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In this article we examine the problem of extending modern operating systems to run efficiently on large-scale shared-memory multiprocessors without a large implementation effort. Our approach brings back an idea popular in the 1970s: virtual machine monitors. We use virtual machines to run multiple commodity operating systems on a scalable multiprocessor. This solution addresses many of the challenges facing the system software for these machines. We demonstrate our approach with a prototype called Disco that runs multiple copies of Silicon Graphics' IRIX operating system on a multiprocessor. Our experience shows that the overheads of the monitor are small and that the approach provides scalability as well as the ability to deal with the nonuniform memory access time of these systems. To reduce the memory overheads associated with running multiple operating systems, virtual machines transparently share major data structures such as the program code and the file system buffer cache. We use the distributed-system support of modern operating systems to export a partial single system image to the users. The overall solution achieves most of the benefits of operating systems customized for scalable multiprocessors, yet it can be achieved with a significantly smaller implementation effort.

Pioneering networks of cameras that can search for wildland fire signatures have been in development for some years (High Performance Wireless Research & Education Network—HPWREN cameras and the ALERT Wildfire camera). While these cameras have proven their worth in monitoring fires reported by other means, we have developed a functioning prototype system that can detect smoke from fires usually within 15 min of ignition, while averaging less than one false positive per day per camera. This smoke detection system relies on machine learning-based image recognition software and a cloud-based work-flow capable of scanning hundreds of cameras every minute. The system is operating around the clock in Southern California and has already detected some fires earlier than the current best methods—people calling emergency agencies or satellite detection from the Geostationary Operational Environmental Satellite (GOES) satellites. This system is already better than some commercial systems and there are still many unexplored methods to further improve accuracy. Ground-based cameras are not going to be able to detect every wildfire, and so we are building a system that combines the best of terrestrial camera-based detection with the best approaches to satellite-based detection.

Despite the fact that large-scale shared-memory multiprocessors have been commercially available for several years, system software that fully utilizes all their features is still not available, mostly due to the complexity and cost of making the required changes to the operating system. A recently proposed approach, called Disco, substantially reduces this development cost by using a virtual machine monitor that leverages the existing operating system technology. In this paper we present a system called Cellular Disco that extends the Disco work to provide all the advantages of the hardware partitioning and scalable operating system approaches. We argue that Cellular Disco can achieve these benefits at only a small fraction of the development cost of modifying the operating system. Cellular Disco effectively turns a large-scale shared-memory multiprocessor into a virtual cluster that supports fault containment and heterogeneity, while avoiding operating system scalability bottlenecks. Yet at the same time, Cellular Disco preserves the benefits of a shared-memory multiprocessor by implementing dynamic, fine-grained resource sharing, and by allowing users to overcommit resources such as processors and memory. This hybrid approach requires a scalable resource manager that makes local decisions with limited information while still providing good global performance and fault containment. In this paper we describe our experience with a Cellular Disco prototype on a 32-processor SGI Origin 2000 system. We show that the execution time penalty for this approach is low, typically within 10% of the best available commercial operating system for most workloads, and that it can manage the CPU and memory resources of the machine significantly better than the hardware partitioning approach.

Current shared-memory multiprocessors are inherently vulnerable to faults: any significant hardware or system software fault causes the entire system to fail. Unless provisions are made to limit the impact of faults, users will perceive a decrease in reliability when they entrust their applications to larger machines. This paper shows that fault containment techniques can be effectively applied to scalable shared-memory multiprocessors to reduce the reliability problems created by increased machine size.The primary goal of our approach is to leave normal-mode performance unaffected. Rather than using expensive faulttolerance techniques to mask the effects of data and resource loss, our strategy is based on limiting the damage caused by faults to only a portion of the machine. After a hardware fault, we run a distributed recovery algorithm that allows normal operation to be resumed in the functioning parts of the machine.Our approach is implemented in the Stanford FLASH multiprocessor. Using a detailed hardware simulator, we have performed a number of fault injection experiments on a FLASH system running Hive, an operating system designed to support fault containment. The results we report validate our approach and show that in conjunction with an operating system like Hive, we can improve the reliability seen by unmodified applications without substantial performance cost. Simulation results suggest that our algorithms scale well for systems up to 128 processors. NodeControlle (MAGIC) x Memory Node FIGURE 2.1. Overview of the FLASH multiprocessor. FLASH consists of a set of nodes connected through a point-topoint interconnect. Each node contains a portion of the distributed main memory and a node controller that handles cache coherence and other communication within the node and with other nodes.