How to Compile Linux kernel

There were 22 Billion internet connected devices in the world at the end of 2018. Therefore much more total computing devices including the ones that are not connected. Plus, by 2024 the number must be substantially higher. These devices run various programs written in different programming languages. Some run boring mainframe code, while others run trendy AI and ML models. Something really really fundamental to all these devices is that they run an OS – an Operating System. Majority of them run Linux. Lets go back to basics today and do something really fundamental. Let’s learn how to compile Linux kernel.

Step 1: Install Dependencies Before compiling the kernel, you’ll need to install some dependencies. These may include development tools, compilers, and libraries. The required packages vary depending on your distribution. For example, on Debian-based systems, you can install the necessary packages with the following command:

sudo apt-get install build-essential libncurses-dev bison flex libssl-dev libelf-dev

Step 2: Download the Kernel Source Code You can download the kernel source code from the official Linux kernel website (https://www.kernel.org/). Choose a long term supported version e.g. linux-6.6.24.tar.xz and download the corresponding tarball.

Step 3: Extract the Source Code Navigate to the directory where you downloaded the tarball and extract it using the following command:

tar xvf linux-6.6.24.tar.xz

Step 4: Configure the Kernel Change into the kernel source directory:

cd linux-6.6.24
Steps to compile Linux kernel
Steps to compile Linux kernel

Run the following command to start the kernel configuration:

make menuconfig

This command opens a text-based menu where you can configure various kernel options. You can navigate through the menu using the arrow keys and select options using the spacebar. Once you’re done configuring, save your changes and exit the menu.

Compile the Kernel Once you’ve configured the kernel, you’re ready to compile it. Run the following commands:

make -j$(nproc)

This command starts the compilation process. The “-j$(nproc)” option tells make to use as many parallel processes as there are CPU cores, which can speed up the compilation process significantly.

Install the Kernel Modules After the compilation is complete, you can install the kernel modules using the following command:

sudo make modules_install

Install the Kernel To install the newly compiled kernel, run the following command:

sudo make install

This command installs the kernel image, kernel modules, and other necessary files.

Step 5: Update Boot Loader Configuration Finally, you need to update your boot loader configuration to include the new kernel. The procedure for doing this varies depending on your boot loader (e.g., GRUB, LILO).

Reboot Once you’ve updated the boot loader configuration, reboot your system to boot into the newly compiled kernel.

That’s it! You’ve successfully compiled and installed the Linux kernel.

Rust Programming Language learning roadmap

Rust is a multi-paradigm, general-purpose programming language exploding in popularity. But what makes it special? Rust offers a unique blend of blazing speed, unparalleled memory safety, and powerful abstractions, making it ideal for building high-performance, reliable systems. This blog delves into the Rust Programming Language learning roadmap

Why Embrace Rust?

  • Unmatched Performance: Rust eliminates the need for a garbage collector, resulting in lightning-fast execution and minimal memory overhead. This makes it perfect for resource-constrained environments and applications demanding real-time responsiveness.
  • Rock-Solid Memory Safety: Rust enforces memory safety at compile time through its ownership system. This eliminates entire classes of memory-related bugs like dangling pointers and use-after-free errors, leading to more stable and secure software.
  • Zero-Cost Abstractions: Unlike some languages where abstractions incur performance penalties, Rust achieves powerful abstractions without sacrificing speed. This allows you to write expressive, concise code while maintaining peak performance.

Language Fundamentals: Understanding the Building Blocks

Syntax and Semantics: Rust borrows inspiration from C-like languages in its syntax, making it familiar to programmers from that background. However, Rust’s semantics are distinct, emphasizing memory safety through ownership and immutability by default.

Constructs and Data Structures: Rust offers a rich set of control flow constructs like if, else, loop, and while for building program logic. Data structures encompass primitive types like integers, booleans, and floating-point numbers, along with powerful composite types like arrays, vectors, structs, and enums.

Ownership System: The Heart of Rust

The ownership system is the cornerstone of Rust’s memory safety. Let’s delve deeper:

  • Ownership Rules: Every value in Rust has a single owner – the variable that binds it. When the variable goes out of scope, the value is automatically dropped, freeing the associated memory. This ensures memory is never left dangling or leaked.
  • Borrowing: Borrowing allows temporary access to a value without taking ownership. References (&) and mutable references (&mut) are used for borrowing. The borrow checker, a powerful Rust feature, enforces strict rules to prevent data races and ensure references always point to valid data.
  • Stack vs. Heap: Understanding these memory regions is crucial in Rust. The stack is a fixed-size memory area used for local variables and function calls. It’s fast but short-lived. The heap is a dynamically allocated memory region for larger data structures. Ownership dictates where data resides: stack for small, short-lived data, and heap for larger, long-lived data.

Rust programming language learning roadmap

Beyond the Basics: Advanced Features

  • Error Handling: Rust adopts an Result type for error handling. It represents either successful computation with a value or an error with an error code. This promotes explicit error handling, leading to more robust code.
  • Modules and Crates: Rust promotes code organization through modules and crates. Modules group related code within a source file, while crates are reusable libraries published on https://crates.io/.
  • Concurrency and Parallelism: Rust provides mechanisms for writing concurrent and parallel programs. Channels and mutexes enable safe communication and synchronization between threads, allowing efficient utilization of multi-core processors.
  • Traits and Generics: Traits define shared behaviors for different types, promoting code reusability. Generics allow writing functions and data structures that work with various types, enhancing code flexibility.
  • Lifetimes and Borrow Checker: Lifetimes specify the lifetime of references in Rust. The borrow checker enforces rules ensuring references are valid for their intended usage duration. This prevents data races and memory unsafety issues.

Rust’s Reach: Applications Across Domains

  • Web Development: Frameworks like Rocket and Actix utilize Rust’s speed and safety for building high-performance web services and APIs.
  • Asynchronous Programming: Async/await syntax allows writing non-blocking, concurrent code, making Rust perfect for building scalable network applications.
  • Networking: Libraries like Tokio provide efficient tools for building networking applications requiring low latency and high throughput.
  • Serialization and Deserialization: Rust’s data structures map well to various data formats like JSON and CBOR, making it suitable for data exchange tasks.
  • Databases: Several database libraries like Diesel offer safe and performant database access from Rust applications.
  • Cryptography: Rust’s strong typing and memory safety make it ideal for building secure cryptographic systems.
  • Game Development: Game engines like Amethyst leverage Rust’s performance and safety for creating high-fidelity games.
  • Embedded Systems: Rust’s resource-efficiency and deterministic memory management make it a compelling choice for resource-constrained embedded systems.

Image Credit : roadmap.sh

My car has a digital twin !

We all have heard about people having a twin. But what if I told you that my car has a digital twin ! Let us understand Embodied AI in Autonomous driving and car’s digital twin.

Embodied AI is at the forefront of transforming the landscape of autonomous and self-driving cars, paving the way for safer roads and enhanced transportation systems. But what exactly is Embodied AI, and how does it revolutionize the realm of autonomous driving?

Embodied AI refers to the integration of artificial intelligence within physical systems, enabling them to perceive, interpret, and interact with the surrounding environment in real-time. In the context of autonomous vehicles, it entails equipping cars with sophisticated sensors, actuators, and intelligent algorithms to navigate roads autonomously while ensuring safety and efficiency.

Now, let’s delve into the fascinating realm of digitization or digital twin technology and its pivotal role in advancing autonomous driving:

πŸ” Digitization and Digital Twin of Cars:

Digitization involves creating a virtual representation of physical objects or systems. In the case of cars, this entails developing a digital twinβ€”a highly detailed, dynamic model that mirrors the behavior, characteristics, and functionality of its real-world counterpart. By continuously syncing data between the physical vehicle and its digital twin, automakers and AI engineers can:

Image Credit : NXP

  1. Enhance Training and Testing: Digital twins serve as invaluable tools for training AI algorithms and conducting extensive simulations in a safe, controlled environment. This enables developers to expose autonomous systems to a myriad of complex scenarios, including rare edge cases and adverse weather conditions, which are crucial for refining their decision-making capabilities.
  2. Iterative Development: Through iterative refinement and optimization, digital twins facilitate the rapid prototyping and iteration of autonomous driving systems. Engineers can simulate various design modifications and algorithmic enhancements, accelerating the development cycle and reducing time-to-market.
  3. Predictive Maintenance: By leveraging real-time sensor data and predictive analytics, digital twins enable proactive maintenance and diagnostics, thereby minimizing downtime and optimizing the operational efficiency of autonomous fleets.

πŸ›£οΈ Predicting and Comparing On-Road Performance through Off-Road Simulation:

One of the greatest challenges in autonomous driving lies in accurately predicting and comparing on-road performance across different driving conditions. Here’s how off-road simulation powered by digital twins addresses this challenge:

  1. Scenario Generation: Off-road simulation platforms leverage digital twins to generate diverse and realistic driving scenarios, encompassing a wide spectrum of environmental factors, traffic conditions, and pedestrian behaviors. By meticulously crafting these scenarios, developers can assess the robustness and adaptability of autonomous systems under various challenging conditions.
  2. Performance Benchmarking: Through off-road simulation, developers can systematically benchmark the performance of different autonomous driving algorithms and sensor configurations. By quantitatively evaluating metrics such as safety, efficiency, and comfort across diverse scenarios, stakeholders can make informed decisions regarding technology integration and deployment strategies.
  3. Continuous Learning and Improvement: Off-road simulation serves as a continuous learning loop, wherein insights gleaned from simulated scenarios inform the iterative refinement of AI algorithms and sensor fusion techniques. By iteratively exposing autonomous systems to increasingly complex and diverse challenges, developers can enhance their resilience and reliability over time.

In conclusion, Embodied AI, coupled with digitization and off-road simulation, heralds a new era of innovation in autonomous driving, promising safer roads, enhanced mobility, and unprecedented levels of efficiency. As we continue to push the boundaries of technological advancement, let us harness the power of AI to shape a future where transportation is not just autonomous but truly intelligent. πŸŒπŸš€ #AutonomousDriving #EmbodiedAI #DigitalTwin #Innovation #FutureofMobility

Traffic simulation

Image Credit : Zhou, Zewei et al. β€œA comprehensive study of speed prediction in transportation system: From vehicle to traffic.” iScience 25 (2022): n. pag.

License info about article containing above image : https://creativecommons.org/licenses/by/4.0/