Runtime Resource Management of Emerging Applications in Heterogeneous Architectures

Runtime resource management for heterogeneous computing systems is becoming more and more complex as workloads in these platforms get increasingly more diverse and the conflicts grow between heterogeneous architectural components and their resource demands. The goal of these runtime resource management mechanisms is to achieve the overall system goal for dynamic workloads while coordinating system resources in a robust and adaptive fashion.To address the complexities in heterogeneous computing systems, state-of-the-art techniques that use heuristics or machine learning have been proposed. On the other hand, conventional control theory can be used for formal guarantees, but may face unmanageable complexity for modeling system dynamics when dealing with heterogeneous computing platforms.In this thesis, we initially analyze a variety of runtime resource management methods and introduce a classification for these methods capturing the utilized resources and metrics. We cover heuristic, machine learning and control theory methods used to manage resources such as performance, power, energy, temperature, Quality-of-Service (QoS) and reliability of the system.In addition, we explore a variety of dynamic resource management frameworks that provide significant gains in terms of self-optimization and self-adaptivity. This includes simulation infrastructures, hardware platforms enhanced with multi-layer management mechanisms and corresponding software frameworks that enable management policies for these systems in an effective and adaptive manner.Ultimately, we address the problem of optimizing energy efficiency, power consumption, performance and QoS in heterogeneous systems by proposing adaptive runtime policies. The proposed methods in this thesis, take into account the constraints and requirements defined by user, dynamic workloads and coordination between conflicting objectives.The projects presented in this dissertation show effectiveness in responding to abrupt changes in heterogeneous computing systems by dynamically adapting to changing application and system behavior at runtime, and are thus able to provide significant improvement compared to commonly used static resource management methods

Runtime Resource Management of Emerging Applications in Heterogeneous Architectures

Runtime resource management for heterogeneous computing systems is becoming more and more complex as workloads in these platforms get increasingly more diverse and the conflicts grow between heterogeneous architectural components and their resource demands. The goal of these runtime resource management mechanisms is to achieve the overall system goal for dynamic workloads while coordinating system resources in a robust and adaptive fashion.To address the complexities in heterogeneous computing systems, state-of-the-art techniques that use heuristics or machine learning have been proposed. On the other hand, conventional control theory can be used for formal guarantees, but may face unmanageable complexity for modeling system dynamics when dealing with heterogeneous computing platforms.In this thesis, we initially analyze a variety of runtime resource management methods and introduce a classification for these methods capturing the utilized resources and metrics. We cover heuristic, machine learning and control theory methods used to manage resources such as performance, power, energy, temperature, Quality-of-Service (QoS) and reliability of the system.In addition, we explore a variety of dynamic resource management frameworks that provide significant gains in terms of self-optimization and self-adaptivity. This includes simulation infrastructures, hardware platforms enhanced with multi-layer management mechanisms and corresponding software frameworks that enable management policies for these systems in an effective and adaptive manner.Ultimately, we address the problem of optimizing energy efficiency, power consumption, performance and QoS in heterogeneous systems by proposing adaptive runtime policies. The proposed methods in this thesis, take into account the constraints and requirements defined by user, dynamic workloads and coordination between conflicting objectives.The projects presented in this dissertation show effectiveness in responding to abrupt changes in heterogeneous computing systems by dynamically adapting to changing application and system behavior at runtime, and are thus able to provide significant improvement compared to commonly used static resource management methods

Rapid, High-Level Performance Estimation for DSE Using Calibrated Weight Tables

Part 5: Embedded HW/SW Design and ApplicationsInternational audienceAutomated Design Space Exploration (DSE) is a critical part of system-level design. It relies on performance estimation to evaluate design alternatives. However, since a plethora of design alternatives need to be compared, the run-time of performance estimation itself may pose a bottleneck. In DSE, fastest performance estimation is of essence while some accuracy may be sacrificed. Fast estimation can be realised through capturing application demand, as well as Processing Element (PE) supply (later on called weight table) in a matrix each. Then, performance estimation (retargeting) is reduced to a matrix multiplication. However, defining the weight table from a data sheet is impractical due to the multitude of (micro-) architecture aspects.This paper introduces a novel methodology, WeiCal, for automatically generating Weight Tables in the context of C source-level estimation using application profiling and Linear Programming (LP). LP solving is based on the measured performance of training benchmarks on an actual PE. We validated WeiCal using a synthetic processor and benchmark model, and also analyse the impact of non-observable features on estimation accuracy. We evaluate the efficiency using 49 benchmarks on 2 different processors with varying configurations (multiple memory configurations and software optimizations). On a 3.1 GHz i5-3450 Intel host, 25 million estimations/second can be obtained regardless of the application size and PE complexity. The accuracy is sufficient for early DSE with a 24% average error

Teratogenic effects of Origanum Vulgare extract in mice fetals

Background: A number of studies on reproduction have mentioned Origanum Vulgare extract’s ability to reduce mortality rates and improve fertility rates. However, other studies have suggested that it is possible to use Origanum Vulgare extract to induce abortion. The aim of this study was to investigate the effect of different doses of Origanum Vulgare on embryo survival and macroscopic abnormalities in mice.Methods: In this study, 24 mice Balb/c female weighting approximately 25-30 g were divided into 4 groups. Origanum Vulgare extract was prepared; different concentrations (2.5, 12.5, and 25 mg in 0.25 ml distilled water) were administered, by oral gavage, to three experimental groups of mice between day 6 (starting gastrulation) until day 15 of pregnancy (end of organogenesis). The control group consisted of six mice that received 0.25 ml of distilled water daily. On day 16 of study, pregnant mice were anesthetized by chloroform and fetuses were removed and stained with Alcian Blue, Alizarin Red s and microwave irradiation. Morphological and skeletal abnormalities were investigated by light and stereomicroscopes.Results: The results of this study showed that high doses of the Origanum Vulgare extract significantly decreased the mean number of embryos (100.5, P>0.05), mean number of live embryos (70.5, P>0.05) in each mouse and resulted in significant reduction in mean weight(11848 mg, P>0.05) and crown-rump length(11.90.23 mm, P>0.05) and the overall size of fetuses compared to control group, whereas there was no significant difference between the groups receiving low dose of Origanum Vulgare extract with control group. In addition, under the effect of the Origanum Vulgare extract the subcutaneous bleeding seemed (20.1, P>0.05) significantly more frequent compared to the control group. Conclusion: Origanum Vulgare extract did not have any positive effect on fetal development; and high dosages led to an increased incidence rate of abortion and fetal malformations in the fetuses of women who received it

On-chip dynamic resource management

Written by leading experts in the field, researchers and students are provided a structured review and discussion of the state of the art that is divided along the primary objectives of resource management techniques: performance, power, reliability and quality of service

Trends in on-chip dynamic resource management

The Complexity of emerging multi/many-core architectures and diversity of modern workloads demands coordinated dynamic resource management methods. We introduce a classification for these methods capturing the utilized resources and metrics. In this work, we use this classification to survey the key efforts in dynamic resource management. We first cover heuristic and optimization methods used to manage resources such as power, energy, temperature, Quality-of-Service (QoS) and reliability of the system. We then identify some of the machine learning based methods used in tuning architectural parameters in computer systems. In many cases, resource managers need to enforce design constraints during runtime with a certain level of guarantee. Hence, we also study the trend in deploying formal control theoretic approaches in order to achieve efficient and robust dynamic resource management