Performance

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: ...

Introduction Orchestrating dozens, hundreds, or even thousands of autonomous agents—whether they are micro‑services, IoT devices, trading bots, or fleets of drones—requires a distributed state management layer that is both fast and reliable. In a traditional monolith, a single database can serve as the single source of truth. In a multi‑agent ecosystem, however, the state is continuously mutated by many actors operating in parallel, often across geographic regions and unreliable networks. ...

Introduction State machines are a timeless abstraction for modeling deterministic behavior. Whether you are orchestrating a traffic light, coordinating a micro‑service workflow, or implementing a protocol stack, the notion of states and transitions gives you a clear, testable contract. The challenge emerges when those machines must operate at scale across many nodes, handle high throughput, and remain resilient to failures. Traditional approaches—centralized coordinators, heavyweight RPC layers, or naïve thread‑per‑machine designs—often crumble under the pressure of modern cloud workloads. ...