Technical summary of the second IEEE workshop on workstation operating systems

Workstation Operating Systems: Proceedings of the Second Workshop on Workstation Operating Systems (WWOS-II) (Cat. No.89TH0281-6)

Proceedings of IEEE 4th Workshop on Workstation Operating Systems. WWOS-III

Proceedings of the Second Workshop on Workstation Operating Systems

IONIS: Approximate atomic photoionization intensities

Many smart healthcare centers are deploying long distance, high bandwidth networks in their computer network infrastructure and operation. Transmission control protocol (TCP) is responsible for reliable and secure communication of data in these medial infrastructure networks. TCP is reliable and secure due to its congestion control mechanism, which is responsible for detecting and reacting to the congestion in the network. Many TCP congestion control mechanisms have been developed previously for different operating systems. TCP CUBIC, TCP Compound, and TCP Fusion are the default congestion control mechanism in Linux, Microsoft Windows, and Sun Solaris operating systems, respectively. The earliest congestion control mechanism Standard TCP acts as the trademark congestion control mechanism. The exponential growth of congestion window ( $cwnd$ ) in slow start phase of the TCP CUBIC causes burst losses of packets, and TCP flows did not share available link bandwidth fairly. The prime aim of this paper is to enhance the performance of TCP CUBIC for long distance, high bandwidth secured networks to achieve better performance in medical infrastructure, concerning packet loss rate, protocol fairness, and convergence time. In this paper, congestion control module for slow start is proposed, which reduces the effect of the exponential growth of $cwnd$ by designing the new limits of $cwnd$ size in slow start phase, which in turn decreases the packet loss rate in healthcare networks. NS-2 is used to simulate the experiments of enhanced TCP CUBIC and state-of-the-art congestion control mechanisms. Results show that the performance of enhanced TCP CUBIC outperforms by 18% as compared with the state-of-the-art congestion control mechanisms.

BABAR is a high-rate experiment to study CP violation in asymmetric e{sup +}e{sup {minus}} collisions. The BABAR Online Farm is a pool of workstations responsible for the last layer of event selection, as well as for full reconstruction of selected events and for monitoring functions. A large number of machine architectures were evaluated for use in this Online Farm. We present an overview of the results of this evaluation, which include tests of low-level OS primitives, tests of memory architecture, and tests of application-specific CPU performance. Factors of general interest to others making hardware decisions are highlighted. Performance of current BABAR reconstruction (written in C++) is found to scale fairly well with SPECint95, but with some noticeable deviations. Even for machines with similar SPEC CPU ratings, large variations in memory system performance exist. No single operating system has an overall edge in the performance of its primitives. In particular, freeware operating systems perform no worse overall than the commercial offerings.

Non-volatile memory (NVM), such as PCM, STT-MRAM, and ReRAM, makes it possible to integrate secondary storage into main memory. This integration reduces I/O access times to typically slow block devices; however, it is unrealistic to construct a large capacity main memory with a single NVM at this time, because NVM have disadvantages regarding write access. Combining NVM and other memory devices is necessary to hide such disadvantages. In particular, we should place write-hot data on DRAM and write-cold data on NVM. For data placement, programming language runtime supports are useful, since they have more detailed information about write access than the operating system. A previous study proposed a method to manage NVM/DRAM hybrid memory with programming language runtime supports, determining data placement based on the number of write accesses to each object (i.e., the individual counting method); however, this approach has two problems, namely memory efficiency and determination of threshold values for data placements. The Most Recent Write (MRW) queue method is an alternative method to distinguish between write-hot data and write-cold data. MRW queue manages the frequency of write accesses to objects. In this study, we discuss the problems of the individual counting method and show solutions using the MRW queue approach. Results of our experimentation show that the MRW queue method improves memory efficiency and reduces overhead of the individual counting method.

Chapter 10 - Files

Datacenters are becoming one of the most significant worldwide consumers of electricity and sources of carbon emissions. With the end of Dennard's scaling, and as cloud datacenter power usage effectiveness becomes close to optimal, we can no longer rely on hardware advancements alone to sustainably meet growing computational needs. System software must play a bigger role in optimizing energy consumption of applications. We argue the operating system (OS), at the heart of datacenter resource allocation and scheduling decisions, must be made energy-aware. This paper has two goals. First, we show how Linux can be made energy-aware without making any kernel changes, by introducing a new energy accounting framework, Wattmeter. Wattmeter uses eBPF functions to efficiently measure per-process energy consumption at millisecond-scale granularity with low overhead. Second, we show how this information can be used to make energy-informed scheduling decisions, with two proof-of-concept scheduling policies: a policy that equalizes energy across processes, and one that caps the amount of energy that can be consumed by a process. This paper represents a first step in making the operating system energy-aware, and demonstrating how that capability can be used to control applications' energy consumption.

We present the design and implementation of a custom GPU-based compute cluster that provides the correlation X-engine of the CHIME Pathfinder radio telescope. It is among the largest such systems in operation, correlating 32,896 baselines (256 inputs) over 400MHz of radio bandwidth. Making heavy use of consumer-grade parts and a custom software stack, the system was developed at a small fraction of the cost of comparable installations. Unlike existing GPU backends, this system is built around OpenCL kernels running on consumer-level AMD GPUs, taking advantage of low-cost hardware and leveraging packed integer operations to double algorithmic efficiency. The system achieves the required 105TOPS in a 10kW power envelope, making it among the most power-efficient X-engines in use today.

Various blocking operations, such as input/output (IO) operations, are an essential functionality of concurrent applications. However, these operations often cause efficiency-related challenges in multithreaded applications because of the single-thread access policy. IO operations' speed and efficiency heavily rely on operating system (OS) threads and scheduling processes, which is time consuming because of the insufficient distribution of OS resources. Because of their overgeneralization, required to support various usage scenarios, robust OS kernel threads are commonly heavyweight and oversized, created in a limited number, and heavily dependent on OS schedulers. Various asynchronous programming techniques tackled issues in implementing modern concurrent applications with blocking operations with the complete reconstruction of threads and their schedulers. Based on our previous preliminary research, we further explored a model of structured concurrency in Java Virtual Machine (JVM), through the updated implementation of virtual threads and accompanying schedulers, as a novel form of lightweight user-mode threads in the JVM, decoupled from OS threads and managed within the JVM. In this paper, we present our findings about the efficiency of those schedulers utilized for virtual threads on blocking operations, compared with the implementation of OS threads' schedulers, and review possibilities of reducing memory footprints and context switching costs.

The rapid advancement in artificial intelligence, semiconductor and electronic technology, software engineering and programming techniques as well as hardware interfaces and communication has raised the demand for advancement in operating system. In this article we have studied the classical and current operating systems and have proposed the advanced components such as new user interfaces, advanced human-to-machine interfaces, communication, artificial intelligence and advanced hardware technology etc. which are necessary for next generation operating systems.

This research is centered on Apple, Inc., a prominent American-based multinational corporation renowned for its preeminent role in the design, manufacture, and distribution of advanced electronic devices alongside a diverse array of internet services. Established in 1976 by Steve Jobs and Steve Wozniak (Alex, 2015), Apple has risen to become the world’s largest company, boasting a market capitalization of $3 trillion and a listing on NASDAQ while occupying a significant position within the S&P 500 index (Bob, 2023). Apple’s influence on computing and mobile technology is unparalleled, exemplified by the widespread adoption of its macOS and iOS operating systems in personal computers and mobile devices, contributing to the global popularity of Apple products. Apple is a frontrunner in the computer sector, known for its advanced systems and hardware. MacOS is one of the three major computer operating systems, alongside Microsoft and Linux.

I.P. Sharp Associates has, for several years, been researching and developing systems for electronic publishing to meet needs internally as well as to provide the results to our customers, normally as part of a software system whose main function would be to do something other than text processing per se. We were seeking a system which had to:support a variety of input devices and output devices, from different vendors, and connected to different computers running under different operating systems, in different parts of the world---yet ensure that the output looked the same wherever it was produced. This is, incidentally, a non-trivial problem.

MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches