 sites during early 2016 is reported. The process of performance monitoring and optimization to achieve efficient and flexible use of the resources is also described.
The CMS experiment collects and analyzes large amounts of data coming from high energy particle collisions produced by the Large Hadron Collider (LHC) at CERN. This involves a huge amount of real and ...simulated data processing that needs to be handled in batch-oriented platforms. The CMS Global Pool of computing resources provide +100K dedicated CPU cores and another 50K to 100K CPU cores from opportunistic resources for these kind of tasks and even though production and event processing analysis workflows are already managed by existing tools, there is still a lack of support to submit final stage condor-like analysis jobs familiar to Tier-3 or local Computing Facilities users into these distributed resources in an integrated (with other CMS services) and friendly way. CMS Connect is a set of computing tools and services designed to augment existing services in the CMS Physics community focusing on these kind of condor analysis jobs. It is based on the CI-Connect platform developed by the Open Science Grid and uses the CMS GlideInWMS infrastructure to transparently plug CMS global grid resources into a virtual pool accessed via a single submission machine. This paper describes the specific developments and deployment of CMS Connect beyond the CI-Connect platform in order to integrate the service with CMS specific needs, including specific Site submission, accounting of jobs and automated reporting to standard CMS monitoring resources in an effortless way to their users.
The CMS experiment at the LHC relies on HTCondor and glideinWMS as its primary batch and pilot-based Grid provisioning systems, respectively. Given the scale of the global queue in CMS, the operators ...found it increasingly difficult to monitor the pool to find problems and fix them. The operators had to rely on several different web pages, with several different levels of information, and sift tirelessly through log files in order to monitor the pool completely. Therefore, coming up with a suitable monitoring system was one of the crucial items before the beginning of the LHC Run 2 in order to ensure early detection of issues and to give a good overview of the whole pool. Our new monitoring page utilizes the HTCondor ClassAd information to provide a complete picture of the whole submission infrastructure in CMS. The monitoring page includes useful information from HTCondor schedulers, central managers, the glideinWMS frontend, and factories. It also incorporates information about users and tasks making it easy for operators to provide support and debug issues.
The CMS Global Pool, based on HTCondor and glideinWMS, is the main computing resource provisioning system for all CMS workflows, including analysis, Monte Carlo production, and detector data ...reprocessing activities. The total resources at Tier-1 and Tier-2 grid sites pledged to CMS exceed 100,000 CPU cores, while another 50,000 to 100,000 CPU cores are available opportunistically, pushing the needs of the Global Pool to higher scales each year. These resources are becoming more diverse in their accessibility and configuration over time. Furthermore, the challenge of stably running at higher and higher scales while introducing new modes of operation such as multi-core pilots, as well as the chaotic nature of physics analysis workflows, places huge strains on the submission infrastructure. This paper details some of the most important challenges to scalability and stability that the CMS Global Pool has faced since the beginning of the LHC Run II and how they were overcome.
Tier-2 to Tier-2 data transfers have been identified as a necessary extension of the CMS computing model. The Debugging Data Transfers (DDT) Task Force in CMS was charged with commissioning Tier-2 to ...Tier-2 PhEDEx transfer links beginning in late 2009, originally to serve the needs of physics analysis groups for the transfer of their results between the storage elements of the Tier-2 sites associated with the groups. PhEDEx is the data transfer middleware of the CMS experiment. For analysis jobs using CRAB, the CMS Remote Analysis Builder, the challenges of remote stage out of job output at the end of the analysis jobs led to the introduction of a local fallback stage out, and will eventually require the asynchronous transfer of user data over essentially all of the Tier-2 to Tier-2 network using the same PhEDEx infrastructure. In addition, direct file sharing of physics and Monte Carlo simulated data between Tier-2 sites can relieve the operational load of the Tier-1 sites in the original CMS Computing Model, and already represents an important component of CMS PhEDEx data transfer volume. The experience, challenges and methods used to debug and commission the thousands of data transfers links between CMS Tier-2 sites world-wide are explained and summarized. The resulting operational experience with Tier-2 to Tier-2 transfers is also presented.
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