Open Source Summit North America 2025

Beyond the hype, AI is already impacting Linux kernel engineering workflows. This talk presents concrete examples from real-world applications in Linux kernel LTS maintenance and CVE assignment processes. We'll examine where AI tools have meaningfully improved development processes and where they fall short.
Drawing from hands-on experience, we'll demonstrate how AI assists in analyzing patches for LTS kernel trees and streamlines vulnerability classification workflows. We'll share specific metrics showing both successes and limitations, focusing on practical applications rather than theoretical possibilities. We'll also explore emerging opportunities where AI could enhance kernel development while maintaining high engineering standards.
This technical session provides kernel developers, maintainers, and engineering leaders with actionable insights for integrating AI tools into their workflows while preserving the rigor of kernel development practices.

Graphics Processing Units (GPUs) have become key to modern computing, enabling high-performance parallel processing beyond their original role in gaming. Today, they are critical for solving complex computational challenges effectively. To achieve high levels of parallelism, OpenCL is commonly used to break tasks into kernels, which are executed by multiple threads. Optimizing OpenCL performance on GPUs remains a persistent challenge, as it involves fine-tuning both software (OpenCL code, kernels) and hardware. One of the critical factors influencing GPU performance is the effective use of workgroups. The size of workgroups significantly impacts parallelism and efficiency.
This session focuses on key software optimizations in OpenCL, such as efficient memory management, kernel fusion and optimal workgroup sizing in improving GPU performance. We will demonstrate, how optimized OpenCL code can significantly enhance parallel execution and efficiency. This talk also addresses challenges faced by automotive GPUs, including power and thermal constraints with strategies to overcome these. Best practices for writing efficient OpenCL code tailored for automotive GPUs will also be outlined.

This presentation explores how to create a multi-threaded version of Valkey by employing userspace Read-Copy-Update (RCU) to achieve high performance. With fewer than 2,000 lines of code modifications, we introduce a coordinator-worker pattern, enabling key-value stores like Valkey and Redis to handle tasks concurrently across multiple threads while using a per-thread event loop for I/O operations. Userspace RCU facilitates lock-free data sharing between a writer thread and multiple reader threads, dramatically improving read performance.

Our experiments show that the multi-threaded Valkey can achieve over one million operations per second on a standard server.

Are you drowning in a sea of vulnerability advisories, wondering why patching one thing doesn't fix everything? Despite a shared origin, a CVE's impact diverges significantly across Linux distributions. Consequently, a fix at the source does not automatically translate to comprehensive protection downstream. Each distribution requires independent patching, leading to a complex web of security advisories stemming from a single flaw.

In this talk, you'll learn how OSV tools can help you navigate this sea of advisories. We'll explore the root causes of advisory proliferation in Linux and demonstrate how OSV.dev aggregates and cross-references vulnerability data at scale to provide a more complete picture. You'll also see how OSV-Scanner accurately identifies vulnerabilities in your Linux systems, considering distribution-specific nuances and offering actionable guidance. By the end of this session, you'll be equipped with the knowledge and tools to patch smarter and secure your Linux infrastructure more effectively.

Understand how all binaries interact with the linux kernel, how the linux kernel sees userspace code, how it expects it to behave, and how you, an author of such programs, should in response interact with it. intentrace is a rewrite of strace in Rust. currently in Beta.

For long time kexec was a faster way to reboot a machine without incurring delays caused by firmware and bootloaders. However for many applications even a kexec reboot still means significant service degradation, like disruption of the running guests in virtualized environments and the need to rebuild in-memory caches for large databases.

We propose Kexec HandOver (KHO) mechanism that allows serialization and deserialization of kernel data as well as preserving arbitrary memory ranges across kexec.

In addition, KHO keeps physically contiguous memory regions that are guaranteed to not have any memory that KHO would preserve, but still can be used by the system. The kexeced kernel bootstraps itself using those regions and marks all handed over memory as in use. KHO users then can recover their state from the preserved data. This includes memory reservations, where the user can either discard or claim reservations.

As eBPF continues to revolutionize Linux observability and networking, the complexity of its program loading mechanism has evolved significantly.

This technical deep dive unravels the sophisticated machinery behind eBPF program loading, exploring the intricate interplay between userspace loader and Linux kernel verifier. We'll dissect the eBPF program relocation mechanisms, examine the role of BTF (BPF Type Format) in enabling strong typing and verification capabilities, and analyze the complex choreography of bpf() syscalls that bridge userspace and kernel operations. Finally, we will also discuss the security implications and program signing challenges in the loading pipeline.

This talk presents a method to enhance CPU scheduling by leveraging machine learning (ML) to extract the key features necessary for task migration, allowing for dynamic and stable optimization of workloads in scenarios prone to CPU imbalance. The approach is built on the sched_ext framework, which integrates eBPF to support user-defined scheduling policies within the Linux kernel.

While conventional approaches maximize CPU utilization, they often overlook contention for lower-level hardware resources, leading to performance bottlenecks -- particularly in compute-intensive servers. By using an ML-based, resource-aware load balancer, this method effectively addresses such imbalances. With sched_ext, we can collect training data and run inference on the model without modifying the kernel.

Our experiments demonstrate that, for certain workloads, this ML-driven approach can outperform EEVDF, offering notable performance gains for the CPU scheduler.