Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2111.15240

Verifying and Optimizing Compact NUMA-Aware Locks on Weak Memory Models

Antonio Paolillo·Hernán Ponce-de-León·Thomas Haas·Diogo Behrens·Rafael Chehab·Ming Fu·Roland Meyer
View original source ↗Read source document ↗Export metadata ↓

Abstract

Developing concurrent software is challenging, especially if it has to run on modern architectures with Weak Memory Models (WMMs) such as ARMv8, Power, or RISC-V. For the sake of performance, WMMs allow hardware and compilers to aggressively reorder memory accesses. To guarantee correctness, developers have to carefully place memory barriers in the code to enforce ordering among critical memory operations. While WMM architectures are growing in popularity, identifying the necessary and sufficient barriers of complex synchronization primitives is notoriously difficult. Unfortunately, publications often consider barriers to be just implementation details and omit them. In this technical note, we report our efforts in verifying the correctness of the Compact NUMA-Aware (CNA) lock algorithm on WMMs. The CNA lock is of special interest because it has been proposed as a new slowpath for Linux qspinlock, the main spinlock in Linux. Besides determining a correct and efficient set of barriers for the original CNA algorithm on WMMs, we investigate the correctness of Linux qspinlock and the latest Linux CNA patch (v15) on the memory models LKMM, ARMv8, and Power. Surprisingly, we have found that Linux qspinlock and, consequently, Linux CNA are incorrect according to LKMM, but are still correct when compiled to ARMv8 or Power.

Explore related subjects

cs.OS

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Antonio Paolillo, Hernán Ponce-de-León, Thomas Haas, Diogo Behrens, Rafael Chehab, Ming Fu, Roland Meyer. 2022-07-09. Verifying and Optimizing Compact NUMA-Aware Locks on Weak Memory Models. https://arxiv.org/abs/2111.15240

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Don't Let AI Agents YOLO Your Files: Information and Control in Agent-Native Filesystems

AI coding agents regularly misuse their filesystem access, causing data corruption, loss, and leakage. We conduct the first systematic study of this problem through an analysis of 290 public reports. Our study reveals two fundamental gaps: users and agents have limited information about filesystem effects and insufficient control over them. To close these gaps, we propose to shift information and control from agents to filesystems. We introduce agent-native filesystems and identify three primitives they should provide: introspect effects, undo mutations, and gate accesses. These primitives let agents operate autonomously while reserving user interaction for sensitive accesses and final review. We build YoloFS, an agent-native filesystem. YoloFS stages mutations until the user commits them, snapshots intermediate states for agent self-correction, and uses progressive permission to let users adapt access rules during execution. We evaluate YoloFS with a new methodology that captures interactions among the user, agent, and filesystem. On 11 tasks with hidden side effects, YoloFS enables agents to self-correct in 8 and stages all mutations for user review. On 112 routine tasks, YoloFS reduces user interaction while matching the baseline success rate. YoloFS is open-sourced at https://github.com/YoloFS/YoloFS.

cs.OS↗
arXiv2026

Netkit: Specializing Linux Packet Delivery for Container Networks

Cloud-native microservices architectures rely on network namespaces for isolation, with the overhead of container communications remaining a critical performance bottleneck. While colocating containers on the same host mitigates some of this overhead, it cannot match the performance of communication within a single network namespace. Existing solutions either require application rewrites or fail to support the full Linux network stack expected by containerized applications. In this paper, we present netkit, an eBPF-based datapath that specializes the Linux networking stack to eliminate redundant backlog queue traversals during network namespace transitions. netkit leverages eBPF to transparently redirect packets between namespaces, bypassing unnecessary buffering while preserving compatibility with existing container applications. Our implementation in the Linux kernel, integrated with minimal changes to the Cilium network plugin for Kubernetes, improves throughput by up to 37\% and achieves parity between container-to-container and process-to-process communications, effectively closing the performance gap introduced by namespace isolation.

cs.OS↗
arXiv2026

Grouper: Scheduling Groups for Multi-Tenant Microsecond-Scale Microservices

Microsecond-scale core allocation makes colocating latency-critical services with batch work worthwhile. A thread that finds no work parks within microseconds and its core goes to a batch task. Putting one back costs $\sim$18 $μ$s, as the allocator must discover that a core is wanted and then take it from the batch task holding it. A monolith pays that tax once per request, a microservice chain pays it at every hop in both directions, and a multi-tenant host multiplies it again, because every tenant's hops queue at the same allocator. On our port of DeathStarBench's hotelReservation, going from two tenants to ten takes a hop from 39 to 222 $μ$s and a 10-RPC path's median from 456 to 2,445 $μ$s, a fivefold degradation even though no tenant's own load changed. We introduce Grouper and the scheduling group, a set of isolated runtimes that the allocator treats as one allocation and accounting unit, whose members may hand cores directly to one another. A service sending an RPC donates its core to the peer through an unprivileged kernel fast path, so the core follows the request through the call graph. The allocator retains control through reconciliation, core-addressed revocation and a pooled budget but leaves the critical path; its load falls from $Θ(R \cdot H)$ to $Θ(R)$ in request rate $R$ and hop count $H$. Over a grid of two to ten tenants at 1,000-30,000 requests per second each, Grouper outperforms Caladan (the allocator Junction also builds on) and Linux by up to 7.9$\times$ and 3.4$\times$ at the median and 4.1$\times$ and 14.2$\times$ at the tail, and leaves batch work more throughput than Caladan at over 70% of load points.

cs.OS↗