In this paper, we propose a new paradigm for network file system design, serverless network file systems. While traditional network file systems rely on a central server machine, a serverless system utilizes workstations cooperating as peers to provide all file system services. Any machine in the system can store, cache, or control any block of data. Our approach uses this location independence, in combination with fast local area networks, to provide better performance and scalability than traditional file systems. Further, because any machine in the system can assume the responsibilities of a failed component, our serverless design also provides high availability via redundant data storage. To demonstrate our approach, we have implemented a prototype serverless network file system called xFS. Preliminary performance measurements suggest that our architecture achieves its goal of scalability. For instance, in a 32-node xFS system with 32 active clients, each client receives nearly as much read or write throughput as it would see if it were the only active client.

this paper we first discuss the wire metrics of interest and examine them in a contemporary 0.25m process. We then discuss technology scaling over the next several generations, from SIA and other predictions, and how our wire metrics trend over that time. We will examine the delay and bandwidth limitations of both long global wires and short local wires and discuss architectural design techniques that help us avoid the limitations of scaled wires.

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This paper introduces the C Region Library (CRL), a new all-software distributed shared memory (DSM) system. CRL requires no special compiler, hardware, or operating system support beyond the ability to send and receive messages. It provides a simple, portable shared address space programming model that is capable of delivering good performance on a wide range of multiprocessor and distributed system architectures. We have developed CRL implementations for two platforms: the CM-5, a commercial multicomputer, and the MIT Alewife machine, an experimental multiprocessor offering efficient support for both message passing and shared memory. We present results for up to 128 processors on the CM-5 and up to 32 processors on Alewife. In a set of controlled experiments, we demonstrate that CRL is the first all-software DSM system capable of delivering performance competitive with hardware DSMs. CRL achieves speedups within 30% of those provided by Alewife's native support for shared memory, eve...

In this paper 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 can run 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 non-uniform memory access time of these systems. To reduce the memory overheads associated with running multiple operating systems, we have developed techniques where the 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. 1

Alewife is a multiprocessor architecture that supports up to 512 processing nodes connected over a scalable and cost-effective mesh network at a constant cost per node. The MIT Alewife machine, a prototype implementation of the architecture, demonstrates that a parallel system can be both scalable and programmable. Four mechanisms combine to achieve these goals: software-extended coherent shared memory provides a global, linear address space; integrated message passing allows compiler and operating system designers to provide efficient communication and synchronization; support for fine-grain computation allows many processorsto cooperate on small problem sizes; and latency tolerance mechanisms -- including block multithreading and prefetching -- mask unavoidable delays due to communication; Microbenchmarks, together with over a dozen complete applications running on the 32-node prototype, help analyze the behavior of the system. Analysis shows that integrating message passing with sha...

This paper investigates the performance of shared virtual memory protocols on large-scale multicomputers. Using experiments on a 64-node Paragon, we show that the traditional Lazy Release Consistency (LRC) protocol does not scale well, because of the large number of messages it requires, the large amount of memory it consumes for protocol overhead data, and because of the diculty of garbage collecting that data. To achieve more scalable performance, we introduce and evaluate two new protocols. The rst, Home-based LRC (HLRC), is based on the Automatic Update Release Consistency (AURC) protocol. Like AURC, HLRC maintains a home for each page to which all updates are propagated and from which all copies are derived. Unlike AURC, HLRC requires no specialized hardware support. We nd that the use of homes provides substantial improvements in performance and scalability over LRC. Our second protocol, called Overlapped Home-based LRC (OHLRC), takes advantage of the communication processor found on each node of the Paragon to ooad some of the protocol overhead of HLRC from the critical path followed by the compute processor. We nd that OHLRC provides modest improvements over HLRC. We also apply overlapping to the base LRC protocol, with similar results. Our experiments were done using ve of the Splash-2 benchmarks. We report overall execution times, as well as detailed breakdowns of elapsed time, message trac, and memory use for each of the protocols. 1

... This paper identifies two challenges that machine simulators such as SimOS must overcome in order to effectively analyze large complex workloads: handling long workload execution times and collecting data effectively. To study long-running workloads, SimOS includes multiple interchangeable simulation models for each hardware component. By selecting the appropriate combination of simulation models, the user can explicitly control the tradeoff between simulation speed and simulation detail. To handle the large amount of low-level data generated by the hardware simulation models, SimOS contains flexible annotation and event classification mechanisms that map the data back to concepts meaningful to the user. SimOS has been extensively used to study new computer hardware designs, to analyze application performance, and to study operating systems. We include two case studies that demonstrate how a low-level machine simulator such as SimOS can be used to study large and complex workloads.

"STiNG" is a Cache Coherent Non-Uniform Memory Access (CC-NUMA) Multiprocessor designed and built by Sequent Computer Systems, Inc. It combines four processor Symmetric Multiprocessor (SMP) nodes (called Quads), using a Scalable Coherent Interface (SCI) based coherent interconnect. The Quads are based on the Intel P6 processor and the external bus it defines. In addition to 4 P6 processors, each Quad may contain up to 4 GBytes of system memory, 2 Peripheral Component Interface (PCI) busses for I/O, and a Lynx board. The Lynx board provides the datapath to the SCI-based interconnect and ensures systemwide cache coherency. STiNG is one of the first commercial CCNUMA systems to be built. This paper describes the motivation for building STiNG as well as its architecture and implementation. In addition, performance analysis is provided for On-Line Transaction Processing (OLTP) and Decision Support System (DSS) workloads. Finally, the status of the current implementation is reviewed. 1. Int...

Computer systems are rapidly changing. Over the next few years, we will see wide-scale deployment of dynamically-scheduled processors that can issue multiple instructions every clock cycle, execute instructions out of order, and overlap computation and cache misses. We also expect clock-rates to increase, caches to grow, and multiprocessors to replace uniprocessors. Using SimOS, a complete machine simulation environment, this paper explores the impact of the above architectural trends on operating system performance. We present results based on the execution of large and realistic workloads (program development, transaction processing, and engineering compute-server) running on the IRIX 5.3 operating system from Silicon Graphics Inc. Looking at uniprocessor trends, we find that disk I/O is the first-order bottleneck for workloads such as program development and transaction processing. Its importance continues to grow over time. Ignoring I/O, we find that the memory system is the key bottleneck, stalling the CPU for over 50 % of the execution time. Surprisingly, however, our results show that this stall fraction is unlikely to increase on future machines due to increased cache sizes and new latency hiding techniques in processors. We also find that the benefits of these architectural trends spread broadly across a majority of the important services provided by the operating system. We find the situation to be much worse for multiprocessors. Most operating systems services consume 30-70 % more time than their uniprocessor counterparts. A large fraction of the stalls are due to coherence misses caused by communication between processors. Because larger caches do not reduce coherence misses, the performance gap between uniprocessor and multiprocessor performance will increase unless operating system developers focus on kernel restructuring to reduce unnecessary communication. The paper presents a detailed decomposition of execution time (e.g., instruction execution time, memory stall time separately for instructions and data, synchronization time) for important kernel services in the three workloads.