Large-scale experiment for topology-aware resource management

International audienceA Resource and Job Management System (RJMS) is a crucial system software part of the HPC stack. It is responsible for efficiently delivering computing power to applications in supercomputing environments and its main intelligence relies on resource selection techniques to find the most adapted resources to schedule the users' jobs. In [8], we introduced a new topology-aware resource selection algorithm to determine the best choice among the available nodes of the platform based on their position in the network and on application behaviour (expressed as a communication matrix). We did integrate this algorithm as a plugin in Slurm and validated it with several optimization schemes by making comparisons with the default Slurm algorithm. This paper presents further experiments with regard to this selection process

Topology-Aware Job Mapping

International audienceA Resource and Job Management System (RJMS) is a crucial system software part of the HPC stack. It is responsible for eciently delivering computing power to applications in supercomputing environments. Its main intelligence relies on resource selection techniques to find the most adapted resources to schedule the users' jobs. This paper introduces a new method that takes into account the topology of the machine and the application characteristics to determine the best choice among the available nodes of the platform, based upon the network topology and taking into account the applications communication pattern. To validate our approach, we integrate this algorithm as a plugin for Slurm, a well-known and widespread RJMS. We assess our plugin with di↵erent optimization schemes by comparing with the default topology-aware Slurm algorithm, using both emulation and simulation of a large-scale platform and by carrying out experiments in a real cluster. We show that transparently taking into account a job communication pattern and the topology allows for relevant performance gains

Topology-aware job mapping

International audienceA Resource and Job Management System (RJMS) is a crucial system software part of the HPC stack. It is responsible for eciently delivering computing power to applications in supercomputing environments. Its main intelligence relies on resource selection techniques to find the most adapted resources to schedule the users' jobs. This paper introduces a new method that takes into account the topology of the machine and the application characteristics to determine the best choice among the available nodes of the platform, based upon the network topology and taking into account the applications communication pattern. To validate our approach, we integrate this algorithm as a plugin for Slurm, a well-known and widespread RJMS. We assess our plugin with di↵erent optimization schemes by comparing with the default topology-aware Slurm algorithm, using both emulation and simulation of a large-scale platform and by carrying out experiments in a real cluster. We show that transparently taking into account a job communication pattern and the topology allows for relevant performance gains

Extracorporeal membrane oxygenation network organisation and clinical outcomes during the COVID-19 pandemic in Greater Paris, France: a multicentre cohort study

Erratum inCorrection to Lancet Respir Med 2021; published online April 19. https://doi.org/10.1016/S2213-2600(21)00096-5.International audienceBackground: In the Île-de-France region (henceforth termed Greater Paris), extracorporeal membrane oxygenation (ECMO) for severe acute respiratory distress syndrome (ARDS) was considered early in the COVID-19 pandemic. We report ECMO network organisation and outcomes during the first wave of the pandemic.Methods: In this multicentre cohort study, we present an analysis of all adult patients with laboratory-confirmed SARS-CoV-2 infection and severe ARDS requiring ECMO who were admitted to 17 Greater Paris intensive care units between March 8 and June 3, 2020. Central regulation for ECMO indications and pooling of resources were organised for the Greater Paris intensive care units, with six mobile ECMO teams available for the region. Details of complications (including ECMO-related complications, renal replacement therapy, and pulmonary embolism), clinical outcomes, survival status at 90 days after ECMO initiation, and causes of death are reported. Multivariable analysis was used to identify pre-ECMO variables independently associated with 90-day survival after ECMO.Findings: The 302 patients included who underwent ECMO had a median age of 52 years (IQR 45-58) and Simplified Acute Physiology Score-II of 40 (31-56), and 235 (78%) of whom were men. 165 (55%) were transferred after cannulation by a mobile ECMO team. Before ECMO, 285 (94%) patients were prone positioned, median driving pressure was 18 cm H2O (14-21), and median ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen was 61 mm Hg (IQR 54-70). During ECMO, 115 (43%) of 270 patients had a major bleeding event, 27 of whom had intracranial haemorrhage; 130 (43%) of 301 patients received renal replacement therapy; and 53 (18%) of 294 had a pulmonary embolism. 138 (46%) patients were alive 90 days after ECMO. The most common causes of death were multiorgan failure (53 [18%] patients) and septic shock (47 [16%] patients). Shorter time between intubation and ECMO (odds ratio 0·91 [95% CI 0·84-0·99] per day decrease), younger age (2·89 [1·41-5·93] for ≤48 years and 2·01 [1·01-3·99] for 49-56 years vs ≥57 years), lower pre-ECMO renal component of the Sequential Organ Failure Assessment score (0·67, 0·55-0·83 per point increase), and treatment in centres managing at least 30 venovenous ECMO cases annually (2·98 [1·46-6·04]) were independently associated with improved 90-day survival. There was no significant difference in survival between patients who had mobile and on-site ECMO initiation.Interpretation: Beyond associations with similar factors to those reported on ECMO for non-COVID-19 ARDS, 90-day survival among ECMO-assisted patients with COVID-19 was strongly associated with a centre's experience in venovenous ECMO during the previous year. Early ECMO management in centres with a high venovenous ECMO case volume should be advocated, by applying centralisation and regulation of ECMO indications, which should also help to prevent a shortage of resources