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Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems最新文献

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Capybara: μSecond-Scale Live TCP Migration Capybara: μSecond-Scale Live TCP Migration
Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609813
I. Choi, Nimish Wadekar, Raj Joshi, Joshua Fried, Dan R. K. Ports, Irene Zhang, Jialin Li
Latency-critical μs-scale data center applications are susceptible to server load spikes. The issue is particularly challenging for services using long-lived TCP connections. This paper introduces Capybara, a highly efficient and versatile live TCP migration system. Capybara builds atop a deterministic, kernel-bypassed TCP stack running in a library OS to realize its μs-scale TCP migration mechanism. Using modern programmable switches, Capybara implements migration-aware dynamic packet forwarding and transient packet buffering, further reducing system interference during live TCP migration. Capybara can transparently migrate a running TCP connection in 4 μs on average. It improves the average migration host latency by about 12 times compared to a Linux kernel-based solution.
延迟关键型μs规模的数据中心应用程序容易受到服务器负载峰值的影响。对于使用长期TCP连接的服务来说,这个问题尤其具有挑战性。介绍了一种高效、通用的实时TCP迁移系统Capybara。Capybara构建在一个确定性的、绕过内核的TCP堆栈之上,运行在一个库操作系统中,以实现其μs级的TCP迁移机制。使用现代可编程交换机,Capybara实现了迁移感知的动态数据包转发和瞬态数据包缓冲,进一步减少了TCP实时迁移期间的系统干扰。Capybara平均可以在4 μs内透明地迁移一个正在运行的TCP连接。与基于Linux内核的解决方案相比,它将平均迁移主机延迟提高了大约12倍。
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引用次数: 3
xOS: The End Of The Process-Thread Duo Reign xOS:进程-线程二人统治的终结
Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609817
A. Tchana, Dorian Goepp, Stella Bitchebe, Renaud Lachaize
Process and Thread are first-order abstractions of the operating system (OS), whose implementation is wired into the OS core. Several research works have shown the inadequacy of these two main abstractions for modern isolation needs, leading to the introduction of additional abstractions with new isolation and communication features. Despite their usefulness, these new proposals are introduced in a somewhat ad-hoc manner, compromising their broad and consensual adoption. This position paper presents xOS, an OS design that does not introduce yet another first-class isolation abstraction but instead investigates how the OS can help application programmers, libraries, and OS developers integrate and easily use new abstractions. To our knowledge, xOS is the first work in this area. Similar to file system development built around a Virtual File System (VFS), xOS introduces the concept of Isolation Context (IC), which should be the unique first-class abstraction wired into the OS core. ICs can be realized in several pluggable Isolation Context Factories (ICFs) such as ProcessFactory (provides processes), Thread-Factory (provides threads), Docker Engine (provides Docker containers), KVM (provides KVM virtual machines), Wasp (provides virtines), etc. We discuss our plan to redesign a general-purpose OS from these foundations, the required APIs, and how to support new and legacy applications.
进程和线程是操作系统(OS)的一阶抽象,它们的实现被连接到OS核心中。一些研究工作表明,这两种主要抽象不足以满足现代隔离需求,因此引入了具有新隔离和通信功能的其他抽象。尽管这些新建议很有用,但它们是以某种临时方式提出的,损害了它们的广泛和协商一致通过。本文介绍了xOS,这是一种没有引入另一种一流隔离抽象的操作系统设计,而是研究了该操作系统如何帮助应用程序程序员、库和操作系统开发人员集成并轻松使用新的抽象。据我们所知,xOS是这一领域的第一个作品。与围绕虚拟文件系统(VFS)构建的文件系统开发类似,xOS引入了隔离上下文(IC)的概念,它应该是连接到OS核心的唯一的一等抽象。ic可以在几个可插拔的隔离上下文工厂(icf)中实现,如ProcessFactory(提供进程)、Thread-Factory(提供线程)、Docker Engine(提供Docker容器)、KVM(提供KVM虚拟机)、Wasp(提供虚拟机)等。我们讨论了在这些基础上重新设计通用操作系统的计划、所需的api,以及如何支持新的和遗留的应用程序。
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引用次数: 0
Improving Throughput-oriented Generative Inference with CPUs 利用cpu改进面向吞吐量的生成推理
Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609815
Daon Park, Sungbin Jo, Bernhard Egger
Despite recent attempts to reduce the number of parameters of large language models (LLMs), their parameter data is still too large to fit into a single GPU. With the emergence of throughput-oriented tasks, high-throughput generative inference frameworks for LLMs on a single commodity GPU leverage GPU, DRAM, and NVMe to run inference on large models with terabytes of data. Our analysis of the technique shows that the runtime is dominated by data transfers of the weights, leading to a low utilization of both the GPU and the CPU. In this paper, we increase the throughput and decrease the total latency of state-of-the-art frameworks by including the CPU as a compute device and overlapping computations on the CPU with GPU data transfers. Our work shows a promising improvement of around 40% in throughput and total latency, with potential room for further improvements.
尽管最近尝试减少大型语言模型(llm)的参数数量,但它们的参数数据仍然太大,无法容纳单个GPU。随着面向吞吐量的任务的出现,llm的高吞吐量生成推理框架在单个商品GPU上利用GPU、DRAM和NVMe在具有tb级数据的大型模型上运行推理。我们对该技术的分析表明,运行时由权重的数据传输主导,导致GPU和CPU的利用率都很低。在本文中,我们通过将CPU作为计算设备并将CPU上的计算与GPU数据传输重叠来增加最先进框架的吞吐量并降低总延迟。我们的工作表明,吞吐量和总延迟有希望提高约40%,还有进一步改进的潜力。
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引用次数: 0
Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems 第14届ACM SIGOPS亚太系统研讨会论文集
Pub Date : 1900-01-01 DOI: 10.1145/3609510
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引用次数: 0
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Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems
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