On the Scalability, Performance Isolation and Device Driver Transparency of the IHK/McKernel Hybrid Lightweight Kernel
On the Scalability, Performance Isolation and Device Driver Transparency of the IHK/McKernel Hybrid Lightweight Kernel
复制标题
论IHK/McKernel混合轻量级内核的可扩展性、性能隔离和设备驱动透明性
DOI:
10.1109/ipdps.2016.80
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发表时间:
2016
期刊:
影响因子:
--
通讯作者:
Y. Ishikawa
中科院分区:
文献类型:
--
作者:
Balazs Gerofi;Masamichi Takagi;A. Hori;Gou Nakamura;Tomoki Shirasawa;Y. Ishikawa
Extreme degree of parallelism in high-end computing requires low operating system noise so that large scale, bulk-synchronous parallel applications can be run efficiently. Noiseless execution has been historically achieved by deploying lightweight kernels (LWK), which, on the other hand, can provide only a restricted set of the POSIX API in exchange for scalability. However, the increasing prevalence of more complex application constructs, such as in-situ analysis and workflow composition, dictates the need for the rich programming APIs of POSIX/Linux. In order to comply with these seemingly contradictory requirements, hybrid kernels, where Linux and a lightweight kernel (LWK) are run side-by-side on compute nodes, have been recently recognized as a promising approach. Although multiple research projects are now pursuing this direction, the questions of how node resources are shared between the two types of kernels, how exactly the two kernels interact with each other and to what extent they are integrated, remain subjects of ongoing debate. In this paper, we describe IHK/McKernel, a hybrid software stack that seamlessly blends an LWK with Linux by selectively offloading system services from the lightweight kernel to Linux. Specifically, we are focusing on transparent reuse of Linux device drivers and detail the design of our framework that enables the LWK to naturally leverage the Linux driver codebase without sacrificing scalability or the POSIX API. Through rigorous evaluation on a medium size cluster we demonstrate how McKernel provides consistent, isolated performance for simulations even in face of competing, in-situ workloads.