Cross-layer memory management for managed language applications

Author:

Jantz Michael R.1,Robinson Forrest J.2,Kulkarni Prasad A.2,Doshi Kshitij A.3

Affiliation:

1. University of Tennessee, USA

2. University of Kansas, USA

3. Intel, USA

Abstract

Performance and energy efficiency in memory have become critically important for a wide range of computing domains. However, it is difficult to control and optimize memory power and performance because these effects depend upon activity across multiple layers of the vertical execution stack. To address this challenge, we construct a novel and collaborative framework that employs object placement, cross-layer communication, and page-level management to effectively distribute application objects in the DRAM hardware to achieve desired power/performance goals. In this work, we describe the design and implementation of our framework, which is the first to integrate automatic object profiling and analysis at the application layer with fine-grained management of memory hardware resources in the operating system. We demonstrate the utility of our framework by employing it to more effectively control memory power consumption. We design a custom memory-intensive workload to show the potential of our approach. Next, we develop sampling and profiling-based analyses and modify the code generator in the HotSpot VM to understand object usage patterns and automatically determine and control the placement of hot and cold objects in a partitioned VM heap. This information is communicated to the operating system, which uses it to map the logical application pages to the appropriate DRAM ranks according to user-defined provisioning goals. We evaluate our framework and find that it achieves our test goal of significant DRAM energy savings across a variety of workloads, without any source code modifications or recompilations.

Funder

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3