Control of Autonomic Parallelism Adaptation on Software Transactional Memory

International audienceParallel programs need to manage the trade-off between the time spent in synchronization and computation. A high parallelism may decrease computing time while increase synchronization cost among threads. A way to improve program performance is to adjust parallelism to balance conflicts among threads. However, there is no universal rule to decide the best parallelism for a program from an offline view. Furthermore, an offline tuning is error-prone. Hence, it becomes necessary to adopt a dynamic tuning-configuration strategy to better manage a STM system. Software Transactional Memory (STM) has emerged as a promising technique, which bypasses locks, to address synchronization issues through transactions. Autonomic computing offers designers a framework of methods and techniques to build automated systems with well-mastered behaviours. Its key idea is to implement feedback control loops to design safe, efficient and predictable controllers, which enable monitoring and adjusting controlled systems dynamically while keeping overhead low. We propose to design feedback control loops to automate the choice of parallelism level at runtime to diminish program execution time

Autonomic Parallelism and Thread Mapping Control on Software Transactional Memory

International audienceParallel programs need to manage the trade-offbetween the time spent in synchronization and computation.The time trade-off is affected by the number of active threadssignificantly. High parallelism may decrease computing time whileincrease synchronization cost. Furthermore thread locality ondifferent cores may impact on program performance too, asthe memory access time can vary from one core to anotherdue to the complexity of the underlying memory architecture.Therefore the performance of a program can be improved byadjusting the number of active threads as well as the mapping ofits threads to physical cores. However, there is no universal rule todecide the parallelism and the thread locality for a program froman offline view. Furthermore, an offline tuning is error-prone.In this paper, we dynamically manage parallelism and threadlocalities. We address multiple threads problems via SoftwareTransactional Memory (STM). STM has emerged as a promisingtechnique, which bypasses locks, to address synchronization issuesthrough transactions. Autonomic computing offers designers aframework of methods and techniques to build autonomic systemswith well-mastered behaviours. Its key idea is to implementfeedback control loops to design safe, efficient and predictablecontrollers, which enable monitoring and adjusting controlledsystems dynamically while keeping overhead low. We propose todesign a feedback control loop to automate thread managementat runtime and diminish program execution time

Towards Confidential Computing: A Secure Cloud Architecture for Big Data Analytics and AI

Cloud computing provisions computer resources at a cost-effective way based on demand. Therefore it has become a viable solution for big data analytics and artificial intelligence which have been widely adopted in various domain science. Data security in certain fields such as biomedical research remains a major concern when moving their workflows to cloud, because cloud environments are generally outsourced which are more exposed to risks. We present a secure cloud architecture and describes how it enables workflow packaging and scheduling while keeping its data, logic and computation secure in transit, in use and at rest.Comment: 2023 IEEE 16th International Conference on Cloud Computing (IEEE CLOUD), Chicago, Illinois, USA, July 2-8, 202

Contrôle autonomique du parallélisme et du placement de threads pour les mémoires transactionnelles logicielles

Parallel programs need to manage the trade-off between the time spent in synchronisation and computation. The trade-off is significantly affected by the number of active threads. High parallelism may decrease computing time while increase synchronisation cost. Furthermore, thread placement on different cores may impact on program performance, as the data access time can vary from one core to another due to intricacies of its underlying memory architecture. Therefore, the performance of a program can be improved by adjusting its parallelism degree and the mapping of its threads to physical cores. Alas, there is no universal rule to decide them for a program from an offline view, especially for a program with online behaviour variation. Moreover, offline tuning is less precise. This thesis presents work on dynamical management of parallelism and thread placement. It addresses multithread issues via Software Transactional Memory (STM). STM has emerged as a promising technique, which bypasses locks, to tackle synchronisation through transactions. Autonomic computing offers designers a framework of methods and techniques to build autonomic systems with well-mastered behaviours. Its key idea is to implement feedback control loops to design safe, efficient and predictable controllers, which enable monitoring and adjusting controlled systems dynamically while keeping overhead low. This dissertation proposes feedback control loops to automate management of threads at runtime and diminish program execution time.L’exécution de programmes paralléles demande à établir un compromis entre le temps de calcul (nombre de threads) et le temps de synchronisation. Ce compromis dépend principalement du nombre de threads actifs. Un haut degré de parallélisme (beaucoup de threads) permet généralement de diminuer le temps de calcul, mais peut aussi avoir pour conséquence d’augmenter les surcoûts de synchronisation entre threads. De plus, le placement des threads sur les cœurs peut impacter les performances du programme, car le temps pour accéder aux données en mémoire peut varier d’un cœur à l’autre en raison de la contention sur la la hiérarchie mémoire. Ainsi, la performance d’un programme peut être améliorée en adaptant le nombre de threads actifs et en plaçant correctement les threads sur les cœurs de calcul. Cependant, il n’existe pas de règle universelle permettant de décider a priori du niveau de parallélisme optimal et du placement de threads d’un programme, en particulier pour un programme avec les changemets de comportement dynamique. D’ailleurs, un paramétrage hors ligne est moins précis. Cette thèse présente un travail sur la gestion dynamique du parallélisme et du placement de threads. Cette thèse s’attaque au problème de gestion de threads utilisant de la mémoire transactionnelle logicielle (Software Transactional Memory, STM). La mémoire transactionnelle logicielle constitue une technique prometteuse pour traiter le problème de synchronisation en évitant les verrous.Le concept de calcul autonomique offre aux programmeurs un cadre de méthodeset techniques pour construire des systèmes auto-adaptatifs ayant un comportementmaîtrisé. L’idée clé est d’implémenter des boucles de rétroaction afin de concevoir des contrôleurs sûrs, efficaces et prédictibles, permettant d’observer et d’ajuster de manière dynamique les systèmes contrôlés, tout en minimisant le surcoût d’une telle méthode. La thèse propose de concevoir des boucles de rétroaction afin d’automatiser le gestion de threads à l’exécution avec comme objectif la réduction du temps d’exécution des programmes

Autonomic Parallelism Adaptation for Software Transactional Memory

International audienceParallel programs need to manage the time trade-off between synchronization and computation. A high parallelism may decrease computing time but meanwhile increase synchronization cost. Software Transactional Memory (STM) has emerged as a promising technique, which bypasses locks, to address synchronization issues through transactions. A way to reduce conflicts is by adjusting parallelisms. However, there is no universal rule to decide the best parallelism for a program from an offline view. Furthermore, an offline tuning is costly and error-prone. Hence, it becomes necessary to adopt a dynamical tuning-configuration strategy to better manage a STM system. Autonomic computing offers designers a framework of methods and techniques to build systems with well-mastered behaviours. Its key idea is to implement feedback control loops to design safe, efficient and predictable controllers, which enable monitoring and adjusting controlled systems dynamically while keeping overhead low

Autonomic Parallelism Adaptation for Software Transactional Memory

International audienceParallel programs need to manage the time trade-off between synchronization and computation. A high parallelism may decrease computing time but meanwhile increase synchronization cost. Software Transactional Memory (STM) has emerged as a promising technique, which bypasses locks, to address synchronization issues through transactions. A way to reduce conflicts is by adjusting parallelisms. However, there is no universal rule to decide the best parallelism for a program from an offline view. Furthermore, an offline tuning is costly and error-prone. Hence, it becomes necessary to adopt a dynamical tuning-configuration strategy to better manage a STM system. Autonomic computing offers designers a framework of methods and techniques to build systems with well-mastered behaviours. Its key idea is to implement feedback control loops to design safe, efficient and predictable controllers, which enable monitoring and adjusting controlled systems dynamically while keeping overhead low

Adaptation autonome du parallélisme dans un système de mémoire transactionnelle logicielle

Parallel programs need to manage the time trade-off between synchronization and computation. A high parallelism may decrease computing time but meanwhile increase synchronization cost among threads. Software Transactional Memory (STM) has emerged as a promising technique, which bypasses locks, to address synchronization issues through transactions. A way to reduce conflicts is by adjusting the parallelism, as a suitable parallelism can maximize program performance. However, there is no universal rule to decide the best parallelism for a program from an offline view. Furthermore, an offline tuning is costly and error-prone. Hence, it becomes necessary to adopt a dynamical tuning-configuration strategy to better manage a STM system. Autonomic computing offers designers a framework of methods and techniques to build autonomic systems with well-mastered behaviours. Its key idea is to implement feedback control loops to design safe, efficient and predictable controllers, which enable monitoring and adjusting controlled systems dynamically while keeping overhead low. We propose to design feedback control loops to automate the choice of parallelism level at runtime and diminish program execution time.L'exécution de programmes parallèles demande la gestion d'un compromis entre le temps de synchronisation et le temps de calcul. Un haut degré de parallélisme peut diminuer le temps de calcul, mais augmenter le coût en temps de synchronisation entre processus légers. La mémoire transactionnelle logicielle (Software Transactional Memory, STM) est une technique prometteuse permettant de traiter le problème de synchronisation entre transactions en évitant les verrous. Une manière de réduire les conflits entre transactions est d'ajuster le degré de parallélisme, afin de trouver le niveau de parallélisme permettant de maximiser les performances du programme. Cependant, il n'existe pas de règle universelle permettant de décider du niveau de parallélisme optimal d'un programme hors ligne. De plus, un paramétrage hors ligne est coûteux et peut entraîner des erreurs. Par conséquent, il est nécessaire d'adopter une stratégie de paramétrage et reconfiguration dynamique afin de mieux gérer un système STM. Le concept de calcul autonomique offre aux programmeurs un cadre de méthodes et techniques pour construire des systèmes autonomiques ayant un comportement maîtrisé. L'idée clé est d'implémenter des boucles de rétroaction afin de concevoir des contrôleurs sûrs, efficaces et prédictibles, permettant d'observer et d'ajuster de manière dynamique les systèmes contrôlés, tout en gardant bas le surcoût d'une telle méthode. Nous proposons de concevoir des boucles de rétroaction afin d'automatiser le choix du niveau de parallélisme à l'exécution, afin de diminuer le temps d'exécution du programme