This blog post, titled "Why is my CPU usage always 100%? (Upgrading my Chumby 8 kernel part 9)", details the author's ongoing journey to upgrade the Linux kernel on their Chumby 8, a now-discontinued internet appliance. A persistent issue of 100% CPU utilization plagues the device after the kernel upgrade, prompting a deep dive into diagnosing the root cause.
Initially, the author suspects a runaway process is consuming all available CPU cycles. Using the top command, they identify the culprit as the kworker process, specifically a kernel thread dedicated to handling software interrupts. This discovery shifts the focus from a misbehaving user-space application to a problem within the kernel itself.
The author's investigation then explores various potential sources of excessive software interrupts. They meticulously eliminate possibilities such as network interrupts by disconnecting the device from the network, and timer interrupts by analyzing their frequency and confirming they are within expected parameters.
The post highlights the challenges of debugging kernel-level issues, especially on an embedded system with limited resources and debugging tools. The author leverages the available tools, including top, /proc/interrupts, and kernel debugging messages, to progressively narrow down the problem.
Through a process of elimination and careful observation, the author eventually identifies the excessive software interrupts as stemming from the SD card driver. The continuous stream of interrupts from the SD card controller overwhelms the system, leading to the observed 100% CPU usage. While the exact reason for the SD card driver's behavior remains unclear at the end of the post, the author pinpoints the source of the problem and sets the stage for further investigation in future installments. The post concludes by emphasizing the iterative nature of debugging and the importance of systematically eliminating potential causes.
This LWN article delves into a significant enhancement proposed for the Linux kernel's io_uring subsystem: the ability to directly create processes using a new operation type. Currently, io_uring excels at asynchronous I/O operations, allowing applications to submit batches of I/O requests without blocking. However, tasks requiring process creation, like launching a helper process to handle a specific part of a workload, necessitate a context switch back to the main kernel, disrupting the efficient asynchronous flow. This proposal aims to remedy this by introducing a dedicated IORING_OP_PROCESS operation.
The proposed mechanism allows applications to specify all necessary parameters for process creation within the io_uring submission queue entry (SQE). This includes details like the executable path, command-line arguments, environment variables, user and group IDs, and various other process attributes. Critically, this eliminates the need for a system call like fork() or execve(), thereby maintaining the asynchronous nature of the operation within the io_uring context. Upon completion, the kernel places the process ID (PID) of the newly created process in the completion queue entry (CQE), enabling the application to monitor and manage the spawned process.
The article highlights the intricate details of how this process creation within io_uring is implemented. It explains how the necessary data structures are populated within the kernel, how the new process is forked and executed within the context of the io_uring kernel threads, and how signal handling and other process-related intricacies are addressed. Specifically, the IORING_OP_PROCESS operation utilizes a dedicated structure called io_uring_process, embedded within the SQE, which mirrors the arguments of the traditional execveat() system call. This allows for a familiar and comprehensive interface for developers already accustomed to process creation in Linux.
Furthermore, the article discusses the security implications and design choices made to mitigate potential vulnerabilities. Given the asynchronous nature of io_uring, ensuring proper isolation and preventing unauthorized process creation are paramount. The article emphasizes how the proposal adheres to existing security mechanisms and leverages existing kernel infrastructure for process management, thereby minimizing the introduction of new security risks. This involves careful handling of file descriptor inheritance, namespace management, and other security-sensitive aspects of process creation.
Finally, the article touches upon the performance benefits of this proposed feature. By avoiding the context switch overhead associated with traditional process creation system calls, applications leveraging io_uring can achieve greater efficiency, particularly in scenarios involving frequent process spawning. This streamlines workflows involving parallel processing and asynchronous task execution, ultimately boosting overall system performance.
The Hacker News post titled "Process Creation in Io_uring" sparked a discussion with several insightful comments. Many commenters focused on the potential performance benefits and use cases of this new functionality.
One commenter highlighted the significance of io_uring evolving from asynchronous I/O to encompassing process creation, viewing it as a step towards a more unified and efficient system interface. They expressed excitement about the possibilities this opens up for streamlining complex operations.
Another commenter delved into the technical details, explaining how CLONE_PIDFD could be leveraged within io_uring to manage child processes more effectively. They pointed out the potential to avoid race conditions and simplify error handling compared to traditional methods. This commenter also discussed the benefits of integrating process management into the same asynchronous framework used for I/O.
The discussion also touched upon the security implications of using io_uring for process creation. One commenter raised concerns about the potential for vulnerabilities if this powerful functionality isn't implemented and used carefully. This concern spurred further discussion about the importance of proper sandboxing and security audits.
Several commenters expressed interest in using this feature for specific applications, such as containerization and serverless computing. They speculated on how the performance improvements could lead to more efficient and responsive systems.
A recurring theme throughout the comments was the innovative nature of io_uring and its potential to reshape system programming. Commenters praised the ongoing development and expressed anticipation for future advancements.
Finally, some commenters discussed the complexities of using io_uring and the need for better documentation and examples. They suggested that wider adoption would depend on making this powerful technology more accessible to developers.
The Mozilla Developer Network (MDN) web documentation article titled "Contain – CSS Cascading Style Sheets" elaborates on the contain CSS property, a powerful tool for optimizing website performance by isolating specific elements from the rest of the document. This isolation limits the browser's calculations for layout, style, and paint, which can significantly improve rendering speed, especially in complex web applications. The contain property achieves this by declaring that an element's subtree (its descendants) are independent and their changes won't affect the layout, style, paint, or size calculations of the rest of the page, or vice-versa.
The article details the various values the contain property can accept, each offering different levels of isolation:
strict: This value provides the strongest level of containment. It encapsulates the element completely, meaning changes within the element will not trigger layout, paint, style, or size recalculations outside of it, nor will external changes affect it. It essentially treats the element as an entirely separate document.
content: This value signifies that the element's contents are independent in terms of layout, style, and paint. Changes within the contained element won't affect the layout or styling of the rest of the document, and vice-versa. Size containment, however, is not implied.
size: This value indicates that the element's dimensions are fixed and known beforehand. This allows the browser to allocate space for the element without needing to examine its descendants, which can expedite layout calculations. Crucially, size containment requires the element to have a specified size (e.g., through properties like width and height). Otherwise, it defaults to a size of 0, potentially hiding the content. This value does not isolate style, layout, or paint.
layout: This isolates the element's layout. Changes in the element's internal layout won't affect the layout of the surrounding elements, and external layout changes won't affect the contained element's internal layout.
style: This prevents style changes within the contained element from leaking out and affecting the styling of the parent document, and likewise, external style changes won't influence the element's internal styling. This particularly applies to style inheritance and counter incrementing. Note: As of the documentation's current state, style containment is still experimental and may not be fully supported by all browsers.
paint: This value ensures that the element's painting is contained within its boundaries. Any painting done within the element won't overflow outside its box, and painting from other elements won't bleed into the contained element. This is particularly useful for elements with effects like shadows or filters, preventing them from overlapping adjacent content.
The article also clarifies that multiple values can be combined, separated by spaces, to provide a composite containment effect. For example, contain: layout paint would isolate both layout and paint. Using the keyword contain: none explicitly disables containment, ensuring no isolation is applied.
Finally, the MDN documentation highlights important considerations for using the contain property effectively. It emphasizes the need for careful planning when implementing containment, especially with the size value, due to its potential to inadvertently hide content if dimensions are not explicitly defined. Overall, the article positions the contain property as a valuable tool for web developers aiming to optimize rendering performance, but it stresses the importance of understanding its nuances to avoid unexpected behavior.
The Hacker News post titled "Contain – CSS Cascading Style Sheets – MDN" linking to the MDN documentation on the CSS contain property has a moderate number of comments discussing various aspects of the property and its usage.
Several commenters highlight the performance benefits of contain. One user emphasizes how crucial this property is for optimizing web performance, particularly in complex applications. They elaborate that contain allows developers to isolate specific parts of the DOM, thereby limiting the scope of reflows and repaints, leading to smoother interactions and faster rendering times. This sentiment is echoed by another comment which points out the significant impact contain can have on improving rendering performance, especially in situations with animations or transitions.
Another thread discusses the nuances of the different values of the contain property (like size, layout, style, and paint). One user questions the practical applications of style containment, leading to a discussion about scenarios where preventing style bleed from a component is beneficial, such as in shadow DOM implementations or when dealing with third-party embedded content. The utility of size containment is also highlighted, specifically for scenarios where the size of a component is known beforehand, enabling the browser to perform layout calculations more efficiently.
One commenter expresses surprise at not having known about this property sooner, suggesting that it's underutilized within the web development community. This comment sparks further discussion about the discoverability of useful CSS properties and the challenges developers face in keeping up with the evolving web standards.
A few comments dive into specific use cases for contain. One user mentions using it to isolate a complex animation, preventing performance issues from affecting the rest of the page. Another explains how contain can be instrumental in optimizing the performance of virtualized lists, where only visible items need to be rendered.
Finally, a commenter points to the MDN documentation itself as an excellent resource for understanding the intricacies of the contain property and its various values, underscoring the value of the original link shared in the Hacker News post. The commenter highlights the detailed explanations and examples provided in the documentation, which allows for a deeper understanding of its effects and proper implementation.
Eli Bendersky's blog post, "ML in Go with a Python Sidecar," explores a practical approach to integrating machine learning (ML) models, typically developed and trained in Python, into applications written in Go. Bendersky acknowledges the strengths of Go for building robust and performant backend systems while simultaneously recognizing Python's dominance in the ML ecosystem, particularly with libraries like TensorFlow, PyTorch, and scikit-learn. Instead of attempting to replicate the extensive ML capabilities of Python within Go, which could prove complex and less efficient, he advocates for a "sidecar" architecture.
This architecture involves running a separate Python process alongside the main Go application. The Go application interacts with the Python ML service through inter-process communication (IPC), specifically using gRPC. This allows the Go application to leverage the strengths of both languages: Go handles the core application logic, networking, and other backend tasks, while Python focuses solely on executing the ML model.
Bendersky meticulously details the implementation of this sidecar pattern. He provides comprehensive code examples demonstrating how to define the gRPC service in Protocol Buffers, implement the Python server utilizing TensorFlow to load and execute a pre-trained model, and create the corresponding Go client to communicate with the Python server. The example focuses on a simple image classification task, where the Go application sends an image to the Python sidecar, which then returns the predicted classification label.
The post highlights several advantages of this approach. Firstly, it enables clear separation of concerns. The Go and Python components remain independent, simplifying development, testing, and deployment. Secondly, it allows leveraging existing Python ML code and expertise without requiring extensive Go ML libraries. Thirdly, it provides flexibility for scaling the ML component independently from the main application. For example, the Python sidecar could be deployed on separate hardware optimized for ML tasks.
Bendersky also discusses the performance implications of this architecture, acknowledging the overhead introduced by IPC. He mentions potential optimizations, like batching requests to the Python sidecar to minimize communication overhead. He also suggests exploring alternative IPC mechanisms besides gRPC if performance becomes a critical bottleneck.
In summary, the blog post presents a pragmatic solution for incorporating ML models into Go applications by leveraging a Python sidecar. The provided code examples and detailed explanations offer a valuable starting point for developers seeking to implement a similar architecture in their own projects. While acknowledging the inherent performance trade-offs of IPC, the post emphasizes the significant benefits of this approach in terms of development simplicity, flexibility, and the ability to leverage the strengths of both Go and Python.
The Hacker News post titled "ML in Go with a Python Sidecar" (https://news.ycombinator.com/item?id=42108933) elicited a modest number of comments, generally focusing on the practicality and trade-offs of the proposed approach of using Python for machine learning tasks within a Go application.
One commenter highlighted the potential benefits of this approach, especially for computationally intensive ML tasks where Go's performance might be a bottleneck. They acknowledged the convenience and rich ecosystem of Python's ML libraries, suggesting that leveraging them while keeping the core application logic in Go could be a sensible compromise. This allows for utilizing the strengths of both languages: Go for its performance and concurrency in handling application logic, and Python for its mature ML ecosystem.
Another commenter questioned the performance implications of the inter-process communication between Go and the Python sidecar, particularly for real-time applications. They raised concerns about the overhead introduced by serialization and deserialization of data being passed between the two processes. This raises the question of whether the benefits of using Python for ML outweigh the performance cost of this communication overhead.
One comment suggested exploring alternatives like using shared memory for communication between Go and Python, as a potential way to mitigate the performance overhead mentioned earlier. This alternative approach aims to optimize the data exchange by avoiding the serialization/deserialization steps, leading to potentially faster processing.
A further comment expanded on the shared memory idea, specifically mentioning Apache Arrow as a suitable technology for this purpose. They argued that Apache Arrow’s columnar data format could further enhance the performance and efficiency of data exchange between the Go and Python processes, specifically highlighting zero-copy reads for improved efficiency.
The discussion also touched upon the complexity introduced by managing two separate processes and the potential challenges in debugging and deployment. One commenter briefly discussed potential deployment complexities with two processes and debugging. This contributes to a more holistic view of the proposed architecture, considering not only its performance characteristics but also the operational aspects.
Another commenter pointed out the maturity and performance improvements in Go's own machine learning libraries, suggesting they might be a viable alternative in some cases, obviating the need for a Python sidecar altogether. This introduces the consideration of whether the proposed approach is necessary in all scenarios, or if native Go libraries are sufficient for certain ML tasks.
Finally, one commenter shared an anecdotal experience, confirming the practicality of the Python sidecar approach. They mentioned successfully using a similar setup in production, lending credibility to the article's proposal. This real-world example provides some validation for the discussed approach and suggests it's not just a theoretical concept but a practical solution.
Summary of Comments ( 74 )
https://news.ycombinator.com/item?id=42649862
The Hacker News comments primarily focus on the surprising complexity and challenges involved in the author's quest to upgrade the kernel of a Chumby 8. Several commenters expressed admiration for the author's deep dive into the embedded system's inner workings, with some jokingly comparing it to a software archaeological expedition. There's also discussion about the prevalence of inefficient browser implementations on embedded devices, contributing to high CPU usage. Some suggest alternative approaches, like using a lightweight browser or a different operating system entirely. A few commenters shared their own experiences with similar embedded devices and the difficulties in optimizing their performance. The overall sentiment reflects appreciation for the author's detailed troubleshooting process and the interesting technical insights it provides.
The Hacker News post discussing the blog post "Why is my CPU usage always 100%? Upgrading my Chumby 8 kernel (Part 9)" has several comments exploring various aspects of the situation and offering potential solutions.
One commenter points out the inherent difficulty in debugging such embedded systems, highlighting the lack of sophisticated tools and the often obscure nature of the problems. They sympathize with the author's struggle, acknowledging the frustration that can arise when dealing with limited resources and cryptic error messages.
Another commenter questions the author's decision to stick with the older kernel (2.6.32), suggesting that moving to a more modern kernel might be a more efficient approach in the long run. They acknowledge the author's stated reasons for remaining with the older kernel (familiarity and control) but argue that the benefits of a newer kernel, including potential performance improvements and bug fixes, might outweigh the effort involved in upgrading.
A third commenter focuses on the specific issue of the
kworkerprocess consuming high CPU. They suggest investigating whether a driver is misbehaving or if some background process is stuck in a loop. They propose using tools likestraceorperfto pinpoint the culprit and gain a better understanding of the kernel's behavior. This commenter also mentions the possibility of a hardware issue, although they consider it less likely.Further discussion revolves around the challenges of real-time systems and the potential impact of interrupt handling on CPU usage. One commenter suggests examining interrupt frequencies and considering the possibility of interrupt coalescing to reduce overhead.
Finally, there's a brief exchange about the Chumby device itself, with one commenter expressing nostalgia for the device and another sharing their own experience with embedded systems development. This adds a touch of personal reflection to the technical discussion.
Overall, the comments provide a valuable extension to the blog post, offering diverse perspectives on debugging embedded systems, troubleshooting high CPU usage, and the specific challenges posed by the Chumby 8 and its older kernel. The commenters offer practical suggestions and insights drawn from their own experiences, creating a collaborative problem-solving environment.