Managed Runtimes for Energy Efficient Multicore Architecture

在现代异构体系结构上开发、控制和优化分布式应用程序是一项复杂的任务，涉及到原始性能之间的权衡（每秒操作数），功耗（焦耳每秒），对于许多应用，计算的准确性（适当定义），所有这些都可以在供给和需求方面变化-从算法和/或硬件-查看致力于大数据处理的Apache项目（https：projects.apache.org/projects.html?虽然有些项目比其他项目更受欢迎，但快速分析揭示了两个主要趋势。首先，大多数软件框架内部都有一个数据驱动或事件驱动的计算模型。其次，大多数软件框架都运行在Java虚拟机上。曼彻斯特大学在数据驱动计算，JIT编译器，运行时系统和虚拟化方面拥有独特的知识。例如，我们一直在将Maxine虚拟机（aresearch JVM，https：//en.wikipedia.org/wiki/Maxine_Virtual_Machine）移植到ARMv7。此外，我们还包括并改进了Maxine VM中GRAAL JIT编译器的支持和性能（https：//arxiv.org/abs/1509.04085）。Maxine VM在ARMv7上的当前状态能够在Xilinx Zynq（双核ARMCortex A9，1GB内存）等平台上运行复杂的应用程序，例如SLAMBench。有关GRAAL以及如何将其集成到OpenJDK中的更多信息，请访问https：github.com/graalvm/graalvm和http://hg.openjdk.java.net/graal/graal-jvmci-9/The曼彻斯特大学也开始致力于Maxine VM到ARMv 8的端口。学生哈特利开始这个过程作为他的硕士论文的一部分。有一个重大的工程努力，需要专门，以便能够优化服务器端的大数据框架，基于ARMv 8的数据中心。主要的研究问题是，我们如何以及在哪里可以优化数据驱动的计算，为大数据处理在ARMv 8服务器上？这个博士项目采取的方法是研究运行时系统。也就是说，寻找JIT编译器，内存管理，JVM以及与操作系统和/或直接与hypervisor的交互的改进。这个博士学位是面向系统的，我们将遵循由构建原型驱动的调查方法，这些原型展示了我们的新想法。为了取得进展，博士生需要掌握文献，并熟悉：性能和能源的托管ARM系统优化;编译器和运行时技术（特别是基于ARM的系统）; ARM架构（多核，特别是ARMv 8系统）。

Developing, controlling and optimizing distributed applications on modern heterogeneousarchitectures in the context of energy efficiency is a complex task involving trade-offs between rawperformance (operations per second), power consumption (joules per second) and, for manyapplications, the accuracy of the computation (suitably defined), all of which may vary in terms ofsupply and demand - from either the algorithm and/or the hardware - over the lifetime of anapplication.Looking at the Apache projects dedicated to Big Data processing(https://projects.apache.org/projects.html?category), there are 36 different software frameworks.Although there are some projects that are more popular than others, a quick analysis reveals two keytrends. First, most of the software frameworks have inside a data-driven or event-driven model ofcomputation. Secondly, most of the software frameworks run on Java Virtual Machines.The University of Manchester has unique knowledge on data-driven computations, JIT compilers,runtime systems and virtualization. For example, we have been porting the Maxine Virtual Machine (aresearch JVM, https://en.wikipedia.org/wiki/Maxine_Virtual_Machine) to the ARMv7. In addition wehave included and improved the support and performance of the GRAAL JIT compiler in Maxine VM(https://arxiv.org/abs/1509.04085). The current status of Maxine VM on ARMv7 is capable of runningof complex applications, e.g. SLAMBench, on platforms such as the Xilinx Zynq (dual-core ARMCortex A9 with 1GB of memory).For more information on GRAAL and how it is being integrated into the OpenJDK visithttps://github.com/graalvm/graalvm and http://hg.openjdk.java.net/graal/graal-jvmci-9/The University of Manchester has also started working on the port of Maxine VM to ARMv8. Thestudent Hartley started this process as part of his MSc thesis. There is a significant engineering effortthat needs to be dedicated so as to be able to optimize server side Big Data frameworks for ARMv8-based datacenters.The main research question is how and where we can optimize data-driven computations for Big Dataprocessing on ARMv8 servers? The approach taken by this PhD project is to investigate the runtimesystem. That is, look for improvements at the JIT compiler, memory management, JVM as well as theinteraction with the OS and or directly with a hypervisor.This PhD is systems-oriented and we will follow a methodology of investigation driven by buildingprototypes which demonstrate our novel ideas. In order to make progress, the PhD student needs tocommand the literature in, and become familiar with: Managed Runtime Systems optimizations forperformance and energy plus ; compiler and runtime technologies (specifically for ARM-basedsystems); ARM architecture (many-cores, particularly ARMv8 systems.