Memory Power Optimization of Java-Based Embedded Systems Exploiting Garbage Collection Information

Nowadays, Java is used in all types of embedded devices. For these memory-constrained systems, the automatic dynamicmemorymanager (Garbage Collector or GC) has been always a key factor in terms of the Java Virtual Machine (JVM) performance. Moreover, in current embedded platforms, power consumption is becoming as important as performance. Thus, in this paper we present an exploration, from an energy viewpoint, of the different possibilities of memory hierarchies for high-performance embedded systems when used by state-of-the-art GCs. This is a starting point for a better understanding of the interactions between the Java applications, the memory hierarchy and the GC. Hence, we subsequently present two techniques to reduce energy consumption on Java-based embedded systems, based on exploiting GC information. The first technique uses GC execution behavior to reduce leakage energy consumption taking advantage of the low-power mode of actual multi-banked SDRAM memories and it is intended for generational collectors. This technique can achieve a reduction up to 50% of SDRAM memory leakage. The second technique involves the inclusion of a software-controlled (scratchpad) memory that stores GC instructions under the JVM control to reduce the active energy consumption and also improve the performance of the target embedded system and it is aimed at all kind of garbage collectors. For this last technique we have experimented with two different approaches for selecting the GC code to be stored in the scratchpad memory: one static and one dynamic. Our experimental results show that the proposed dynamic scratchpad management approach for GCs enables up to 63% energy consumption reduction and 25% performance improvement during the collector phase, which means, in terms of JVM execution, a global reduction of 29% and 17% for energy and cycles respectively. Overall, this work outlines that the key for an efficient low-power implementation of Java Virtual Machines for high-performance embedded systems is the synergy between the GC choice, the memory architecture tuning, and the inclusion of power management schemes controlled by the JVM, exploiting knowledge of the GC behavior

A selective dynamic compiler for embedded Java virtual machine targeting ARM processors

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2004-2005Ce travail présente une nouvelle technique de compilation dynamique sélective pour les systèmes embarqués avec processeurs ARM. Ce compilateur a été intégré dans la plateforme J2ME/CLDC (Java 2 Micro Edition for Connected Limited Device Con- figuration). L’objectif principal de notre travail est d’obtenir une machine virtuelle accélérée, légère et compacte prête pour l’exécution sur les systèmes embarqués. Cela est atteint par l’implémentation d’un compilateur dynamique sélectif pour l’architecture ARM dans la Kilo machine virtuelle de Sun (KVM). Ce compilateur est appelé Armed E-Bunny. Premièrement, on présente la plateforme Java, le Java 2 Micro Edition(J2ME) pour les systèmes embarqués et les composants de la machine virtuelle Java. Ensuite, on discute les différentes techniques d’accélération pour la machine virtuelle Java et on détaille le principe de la compilation dynamique. Enfin, on illustre l’architecture, le design (la conception), l’implémentation et les résultats expérimentaux de notre compilateur dynamique sélective Armed E-Bunny. La version modifiée de KVM a été portée sur un ordinateur de poche (PDA) et a été testée en utilisant un benchmark standard de J2ME. Les résultats expérimentaux de la performance montrent une accélération de 360 % par rapport à la dernière version de la KVM de Sun avec un espace mémoire additionnel qui n’excède pas 119 kilobytes.This work presents a new selective dynamic compilation technique targeting ARM 16/32-bit embedded system processors. This compiler is built inside the J2ME/CLDC (Java 2 Micro Edition for Connected Limited Device Configuration) platform. The primary objective of our work is to come up with an efficient, lightweight and low-footprint accelerated Java virtual machine ready to be executed on embedded machines. This is achieved by implementing a selective ARM dynamic compiler called Armed E-Bunny into Sun’s Kilobyte Virtual Machine (KVM). We first present the Java platform, Java 2 Micro Edition (J2ME) for embedded systems and Java virtual machine components. Then, we discuss the different acceleration techniques for Java virtual machine and we detail the principle of dynamic compilation. After that we illustrate the architecture, design, implementation and experimental results of our selective dynamic compiler Armed E-Bunny. The modified KVM is ported on a handheld PDA and is tested using standard J2ME benchmarks. The experimental results on its performance demonstrate that a speedup of 360% over the last version of Sun’s KVM is accomplished with a footprint overhead that does not exceed 119 kilobytes

Effective memory management for mobile environments

Smartphones, tablets, and other mobile devices exhibit vastly different constraints compared to regular or classic computing environments like desktops, laptops, or servers. Mobile devices run dozens of so-called “apps” hosted by independent virtual machines (VM). All these VMs run concurrently and each VM deploys purely local heuristics to organize resources like memory, performance, and power. Such a design causes conflicts across all layers of the software stack, calling for the evaluation of VMs and the optimization techniques specific for mobile frameworks. In this dissertation, we study the design of managed runtime systems for mobile platforms. More specifically, we deepen the understanding of interactions between garbage collection (GC) and system layers. We develop tools to monitor the memory behavior of Android-based apps and to characterize GC performance, leading to the development of new techniques for memory management that address energy constraints, time performance, and responsiveness. We implement a GC-aware frequency scaling governor for Android devices. We also explore the tradeoffs of power and performance in vivo for a range of realistic GC variants, with established benchmarks and real applications running on Android virtual machines. We control for variation due to dynamic voltage and frequency scaling (DVFS), Just-in-time (JIT) compilation, and across established dimensions of heap memory size and concurrency. Finally, we provision GC as a global service that collects statistics from all running VMs and then makes an informed decision that optimizes across all them (and not just locally), and across all layers of the stack. Our evaluation illustrates the power of such a central coordination service and garbage collection mechanism in improving memory utilization, throughput, and adaptability to user activities. In fact, our techniques aim at a sweet spot, where total on-chip energy is reduced (20–30%) with minimal impact on throughput and responsiveness (5–10%). The simplicity and efficacy of our approach reaches well beyond the usual optimization techniques

Improving Energy Consumption Of Java Programs

Information and Communications Technologies (ICT) amounts for 10% of the world energy which will keep on growing in the future and 3% of the overall carbon footprint which is now more than the level of CO2 emission as that of the aviation industry. For many past years, the focus was on hardware to optimize the energy consumption of ICT systems. This includes dynamic adaptation of hardware techniques such as fine-grain clock gating, power gating, and dynamic voltage/frequency scaling. However, recent demands of exascale computation, as well as the increasing carbon footprint, require new breakthroughs to make ICT systems more energy-efficient. This is not possible by only making the hardware energy-efficient. As a result, the focus is shifting on software now. Software is one of the most critical bottlenecks while trying to optimize the energy consumption of any ICT system. Software energy consumption can be optimized in several ways like choosing the energy-efficient option in a programming language, using an energy-efficient programming language or choosing an energy-efficient compiling option. In this work, we concentrate on the energy-efficient options and command-line options to optimize software energy consumption. Today’s programming languages provide software developers with several options to perform the same task. For example, in Java, an Array can be copied to other Array either manually or using Java methods. However, not every option available is energy-efficient and the software developers lack the knowledge to choose the best energy-efficient option. We perform various analyses to decide on choosing the best option for different components of Java programming language. These components include data types, operators, control statements, String, exceptions, objects, and Arrays. Java has different command-line options that can be used to tune the JVM. These options can significantly affect the energy behavior of Java applications. We conduct a comprehensive study to evaluate the energy efficiency of Java command-line options. We first stabilize the idle energy consumption of two ICT systems and then evaluate the active energy consumption of SPECjvm2008 benchmarks using different JDKs (Open and Oracle) and Java command-line options. The Java command-line options include client, server, Xbatch, Xcomp, Xfuture, Xint, Xmixed, Xrs, AggressiveOpts, AggressiveHeap, Inline, AlwaysPreTouch, Xnoclassgc, UseSerialGC, UseParallelGC, UseConcMarkSweepGC, and UseG1GC. Next, we present Java Energy Profiler and Optimizer (JEPO) tool to help software developers to write energy-efficient code. This tool is an Eclipse IDE plugin and provides energy efficiency suggestions for Java programming language. It can provide suggestions dynamically while writing code or statically to refactor already written code. For providing suggestions, it analyzes each line of Java file and matches it to the pool of suggestions. JEPO can also help the software developers to automatically measure energy consumption at method granularity to determine the energy-hungry Java methods in software. We hope our findings and tool can help software developers to write energy-efficient code in the future

Investigation of High-Level Language Support in a Resource-Constrained Embedded Environment

Personal computers have gained a significant boost in computational power and digital storage space at a reduced cost in the last decade. In the search of increased programmer productivity and cross platform portability, language popularity has shifted from lower level languages such as C to higher level languages such as Java and C#. Many of today’s embedded systems are experiencing the same development as the personal computers did. However, most companies dealing with embedded devices still use C. We investigated what effect a shift like this would have at Axis Communications. The study was done by setting up C# and Java on a camera and conducting performance tests on it. The analysis showed that when using C# as a replacement for C, we saw improvements in programmer productivity whilst still upholding performance for some applications. For the most performance intense use cases, the performance requirements were not satisfied. With the growth of high-level languages, we do see a bright future for the support for them in embedded systems

Repositioning Tiered HotSpot Execution Performance Relative to the Interpreter

Although the advantages of just-in-time compilation over traditional interpretive execution are widely recognised, there needs to be more current research investigating and repositioning the performance differences between these two execution models relative to contemporary workloads. Specifically, there is a need to examine the performance differences between Java Runtime Environment (JRE) Java Virtual Machine (JVM) tiered execution and JRE JVM interpretive execution relative to modern multicore architectures and modern concurrent and parallel benchmark workloads. This article aims to fill this research gap by presenting the results of a study that compares the performance of these two execution models under load from the Renaissance Benchmark Suite. This research is relevant to anyone interested in understanding the performance differences between just-in-time compiled code and interpretive execution. It provides a contemporary assessment of the interpretive JVM core, the entry and starting point for bytecode execution, relative to just-in-time tiered execution. The study considers factors such as the JRE version, the GNU GCC version used in the JRE build toolchain, and the garbage collector algorithm specified at runtime, and their impact on the performance difference envelope between interpretive and tiered execution. Our findings indicate that tiered execution is considerably more efficient than interpretive execution, and the performance gap has increased, ranging from 4 to 37 times more efficient. On average, tiered execution is approximately 15 times more efficient than interpretive execution. Additionally, the performance differences between interpretive and tiered execution are influenced by workload category, with narrower performance differences observed for web-based workloads and more significant differences for Functional and Scala-type workloads.Comment: 17 page

Flexible Sensor Network Reprogramming for Logistics

Besides the currently realized applications, Wireless Sensor Networks can be put to use in logistics processes. However, doing so requires a level of flexibility and safety not provided by the current WSN software platforms. This paper discusses a logistics scenario, and presents SensorScheme, a runtime environment used to realize this scenario, based on semantics of the Scheme programming language. SensorScheme is a general purpose WSN platform, providing dynamic reprogramming, memory safety (sandboxing), blocking I/O, marshalled communication, compact code transport. It improves on the state of the art by making better use of the little available memory, thereby providing greater capability in terms of program size and complexity. We illustrate the use of our platform with some application examples, and provide experimental results to show its compactness, speed of operation and energy efficiency