Mode d'adressage | EPFL Graph Search

Les modes d'adressage sont un aspect de l'architecture des processeurs et de leurs jeux d'instructions. Les modes d'adressage définis dans une architecture régissent la façon dont les instructions en langage machine identifient leurs opérandes. Un mode d'adressage spécifie la façon dont est calculée l'adresse mémoire effective d'un opérande à partir de valeurs contenues dans des registres et de constantes contenues dans l'instruction ou ailleurs dans la machine. En programmation informatique, les personnes concernées par les modes d'adressage sont principalement celles qui programment en assembleur et les auteurs de compilateurs. Modes d'adressage pour les branchements L'adresse de la prochaine instruction à exécuter est contenue dans un registre spécial du processeur, appelé compteur ordinal (souvent noté IP, initiales de l'anglais instruction pointer) ou compteur de programme. Les instructions de branchement (ou de saut) visent à modifier la valeur du compteur ordinal.

Multi-Objective System-Level Management of Modern Green Data Centers

Ali Pahlevan

In our modern society, the average citizen has turned into a daily cloud user. Despite being virtually transparent for the user, internet services require data centers behind the scenes. Data centers burn several megawatts of power and their electricity bill is a major fraction of their costs. Hence, modern cloud data centers need to tackle efficiently the increasing demand for computing resources while at the same time addressing the energy efficiency challenge. This is a complex optimization problem that worsens as more constraints and objectives are added. In public clouds, virtualization transforms a data center into a flexible cloud infrastructure in which Virtual Machines (VMs) behave as separate entities that share physical hardware resources. Therefore, it is essential to develop resource provisioning policies that are aware of VMs characteristics and applicable in dynamic scenarios. To address these challenges, this thesis first presents heuristic and Machine Learning (ML)-based VM allocation methods for various data center scenarios. Then, a novel hyper-heuristic algorithm is proposed that exploits the benefits of both methods by dynamically finding the best one according to a user-defined metric. However, optimizing the energy and cost of a single data center powered by the grid is not enough in today's cloud computing, where multiple data centers are used to deploy online services, and utilize renewable energy sources to reduce their carbon footprint. This thesis also presents a two-phase multi-objective VM placement along with a dynamic migration technique, for geo-distributed data centers coupled with renewable and Electrical Energy Storage (EES) sources to tackle the challenges of operational cost optimization and energy-performance trade-offs. Furthermore, in order to efficiently minimize cost, power market operators have recently introduced emerging demand-response programs, in which electricity consumers regulate their power usage following providers' requests. Therefore, it is essential to develop bidding strategies for data centers to participate in emerging power markets together with power management policies that are aware of power market requirements at run-time. In this thesis I also propose a strategy to jointly optimize the data center demand-response provision problem and VM allocation that satisfies the hour-ahead power market constraints in the presence of renewable and EES energy. Even if novel data center resource management techniques can efficiently tackle the dramatic increase in the number of servers, each computing server remains power limited due to effect of post-Dennard scaling. Therefore, techniques such as Near-Threshold Computing (NTC) need to complement novel system-level approaches to improve data centers' energy efficiency. For this purpose, I first use an accurate power modeling characterization for a new server architecture based on the Fully Depleted Silicon On Insulator (FD-SOI) process technology that enables NTC features. Then, I explore the new energy-performance trade-offs brought by NTC servers when executing virtualized applications. Finally, based on this analysis, I propose an energy proportionality-aware VM allocation method at data center level that exploits the knowledge of VMs characteristics together with the accurate power model presented for NTC servers. As a result, the proposed approach increases the energy proportionality of next-generation NTC-based data centers.

EPFL2019

Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation

Aude Billard, Miao Li

We present a unified framework for grasp planning and in-hand grasp adaptation using visual, tactile, and proprioceptive feedback. The main objective of the proposed framework is to enable fingertip grasping by addressing problems of changed weight of the object, slippage, and external disturbances. For this purpose we introduce the Hierarchical Fingertip Space as a representation enabling optimization for both efficient grasp synthesis and online finger gaiting. Grasp synthesis is followed by a grasp adaptation step that consists of both grasp force adaptation through impedance control and regrasping/finger gaiting when the former is not sufficient. Experimental evaluation is conducted on an Allegro hand mounted on a Kuka LWR arm.

Institute of Electrical and Electronics Engineers2016

Compilation and Code Optimization for Data Analytics

Amir Shaikhha

The trade-offs between the use of modern high-level and low-level programming languages in constructing complex software artifacts are well known. High-level languages allow for greater programmer productivity: abstraction and genericity allow for the same functionality to be implemented with significantly less code compared to low-level languages. Modularity, object-orientation, functional programming, and powerful type systems allow programmers not only to create clean abstractions and protect them from leaking, but also to define code units that are reusable and easily composable, and software architectures that are adaptable and extensible. The abstraction, succinctness, and modularity of high-level code help to avoid software bugs and facilitate debugging and maintenance. The use of high-level languages comes at a performance cost: increased indirection due to abstraction, virtualization, and interpretation, and superfluous work, particularly in the form of tempory memory allocation and deallocation to support objects and encapsulation. As a result of this, the cost of high-level languages for performance-critical systems may seem prohibitive. The vision of abstraction without regret argues that it is possible to use high-level languages for building performance-critical systems that allow for both productivity and high performance, instead of trading off the former for the latter. In this thesis, we realize this vision for building different types of data analytics systems. Our means of achieving this is by employing compilation. The goal is to compile away expensive language features -- to compile high-level code down to efficient low-level code.

EPFL2018

Multi-Objective System-Level Management of Modern Green Data Centers

Ali Pahlevan

EPFL2019