Performance

Introduction Shared hosting has been the entry point for millions of websites since the early days of the World Wide Web. It offers an affordable, low‑maintenance solution for individuals, small businesses, and hobbyists who need a reliable place to publish content without the overhead of managing a full server stack. Yet, despite its popularity, many developers and site owners treat shared hosting as a “black box” and miss out on optimizations, security best practices, and cost‑saving opportunities that are possible even within the constraints of a multi‑tenant environment. ...

Introduction Edge computing is no longer a niche experiment; it has become a cornerstone of modern cloud architectures, IoT platforms, and latency‑sensitive applications such as augmented reality, autonomous vehicles, and real‑time analytics. By moving computation closer to the data source, edge nodes reduce round‑trip latency, offload central clouds, and enable operation under intermittent connectivity. However, distributing workloads across thousands of heterogeneous edge devices introduces a new set of challenges: Resilience – nodes can be added, removed, or fail without warning. Performance – each node may have limited CPU, memory, and power budgets. Portability – software must run on a wide variety of hardware architectures (x86, ARM, RISC‑V) and operating systems (Linux, custom OSes, even bare‑metal). Security – the edge surface is larger, making isolation and attack mitigation critical. Two technologies have emerged as natural allies in this space: ...

Introduction The web has come a long way from static HTML pages to rich, interactive applications that rival native desktop software. Two emerging technologies are at the heart of this transformation: WebAssembly (Wasm) – a low‑level binary format that brings near‑native performance to the browser while preserving safety and portability. WebGPU – the next‑generation graphics and compute API for the web, offering explicit, high‑performance access to modern GPUs. Individually, each technology is powerful. Together, they form a compelling stack for building high‑performance graphics, simulations, and compute‑heavy workloads that run directly in the browser without plug‑ins. This article provides an in‑depth look at how WebAssembly and WebGPU complement each other, walks through a complete example from Rust source to a running WebGPU demo, and discusses best practices, tooling, and real‑world use cases. ...

Introduction If you have ever peered into a running Linux system with tools like top, htop, or ps, you might have noticed a set of processes named kworker/*. These processes are not user‑space daemons; they are kernel threads that drive the workqueue subsystem, a core mechanism that lets the kernel defer work to a later time or to a different context. Understanding kworker is essential for anyone who: Writes kernel modules or device drivers. Diagnoses performance or latency problems on Linux servers, embedded devices, or real‑time systems. Wants to comprehend how the kernel handles asynchronous I/O, timers, and deferred work. This article dives deep into the architecture, APIs, practical usage, debugging techniques, and performance considerations surrounding kworker. By the end, you’ll be able to: ...

Introduction When a Linux system runs out of memory, the kernel must decide which processes to terminate to reclaim RAM and keep the machine alive. That decisive, sometimes brutal, component is the Out‑Of‑Memory (OOM) Killer. While most users never see it in action, administrators, developers, and anyone who runs workloads on servers, virtual machines, or containers will eventually encounter it—especially under heavy load, memory leaks, or mis‑configured resource limits. This article provides an in‑depth, practical guide to the OOM Killer: ...