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K42 is one of the few recent research projects that is examining operating system design structure issues in the context of new whole-system design. K42 is open source and was designed from the ground up to perform well and to be scalable, customizable, and maintainable. The project was begun in 1996 by a team at IBM Research. Over the last nine years there has been a development effort on K42 from between six to twenty researchers and developers across IBM, collaborating universities, and national laboratories. K42 supports the Linux API and ABI, and is able to run unmodified Linux applications and libraries. The approach we took in K42 to achieve scalability and customizability has been successful. The project has produced positive research results, has resulted in contributions to Linux and the Xen hypervisor on Power, and continues to be a rich platform for exploring system software technology. Today, K42, is one of the key exploratory platforms in the DOE’s FAST-OS program, is being used as a prototyping vehicle in IBM’s PERCS project, and is being used by universities and national labs for exploratory research. In this paper, we provide insight into building an entire system by discussing the motivation and history of K42, describing its fundamental technologies, and presenting an overview of the research directions we have been pursuing.

We discuss diversity and heterogeneity in manycore computer systems, and identify three distinct types of diversity, all of which present challenges to operating system designers and application writers alike. We observe that most current research work has concentrated on a narrow form of one of these (non-uniform memory access) to the exclusion of the others, and show with measurement why this makes sense in the short term. However, we claim that this is not viable in the long term given current processor and system roadmaps, and present our approach to dealing with both heterogeneous hardware within a single system, and the increasing diversity of complete system configurations: we directly represent detailed system information in an expressive “system knowledge base ” accessible to applications and OS subsystems alike, and use this to control tasks such as scheduling and resource allocation. 1.

With the growing awareness that individual hardware cores will not continue to produce the same level of performance improvement, there is a need to develop an integrated approach to performance optimization. In this paper we present a paradigm for Continuous Program Optimization (CPO), whereby automatic agents monitor and optimize application and system performance. The monitoring data is used to analyze and create models of application and system behavior. Using this analysis, we describe how CPO agents can improve the performance of both the application and the underlying system. Using the CPO paradigm, we implemented cooperating page size optimization agents that automatically optimize large page usage. An offline agent uses vertically integrated performance data to produce a page size benefit analysis for different categories of data structures within an application. We show how an online CPO agent can use the results of the predictive analysis to automatically improve application performance. We validate that the predictions made by the CPO agent reflect the actual performance gains of up to 60 % across a range of scientific applications including the SPECcpu2000 floating point benchmarks and two large high performance computing (HPC) applications. 1.

Understanding the detailed behavior of an operating system is crucial for making informed design decisions. But such an understanding is very hard to achieve, due to the increasing complexity of such systems and the fact that they are implemented and maintained by large and diverse groups of developers. Tools like Klogger — presented in this paper — can help by enabling fine-grained logging of system events and the sharing of a logging infrastructure between multiple developers and researchers, facilitating a methodology where design evaluation can be an integral part of kernel development. We demonstrate the need for such methodology by a host of case studies, using Klogger to better understand various subsystems in the Linux kernel, and pinpointing overheads and problems therein. 1

K42 is an open-source scalable research operating system well suited to support systems research. The primary goals of K42’s design that support such research include flexibility to allow a multitude of policies and implementations to be supported simultaneously, extensibility to allow new policies and implementations to be readily added, and scalability to enable good performance for both small and large applications on both small and large multiprocessor systems. The goals are accomplished via key features including an objectoriented structure that allows specialized resource management implementations and policies on a per-resource, perapplication basis, implementation in user-level servers of much of the system functionality, and a sophisticated set of underlying services that provides a programming model for developing system software in a scalable and modular fashion. These characteristics make K42 an attractive framework for prototyping new operating system ideas. In addition, K42 has a sophisticated performance monitoring infrastructure allowing a thorough understanding of new ideas to be gained. The above framework combined with a consistent emphasis on scalability makes K42 well suited for high-end computing initiatives. In this paper, we describe the structure of K42 which contributes to the advantageous prototyping environment, and demonstrate how to utilize it by describing ongoing research efforts.

Adaptive, or self-aware, computing has been proposed as one method to help application programmers confront the growing complexity of multicore software development. However, existing approaches to adaptive systems are largely ad hoc and often do not manage to incorporate the true performance goals of the applications they are designed to support. This paper presents an enabling technology for adaptive computing systems: Application Heartbeats. The Application Heartbeats framework provides a simple, standard programming interface that applications can use to indicate their performance and system software (and hardware) can use to query an application’s performance. Several experiments demonstrate the simplicity and efficacy of the Application Heartbeat approach. First the PARSEC benchmark suite is instrumented with Application Heartbeats to show the broad applicability of the interface. Then, an adaptive H.264 encoder is developed to show how applications might use Application Heartbeats internally. Next, an external resource scheduler is developed which assigns cores to an application based on its performance as specified with Application Heartbeats. Finally, the adaptive H.264 encoder is used to illustrate how Application Heartbeats can aid fault tolerance. 1.

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Patches to modern operating systems, including bug fixes and security updates, and the reboots and downtime they require, cause tremendous problems for system users and administrators. The aim of this research is to develop a model for dynamic update of operating systems, allowing a system to be patched without the need for a reboot or other service interruption. In this work, a model for dynamic update based on operating system modularity is developed and evaluated using a prototype implementation for the K42 operating system. The prototype is able to update kernel code and data structures, even when the interfaces between kernel modules change. When applying an update, at no point is the system’s entire execution blocked, and there is no additional overhead after an update has been applied. The base runtime overhead is also very low. An analysis of the K42 revision history shows that approximately 79 % of past performance and bug-fix changes to K42 could be converted to dynamic updates, and the proportion would be even higher if the changes were being developed for dynamic update. The model also extends to other systems such as Linux and BSD, that although structured modularly, are not strictly object-oriented like

Fay is a flexible platform for the efficient collection, processing, and analysis of software execution traces. Fay provides dynamic tracing through use of runtime instrumentation and distributed aggregation within machines and across clusters. At the lowest level, Fay can be safely extended with new tracing primitives, including even untrusted, fully-optimized machine code, and Fay can be applied to running user-mode or kernel-mode software without compromising system stability. At the highest level, Fay provides a unified, declarative means of specifying what events to trace, as well as the aggregation, processing, and analysis of those events. We have implemented the Fay tracing platform for Windows and integrated it with two powerful, expressive systems for distributed programming. Our implementation is easy to use, can be applied to unmodified production systems, and provides primitives that allow