Ordonnancement de charges de travail intensives en entrées-sorties par lot utilisant la localité des données

Clusters make use of workload schedulers such as the Slurm Workload Manager to allocate computing jobs onto nodes. These schedulers usually aim at a good trade-off between increasing resource utilization and user satisfaction (decreasing job waiting time). However, these schedulers are typically unaware of jobs sharing large input files, which may happen in data intensive scenarios. The same input files may be loaded several times, leading to a waste of resources. We study how to design a data-aware job scheduler that is able to keep large input files on the computing nodes, without impacting other memory needs, and can use previously loaded files to limit data transfers in order to reduce the waiting times of jobs. We present three schedulers capable of distributing the load between the computing nodes as well as re-using an input file already loaded in the memory of some node as much as possible. We perform simulations using real cluster usage traces to compare them to classical job schedulers. The results show that keeping data in local memory between successive jobs and using data locality information to schedule jobs allows a reduction in job waiting time and a drastic decrease in the amount of data transfers.Les plateformes de calculs utilisent des planificateurs de charges de travail comme le Slurm Workload Manager pour allouer des travaux aux nœuds de calcul. Ces ordonnanceurs visent à équilibrer l'utilisation des ressources et la satisfaction des utilisateurs (réduire le temps d'attente). Cependant, ces ordonnanceurs ne sont pas conscients des travaux partageant des fichiers d'entrée volumineux. Dans certaines situations, la quantité de tels travaux peut être importante. Le même fichier d'entrée peut être chargé plusieurs fois, menant à une perte de ressources. Nous étudions la création d'un ordonnanceur de travaux utilisant la localité des données capable de garder de larges fichiers d'entrées sur les nœuds de calculs tout en préservant les autres besoins en mémoire, et également capable de réutiliser des fichiers déjà chargés afin de limiter les transferts de données et ainsi réduire le temps d'attente des travaux. Nous présentons trois ordonnanceurs capables de distribuer la charge entre les nœuds de calculs ainsi que de réutiliser le plus possible les fichiers d'entrées déjà chargés en mémoire de certains nœuds. Nous avons effectué des simulations en utilisant l'historique d'une vraie plateforme de calcul afin de comparer nos algorithmes à des ordonnanceurs classiques. Les résultats montrent que garder des données en mémoire entre des travaux successifs et utiliser la localité des données pour ordonner les travaux permettent une réduction du temps d'attente des travaux et une diminution drastique de la quantité de transferts de données

Data-Driven Locality-Aware Batch Scheduling

International audienceClusters employ workload schedulers such as theSlurm Workload Manager to allocate computing jobs onto nodes.These schedulers usually aim at a good trade-off between in-creasing resource utilization and user satisfaction (decreasing jobwaiting time). However, these schedulers are typically unaware ofjobs sharing large input files, which may happen in data intensivescenarios. The same input files may end up being loaded severaltimes, leading to a waste of resources.We study how to design a data-aware job scheduler that isable to keep large input files on the computing nodes, withoutimpacting other memory needs, and can benefit from previously-loaded files to decrease data transfers in order to reduce the waitingtimes of jobs.We present three schedulers capable of distributing the loadbetween the computing nodes as well as re-using input filesalready loaded in the memory of some node as much as possible.We perform simulations with single node jobs using tracesof real HPC-cluster usage, to compare them to classical jobschedulers. The results show that keeping data in local memorybetween successive jobs and using data-locality information toschedule jobs improves performance compared to a widely-usedscheduler (FCFS, with and without backfilling): a reduction injob waiting time (a 7.5% improvement in stretch), and a decreasein the amount of data transfers (7%)

Data-Driven Locality-Aware Batch Scheduling

International audienceClusters employ workload schedulers such as theSlurm Workload Manager to allocate computing jobs onto nodes.These schedulers usually aim at a good trade-off between in-creasing resource utilization and user satisfaction (decreasing jobwaiting time). However, these schedulers are typically unaware ofjobs sharing large input files, which may happen in data intensivescenarios. The same input files may end up being loaded severaltimes, leading to a waste of resources.We study how to design a data-aware job scheduler that isable to keep large input files on the computing nodes, withoutimpacting other memory needs, and can benefit from previously-loaded files to decrease data transfers in order to reduce the waitingtimes of jobs.We present three schedulers capable of distributing the loadbetween the computing nodes as well as re-using input filesalready loaded in the memory of some node as much as possible.We perform simulations with single node jobs using tracesof real HPC-cluster usage, to compare them to classical jobschedulers. The results show that keeping data in local memorybetween successive jobs and using data-locality information toschedule jobs improves performance compared to a widely-usedscheduler (FCFS, with and without backfilling): a reduction injob waiting time (a 7.5% improvement in stretch), and a decreasein the amount of data transfers (7%)

Evaluation of nutritional quality and benefits of Victoria pineapple from Reunion island against obesity-related disorders

International audienc

Residual Microscopic Peritoneal Metastases after Macroscopic Complete Cytoreductive Surgery for Advanced High-Grade Serous Ovarian Carcinoma: A Target for Folate Receptor Targeted Photodynamic Therapy?

International audienceDespite conventional treatment combining complete macroscopic cytoreductive surgery (CRS) and systemic chemotherapy, residual microscopic peritoneal metastases (mPM) may persist as the cause of peritoneal recurrence in 60% of patients. Therefore, there is a real need to specifically target these mPM to definitively eradicate any traces of the disease and improve patient survival. Therapeutic targeting method, such as photodynamic therapy, would be a promising method for such a purpose. Folate receptor alpha (FRα), as it is specifically overexpressed by cancer cells from various origins, including ovarian cancer cells, is a good target to address photosensitizing molecules. The aim of this study was to determine FRα expression by residual mPM after complete macroscopic CRS in patients with advanced high-grade serous ovarian cancer (HGSOC). A prospective study conducted between 1 June 2018 and 10 July 2019 in a single referent center accredited by the European Society of Gynecological Oncology for advanced EOC surgical management. Consecutive patients presenting with advanced HGSOC and eligible for complete macroscopic CRS were included. Up to 13 peritoneal biopsies were taken from macroscopically healthy peritoneum at the end of CRS and examined for the presence of mPM. In case of detection of mPM, a systematic search for RFα expression by immunohistochemistry was performed. Twenty-six patients were included and 26.9% presented mPM. In the subgroup of patients with mPM, FRα expression was positive on diagnostic biopsy before neoadjuvant chemotherapy for 67% of patients, on macroscopic peritoneal metastases for 86% of patients, and on mPM for 75% of patients. In the subgroup of patients with no mPM, FRα expression was found on diagnostic biopsy before neoadjuvant chemotherapy in 29% of patients and on macroscopic peritoneal metastases in 78% of patients. FRα is well expressed by patients with or without mPM after complete macroscopic CRS in patients with advanced HGSOC. In addition to conventional cytoreductive surgery, the use of a therapeutic targeting method, such as photodynamic therapy, by addressing photosensitizing molecules that specifically target FRα may be studied

REview of potentially inappropriate MEDIcation pr[e]scribing in Seniors (REMEDI[e]S): French implicit and explicit criteria

International audiencePurpose To establish a consensus on both explicit and implicit criteria in order to identify potentially inappropriate prescribing (PIP) in French older people aged 75 years and over or 65 years and over with multimorbidity. Methods Fifteen experts in geriatrics, general practice, pharmacy, and clinical pharmacology were involved in a two-round Delphi survey to assess preliminary explicit and implicit criteria based on an extensive literature review and up-to-date evidence data. Experts were asked to rate their level of agreement using a 5-level Likert scale for inclusion of criteria and also for rationale and therapeutic alternatives. A consensus was considered as reached if at least 75% of the experts rated criteria as "strongly agreed" or "agreed." Results The new tool included a seven-step algorithm (implicit criteria) encompassing the three main domains that define PIP (i.e. overprescribing, underprescribing, and misprescribing) and 104 explicit criteria. Explicit criteria were divided into 6 tables related to inappropriate drug duplications (n = 7 criteria), omissions of medications and/or medication associations (n = 16), medications with an unfavourable benefit/risk ratio and/or a questionable efficacy (n = 39), medications with an unsuitable dose (n = 4) or duration (n = 6), drug-disease (n = 13), and drug-drug interactions (n = 19). Conclusion The REMEDI[e]S tool (REview of potentially inappropriate MEDIcation pr[e]scribing in Seniors) is an original mixed tool, adapted to French medical practices, aimed at preventing PIP both at the individual level in clinical practice and the population level in large-scale studies. Therefore, its use could contribute to an improvement in healthcare professionals' prescribing practices and safer care in older adults