An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System

Training machine learning (ML) algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, processor-centric systems (e.g., CPU, GPU) suffer from costly data movement between memory units and processing units, which consumes large amounts of energy and execution cycles. Memory-centric computing systems, i.e., with processing-in-memory (PIM) capabilities, can alleviate this data movement bottleneck. Our goal is to understand the potential of modern general-purpose PIM architectures to accelerate ML training. To do so, we (1) implement several representative classic ML algorithms (namely, linear regression, logistic regression, decision tree, K-Means clustering) on a real-world general-purpose PIM architecture, (2) rigorously evaluate and characterize them in terms of accuracy, performance and scaling, and (3) compare to their counterpart implementations on CPU and GPU. Our evaluation on a real memory-centric computing system with more than 2500 PIM cores shows that general-purpose PIM architectures can greatly accelerate memory-bound ML workloads, when the necessary operations and datatypes are natively supported by PIM hardware. For example, our PIM implementation of decision tree is $27\times$ faster than a state-of-the-art CPU version on an 8-core Intel Xeon, and $1.34\times$ faster than a state-of-the-art GPU version on an NVIDIA A100. Our K-Means clustering on PIM is $2.8\times$ and $3.2\times$ than state-of-the-art CPU and GPU versions, respectively. To our knowledge, our work is the first one to evaluate ML training on a real-world PIM architecture. We conclude with key observations, takeaways, and recommendations that can inspire users of ML workloads, programmers of PIM architectures, and hardware designers & architects of future memory-centric computing systems

Delayed awakening after cardiac arrest: prevalence and risk factors in the Parisian registry

PURPOSE: Although prolonged unconsciousness after cardiac arrest (CA) is a sign of poor neurological outcome, limited evidence shows that a late recovery may occur in a minority of patients. We investigated the prevalence and the predictive factors of delayed awakening in comatose CA survivors treated with targeted temperature management (TTM). METHODS: Retrospective analysis of the Parisian Region Out-of-Hospital CA Registry (2008-2013). In adult comatose CA survivors treated with TTM, sedated with midazolam and fentanyl, time to awakening was measured starting from discontinuation of sedation at the end of rewarming. Awakening was defined as delayed when it occurred after more than 48 h. RESULTS: A total of 326 patients (71 % male, mean age 59 ± 16 years) were included, among whom 194 awoke. Delayed awakening occurred in 56/194 (29 %) patients, at a median time of 93 h (IQR 70-117) from discontinuation of sedation. In 5/56 (9 %) late awakeners, pupillary reflex and motor response were both absent 48 h after sedation discontinuation. In multivariate analysis, age over 59 years (OR 2.1, 95 % CI 1.0-4.3), post-resuscitation shock (OR 2.6 [1.3-5.2]), and renal insufficiency at admission (OR 3.1 [1.4-6.8]) were associated with significantly higher rates of delayed awakening. CONCLUSIONS: Delayed awakening is common among patients recovering from coma after CA. Renal insufficiency, older age, and post-resuscitation shock were independent predictors of delayed awakening. Presence of unfavorable neurological signs at 48 h after rewarming from TTM and discontinuation of sedation did not rule out recovery of consciousness in late awakeners

Neurological failure in ICU patients with hematological malignancies : a prospective cohort study

Background : Epidemiological studies of neurological complications in patients with hematological malignancies are scant. The objective of the study was to identify determinants of survival in patients with hematological malignancy and neurological failure. Methods : Post hoc analysis of a prospective study of adults with hematological malignancies admitted for any reason to one of 17 university or university-affiliated participating ICUs in France and Belgium (2010-2012). The primary outcome was vital status at hospital discharge. Results : Of the 1011 patients enrolled initially, 226 (22.4%) had neurological failure. Presenting manifestations were dominated by drowsiness or stupor (65%), coma (32%), weakness (26%), and seizures (19%). Neuroimaging, lumbar puncture, and electroencephalography were performed in 113 (50%), 73 (32%), and 63 (28%) patients, respectively. A neurosurgical biopsy was done in 1 patient. Hospital mortality was 50%. By multivariate analysis, factors independently associated with higher hospital mortality were poor performance status ( odds ratio [OR], 3.99; 95% CI, 1.82-9.39; P = 0.0009), non-Hodgkin's lymphoma ( OR, 2.60; 95% CI, 1.35-5.15; P = 0.005), shock ( OR, 1.95; 95% CI, 1.04-3.72; P = 0.04), and respiratory failure ( OR, 2.18; 95% CI, 1.140-4.25; P = 0.02); and factors independently associated with lower hospital mortality were GCS score on day 1 ( OR, 0.88/point; 95% CI, 0.81-0.95; P = 0.0009) and autologous stem cell transplantation ( OR, 0.25; 95% CI, 0.07-0.75; P = 0.02). Conclusions : In ICU patients with hematological malignancies, neurological failure is common and often fatal. Independent predictors of higher hospital mortality were type of underlying hematological malignancy, poor performance status, hemodynamic and respiratory failures, and severity of consciousness impairment. Knowledge of these risk factors might help to optimize management strategies

Epidemiology and outcome predictors in 450 patients with hanging-induced cardiac arrest: a retrospective study

BackgroundCardiac arrest is the most life-threatening complication of attempted suicide by hanging. However, data are scarce on its characteristics and outcome predictors.MethodsThis retrospective observational multicentre study in 31 hospitals included consecutive adults admitted after cardiac arrest induced by suicidal hanging. Factors associated with in-hospital mortality were identified by multivariate logistic regression with multiple imputations for missing data and adjusted to the temporal trends over the study period.ResultsOf 450 patients (350 men, median age, 43 [34–52] years), 305 (68%) had a psychiatric history, and 31 (6.9%) attempted hanging while hospitalized. The median time from unhanging to cardiopulmonary resuscitation was 0 [0–5] min, and the median time to return of spontaneous circulation (ROSC) was 20 [10–30] min. Seventy-nine (18%) patients survived to hospital discharge. Three variables were independently associated with higher in-hospital mortality: time from collapse or unhanging to ROSC>20 min (odds ratio [OR], 4.71; 95% confidence intervals [95%CIs], 2.02–10.96; p = 0.0004); glycaemia >1.4 g/L at admission (OR, 6.38; 95%CI, 2.60–15.66; p < 0.0001); and lactate >3.5 mmol/L at admission (OR, 6.08; 95%CI, 1.71–21.06; p = 0.005). A Glasgow Coma Scale (GCS) score of >5 at admission was associated with lower in-hospital mortality (OR, 0.009; 95%CI, 0.02–0.37; p = 0.0009).ConclusionIn patients with hanging-induced cardiac arrest, time from collapse or unhanging to return of spontaneous circulation, glycaemia, arterial lactate, and coma depth at admission were independently associated with survival to hospital discharge. Knowledge of these risk factors may help guide treatment decisions in these patients at high risk of hospital mortality

Multi-Criteria Optimization and its Application to Multi-Processor Embedded Systems

Dans cette thèse nous développons de nouvelles techniques pour résoudre les problèmes d'optimisation multi-critère. Ces problèmes se posent naturellement dans de nombreux domaines d'application (sinon tous) où les choix sont évalués selon différents critères conflictuels (coûts et performance par exemple). Contrairement au cas de l'optimisation classique, de tels problèmes n'admettent pas en général un optimum unique mais un ensemble de solutions incomparables, aussi connu comme le front de Pareto, qui représente les meilleurs compromis possibles entre les objectifs conflictuels. La contribution majeure de la thèse est le développement d'algorithmes pour trouver ou approximer ces solutions de Pareto pour les problèmes combinatoires difficiles. Plusieurs problèmes de ce type se posent naturellement lors du processus de placement et d'ordonnancement d'une application logicielle sur une architecture multi-coeur comme P2012, qui est actuellement développé par STMicroelectronics.In this thesis we develop new techniques for solving multi-criteria optimization problems. Such problems arise naturally in many (if not all) application domains where choices are evaluated according to two or more conflicting criteria such as price vs. performance. Unlike ordinary optimization, such problems typically do not admit a unique optimum but a set of incomparable solutions, also known as the Pareto Front, which represent the best possible trade-offs between the conflicting goals. The major contribution of the thesis is the development of algorithms for finding or approximating these Pareto solutions for hard combinatorial problems that arise naturally in the process of mapping and scheduling application software on multi-core architectures such as P2012 which is currently being developed by ST Microelectronics

Optimisation multicritères et applications aux systèmes multi-processeurs embarqués

In this thesis we develop new techniques for solving multi-criteria optimization problems. Such problems arise naturally in many (if not all) application domains where choices are evaluated according to two or more conflicting criteria such as price vs. performance. Unlike ordinary optimization, such problems typically do not admit a unique optimum but a set of incomparable solutions, also known as the Pareto Front, which represent the best possible trade-offs between the conflicting goals. The major contribution of the thesis is the development of algorithms for finding or approximating these Pareto solutions for hard combinatorial problems that arise naturally in the process of mapping and scheduling application software on multi-core architectures such as P2012 which is currently being developed by ST Microelectronics.Dans cette thèse nous développons de nouvelles techniques pour résoudre les problèmes d'optimisation multi-critère. Ces problèmes se posent naturellement dans de nombreux domaines d'application (sinon tous) où les choix sont évalués selon différents critères conflictuels (coûts et performance par exemple). Contrairement au cas de l'optimisation classique, de tels problèmes n'admettent pas en général un optimum unique mais un ensemble de solutions incomparables, aussi connu comme le front de Pareto, qui représente les meilleurs compromis possibles entre les objectifs conflictuels. La contribution majeure de la thèse est le développement d'algorithmes pour trouver ou approximer ces solutions de Pareto pour les problèmes combinatoires difficiles. Plusieurs problèmes de ce type se posent naturellement lors du processus de placement et d'ordonnancement d'une application logicielle sur une architecture multi-coeur comme P2012, qui est actuellement développé par STMicroelectronics

Optimisation multicritères et applications aux systèmes multi-processeurs embarqués

Dans cette thèse nous développons de nouvelles techniques pour résoudre les problèmes d'optimisation multi-critère. Ces problèmes se posent naturellement dans de nombreux domaines d'application (sinon tous) où les choix sont évalués selon différents critères conflictuels (coûts et performance par exemple). Contrairement au cas de l'optimisation classique, de tels problèmes n'admettent pas en général un optimum unique mais un ensemble de solutions incomparables, aussi connu comme le front de Pareto, qui représente les meilleurs compromis possibles entre les objectifs conflictuels. La contribution majeure de la thèse est le développement d'algorithmes pour trouver ou approximer ces solutions de Pareto pour les problèmes combinatoires difficiles. Plusieurs problèmes de ce type se posent naturellement lors du processus de placement et d'ordonnancement d'une application logicielle sur une architecture multi-coeur comme P2012, qui est actuellement développé par STMicroelectronics.In this thesis we develop new techniques for solving multi-criteria optimization problems. Such problems arise naturally in many (if not all) application domains where choices are evaluated according to two or more conflicting criteria such as price vs. performance. Unlike ordinary optimization, such problems typically do not admit a unique optimum but a set of incomparable solutions, also known as the Pareto Front, which represent the best possible trade-offs between the conflicting goals. The major contribution of the thesis is the development of algorithms for finding or approximating these Pareto solutions for hard combinatorial problems that arise naturally in the process of mapping and scheduling application software on multi-core architectures such as P2012 which is currently being developed by ST Microelectronics.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF