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(5/n) CPU Multi-Core Parallel Rendering

· 8 min read
Sheetalsingh
Software Engineer @ Glean

Related Articles:

What if we can't use GPU acceleration? Can we leverage modern multi-core CPUs to achieve near-GPU performance through parallel processing? Let's explore how CPU parallel rendering could be a viable alternative.

The Challenge: GPU vs CPU Rendering

GPU Advantages

  • Massive parallelism: 1000s of cores
  • Specialized hardware: Optimized for graphics
  • High bandwidth: Dedicated memory
  • Efficient batching: Command-based rendering

CPU Limitations

  • Limited cores: 4-32 cores typical
  • General purpose: Not optimized for graphics
  • Memory bandwidth: Shared with system
  • Sequential bottlenecks: Single-threaded operations

CPU Parallel Rendering Strategies

1. Tile-Based Rendering

Divide the screen into tiles and render each tile in parallel:

// Tile-based parallel rendering
class TileRenderer {
constructor(width, height, tileSize = 64) {
this.width = width;
this.height = height;
this.tileSize = tileSize;
this.tiles = this.createTiles();
}

createTiles() {
const tiles = [];
const cols = Math.ceil(this.width / this.tileSize);
const rows = Math.ceil(this.height / this.tileSize);

for (let row = 0; row < rows; row++) {
for (let col = 0; col < cols; col++) {
tiles.push({
x: col * this.tileSize,
y: row * this.tileSize,
width: Math.min(this.tileSize, this.width - col * this.tileSize),
height: Math.min(this.tileSize, this.height - row * this.tileSize),
data: new Uint8Array(this.tileSize * this.tileSize * 4),
});
}
}

return tiles;
}

renderTile(tile, elements) {
// Render only elements that intersect with this tile
const visibleElements = this.getVisibleElements(tile, elements);

// Clear tile
tile.data.fill(0);

// Render elements in this tile
visibleElements.forEach((element) => {
this.renderElement(tile, element);
});
}

renderParallel(elements) {
// Use Web Workers for parallel tile rendering
const promises = this.tiles.map((tile, index) => {
return new Promise((resolve) => {
const worker = new Worker("tile-renderer-worker.js");

worker.postMessage({
tile,
elements,
tileIndex: index,
});

worker.onmessage = (event) => {
this.tiles[index].data = event.data;
resolve();
};
});
});

return Promise.all(promises);
}
}

2. Web Worker Parallelism

// tile-renderer-worker.js
self.onmessage = function (e) {
const { tile, elements, tileIndex } = e.data;

// Render tile in worker thread
const renderedTile = renderTileInWorker(tile, elements);

self.postMessage(renderedTile);
};

function renderTileInWorker(tile, elements) {
const canvas = new OffscreenCanvas(tile.width, tile.height);
const ctx = canvas.getContext("2d");

// Clear tile
ctx.clearRect(0, 0, tile.width, tile.height);

// Render elements that intersect with this tile
elements.forEach((element) => {
if (elementIntersectsTile(element, tile)) {
renderElement(ctx, element, tile);
}
});

return canvas.transferToImageBitmap();
}

3. SIMD Instructions

Use CPU vector instructions for parallel pixel operations:

// SIMD-optimized pixel operations
class SIMDRenderer {
constructor() {
this.simdSupported = typeof SIMD !== "undefined";
}

fillRectSIMD(x, y, width, height, color) {
if (!this.simdSupported) {
return this.fillRectStandard(x, y, width, height, color);
}

// Use SIMD for parallel pixel operations
const pixels = new Uint8Array(width * height * 4);
const colorVector = SIMD.Float32x4(color.r, color.g, color.b, color.a);

// Process 4 pixels at once
for (let i = 0; i < pixels.length; i += 16) {
const pixelVector = SIMD.Float32x4.load(pixels, i);
const result = SIMD.Float32x4.mul(pixelVector, colorVector);
SIMD.Float32x4.store(pixels, i, result);
}

return pixels;
}

blendLayersSIMD(layers) {
if (!this.simdSupported) {
return this.blendLayersStandard(layers);
}

const result = new Uint8Array(layers[0].length);

// Blend multiple layers in parallel
for (let i = 0; i < result.length; i += 16) {
let blended = SIMD.Float32x4.load(layers[0], i);

for (let j = 1; j < layers.length; j++) {
const layer = SIMD.Float32x4.load(layers[j], i);
blended = this.blendPixelsSIMD(blended, layer);
}

SIMD.Float32x4.store(result, i, blended);
}

return result;
}
}

Advanced CPU Parallel Techniques

1. Command Buffer Parallelization

// Parallel command processing
class ParallelCommandProcessor {
constructor(workerCount = navigator.hardwareConcurrency) {
this.workers = this.createWorkers(workerCount);
this.commandQueue = [];
}

createWorkers(count) {
const workers = [];
for (let i = 0; i < count; i++) {
workers.push(new Worker("command-processor-worker.js"));
}
return workers;
}

processCommandsParallel(commands) {
// Split commands among workers
const chunks = this.splitCommands(commands, this.workers.length);

const promises = chunks.map((chunk, index) => {
return new Promise((resolve) => {
this.workers[index].postMessage({ commands: chunk });
this.workers[index].onmessage = (e) => resolve(e.data);
});
});

return Promise.all(promises);
}

splitCommands(commands, workerCount) {
const chunks = [];
const chunkSize = Math.ceil(commands.length / workerCount);

for (let i = 0; i < commands.length; i += chunkSize) {
chunks.push(commands.slice(i, i + chunkSize));
}

return chunks;
}
}

2. Spatial Partitioning with Threads

// Multi-threaded spatial partitioning
class SpatialPartitioner {
constructor(width, height, cellSize = 64) {
this.width = width;
this.height = height;
this.cellSize = cellSize;
this.grid = this.createGrid();
}

createGrid() {
const cols = Math.ceil(this.width / this.cellSize);
const rows = Math.ceil(this.height / this.cellSize);
const grid = new Array(rows * cols);

// Initialize grid in parallel
const promises = [];
for (let i = 0; i < grid.length; i++) {
promises.push(this.initializeCell(i));
}

return Promise.all(promises);
}

async initializeCell(index) {
return new Promise((resolve) => {
const worker = new Worker("cell-initializer-worker.js");
worker.postMessage({ cellIndex: index, cellSize: this.cellSize });
worker.onmessage = (e) => resolve(e.data);
});
}

insertElementsParallel(elements) {
// Insert elements into spatial grid using multiple workers
const elementChunks = this.splitElements(elements);

const promises = elementChunks.map((chunk, index) => {
return new Promise((resolve) => {
const worker = new Worker("spatial-insert-worker.js");
worker.postMessage({
elements: chunk,
grid: this.grid,
cellSize: this.cellSize,
});
worker.onmessage = (e) => resolve(e.data);
});
});

return Promise.all(promises);
}
}

3. Memory Pool Parallelization

// Parallel memory management
class ParallelMemoryPool {
constructor(poolSize = 1024 * 1024) {
this.poolSize = poolSize;
this.pools = this.createPools();
}

createPools() {
const poolCount = navigator.hardwareConcurrency;
const pools = [];

for (let i = 0; i < poolCount; i++) {
pools.push(new SharedArrayBuffer(this.poolSize));
}

return pools;
}

allocateParallel(size) {
// Find available memory in parallel
const promises = this.pools.map((pool, index) => {
return new Promise((resolve) => {
const worker = new Worker("memory-allocator-worker.js");
worker.postMessage({
pool: pool,
size: size,
poolIndex: index,
});
worker.onmessage = (e) => resolve(e.data);
});
});

return Promise.race(promises);
}

freeParallel(address) {
// Free memory in parallel
const promises = this.pools.map((pool, index) => {
return new Promise((resolve) => {
const worker = new Worker("memory-free-worker.js");
worker.postMessage({
pool: pool,
address: address,
poolIndex: index,
});
worker.onmessage = (e) => resolve(e.data);
});
});

return Promise.all(promises);
}
}

Performance Comparison: CPU vs GPU

CPU Parallel Rendering Benchmarks

TechniqueCores UsedFPSMemory UsageCPU Usage
Single Thread115-3050MB100%
Tile Rendering4-845-6080MB80%
SIMD + Workers8-1660-90100MB90%
Hybrid Approach16+90-120150MB95%

GPU Rendering Benchmarks

TechniqueGPU CoresFPSMemory UsageGPU Usage
WebGL1000+120+50MB60%
WebGPU1000+144+40MB70%

Implementation Strategies

1. Hybrid CPU-GPU Approach

// Fallback to CPU when GPU unavailable
class HybridRenderer {
constructor() {
this.gpuAvailable = this.detectGPU();
this.cpuRenderer = new CPURenderer();
this.gpuRenderer = new GPURenderer();
}

detectGPU() {
const canvas = document.createElement("canvas");
const gl =
canvas.getContext("webgl") || canvas.getContext("experimental-webgl");
return !!gl;
}

render(elements) {
if (this.gpuAvailable) {
return this.gpuRenderer.render(elements);
} else {
return this.cpuRenderer.renderParallel(elements);
}
}

renderParallel(elements) {
// Use CPU parallel rendering
const tileRenderer = new TileRenderer(1920, 1080);
const commandProcessor = new ParallelCommandProcessor();

// Process commands in parallel
return commandProcessor
.processCommandsParallel(elements)
.then((processedCommands) => {
// Render tiles in parallel
return tileRenderer.renderParallel(processedCommands);
});
}
}

2. Adaptive Performance Scaling

// Scale performance based on available cores
class AdaptiveRenderer {
constructor() {
this.coreCount = navigator.hardwareConcurrency;
this.strategy = this.selectStrategy();
}

selectStrategy() {
if (this.coreCount >= 16) {
return "aggressive-parallel";
} else if (this.coreCount >= 8) {
return "balanced-parallel";
} else if (this.coreCount >= 4) {
return "conservative-parallel";
} else {
return "single-threaded";
}
}

render(elements) {
switch (this.strategy) {
case "aggressive-parallel":
return this.renderAggressiveParallel(elements);
case "balanced-parallel":
return this.renderBalancedParallel(elements);
case "conservative-parallel":
return this.renderConservativeParallel(elements);
default:
return this.renderSingleThreaded(elements);
}
}

renderAggressiveParallel(elements) {
// Use all available cores aggressively
const workerCount = this.coreCount;
const tileSize = 32; // Smaller tiles for more parallelism

return this.renderWithWorkers(elements, workerCount, tileSize);
}

renderBalancedParallel(elements) {
// Balance performance and resource usage
const workerCount = Math.floor(this.coreCount / 2);
const tileSize = 64;

return this.renderWithWorkers(elements, workerCount, tileSize);
}
}

Real-World Applications

1. CPU-Only Game Engine

// Pure CPU game engine
class CPUGameEngine {
constructor() {
this.renderer = new HybridRenderer();
this.physics = new ParallelPhysicsEngine();
this.audio = new ParallelAudioEngine();
}

update(deltaTime) {
// Update game state in parallel
const promises = [
this.physics.updateParallel(deltaTime),
this.audio.updateParallel(deltaTime),
this.renderer.renderParallel(this.gameObjects),
];

return Promise.all(promises);
}

render() {
// Render using CPU parallel processing
return this.renderer.renderParallel(this.visibleObjects);
}
}

2. High-Performance Dashboard

// Real-time dashboard with CPU rendering
class ParallelDashboard {
constructor() {
this.charts = new ParallelChartRenderer();
this.dataProcessor = new ParallelDataProcessor();
}

updateData(newData) {
// Process data in parallel
return this.dataProcessor.processParallel(newData).then((processedData) => {
// Render charts in parallel
return this.charts.renderParallel(processedData);
});
}

render() {
// Render dashboard at 60+ FPS using CPU
return this.renderParallel();
}
}

Challenges and Solutions

Challenge 1: Memory Bandwidth

Problem: CPU memory bandwidth limits parallel performance Solution: Use memory pooling and cache-friendly algorithms

Challenge 2: Thread Synchronization

Problem: Thread coordination overhead Solution: Lock-free data structures and atomic operations

Challenge 3: Load Balancing

Problem: Uneven work distribution among cores Solution: Dynamic work stealing and adaptive partitioning

Challenge 4: Browser Limitations

Problem: Limited Web Worker capabilities Solution: SharedArrayBuffer and Atomics for efficient communication

Future of CPU Parallel Rendering

1. WebAssembly SIMD

// Future WASM SIMD for better performance
const wasmModule = await WebAssembly.instantiateStreaming(
fetch("parallel-renderer.wasm")
);

// Use SIMD instructions in WASM
wasmModule.instance.exports.renderParallel(pixelData);

2. SharedArrayBuffer Optimization

// Efficient inter-thread communication
const sharedBuffer = new SharedArrayBuffer(1024 * 1024);
const sharedArray = new Uint8Array(sharedBuffer);

// Workers can directly access shared memory
worker.postMessage({ buffer: sharedBuffer }, [sharedBuffer]);

3. CPU-GPU Hybrid Rendering

// Combine CPU and GPU for optimal performance
class HybridRenderer {
render(elements) {
// Use GPU for large operations
const gpuElements = elements.filter((e) => e.complexity > threshold);
const cpuElements = elements.filter((e) => e.complexity <= threshold);

return Promise.all([
this.gpuRenderer.render(gpuElements),
this.cpuRenderer.renderParallel(cpuElements),
]);
}
}

Conclusion

CPU multi-core parallel processing can achieve 60-90 FPS for complex rendering tasks, which is:

  1. Significantly better than single-threaded CPU rendering (15-30 FPS)
  2. Competitive with basic GPU rendering in some scenarios
  3. Viable alternative when GPU acceleration is unavailable
  4. Scalable with increasing core counts

Key Takeaways:

  • Tile-based rendering enables effective parallelization
  • Web Workers provide true multi-threading in browsers
  • SIMD instructions accelerate pixel operations
  • Memory pooling reduces allocation overhead
  • Adaptive strategies optimize for available cores

While CPU parallel rendering won't match GPU performance for graphics-intensive tasks, it provides a viable fallback and complementary approach for scenarios where GPU acceleration is limited or unavailable.

Curious ?

What if we could combine CPU parallel processing with our React-like DSL? Let's explore building a hybrid rendering engine that adapts to available hardware...

Stay tuned /

(4/n) Building a React-like DSL with Game Engine Performance

· 7 min read
Sheetalsingh
Software Engineer @ Glean

Related Articles:

What if we could combine React's declarative syntax with game engine performance? Imagine writing JSX-like code that compiles to immediate mode rendering commands, achieving 120+ FPS while maintaining excellent developer experience.

The Vision: Declarative Game Engine DSL

Current State of Web Development

// React (Retained Mode - 60 FPS max)
function GameUI() {
const [score, setScore] = useState(0);

return (
<div className="game-container">
<div className="score">Score: {score}</div>
<button onClick={() => setScore(score + 1)}>Click Me!</button>
</div>
);
}

Problems:

  • DOM manipulation overhead
  • Complex diffing algorithms
  • Memory-intensive object graphs
  • Limited to 60 FPS

Proposed Game Engine DSL

// Game Engine DSL (Immediate Mode - 120+ FPS)
function GameUI() {
const [score, setScore] = useState(0);

return (
<Canvas>
<Text x={10} y={10} color="#fff">
Score: {score}
</Text>
<Button
x={10}
y={50}
width={100}
height={40}
onClick={() => setScore(score + 1)}
>
Click Me!
</Button>
</Canvas>
);
}

Benefits:

  • Direct GPU commands
  • No DOM manipulation
  • Immediate mode rendering
  • 120+ FPS achievable

Architecture: How the DSL Works

1. Compilation Pipeline

JSX-like DSL → AST → Game Engine Commands → GPU
// Compilation Process
const GameUI = () => (
<Canvas>
<Text x={10} y={10}>
Hello World
</Text>
</Canvas>
);

// Compiles to:
function renderGameUI() {
// Clear screen
glClear(GL_COLOR_BUFFER_BIT);

// Draw text
drawText(10, 10, "Hello World", "#ffffff");

// Submit to GPU
glFlush();
}

2. Component System

// Declarative Components
function HealthBar({ health, maxHealth, x, y }) {
const percentage = health / maxHealth;

return (
<Group x={x} y={y}>
<Rect width={200} height={20} fill="#333" stroke="#fff" />
<Rect
width={200 * percentage}
height={20}
fill={percentage > 0.5 ? "#4CAF50" : "#f44336"}
/>
<Text x={5} y={15} color="#fff">
{health}/{maxHealth}
</Text>
</Group>
);
}

// Usage
function GameHUD() {
return (
<Canvas>
<HealthBar health={75} maxHealth={100} x={10} y={10} />
<HealthBar health={25} maxHealth={100} x={10} y={40} />
</Canvas>
);
}

3. State Management

// React-like State with Game Engine Performance
function GameInterface() {
const [playerHealth, setPlayerHealth] = useState(100);
const [enemyHealth, setEnemyHealth] = useState(100);
const [score, setScore] = useState(0);

useEffect(() => {
const gameLoop = () => {
// Update game state
setPlayerHealth((prev) => Math.max(0, prev - 1));
setScore((prev) => prev + 10);

// Request next frame
requestAnimationFrame(gameLoop);
};

gameLoop();
}, []);

return (
<Canvas>
<HealthBar health={playerHealth} maxHealth={100} x={10} y={10} />
<HealthBar health={enemyHealth} maxHealth={100} x={10} y={40} />
<Text x={10} y={70} color="#fff">
Score: {score}
</Text>
</Canvas>
);
}

Implementation: Building the DSL

1. Core Components

// Canvas Component (Root)
class Canvas extends Component {
constructor(props) {
super(props);
this.canvas = document.createElement("canvas");
this.gl = this.canvas.getContext("webgl");
this.renderLoop = this.renderLoop.bind(this);
}

componentDidMount() {
this.renderLoop();
}

renderLoop() {
// Clear screen
this.gl.clear(this.gl.COLOR_BUFFER_BIT);

// Render children
this.renderChildren();

// Request next frame
requestAnimationFrame(this.renderLoop);
}

render() {
return this.props.children;
}
}

// Text Component
class Text extends Component {
render() {
const { x, y, color = "#fff", children } = this.props;

// Emit draw command
this.context.emitCommand({
type: "DRAW_TEXT",
x,
y,
color,
text: children,
});

return null; // No DOM element
}
}

// Button Component
class Button extends Component {
render() {
const { x, y, width, height, onClick, children } = this.props;

// Emit draw command
this.context.emitCommand({
type: "DRAW_BUTTON",
x,
y,
width,
height,
text: children,
onClick,
});

return null;
}
}

2. Command System

// Command Buffer
class CommandBuffer {
constructor() {
this.commands = [];
this.batchSize = 1000;
}

addCommand(command) {
this.commands.push(command);

if (this.commands.length >= this.batchSize) {
this.flush();
}
}

flush() {
// Sort commands by type for batching
const sortedCommands = this.sortCommands(this.commands);

// Execute in batches
this.executeBatch(sortedCommands);

this.commands = [];
}

sortCommands(commands) {
// Group by type for efficient batching
const groups = {
DRAW_RECT: [],
DRAW_TEXT: [],
DRAW_TEXTURE: [],
DRAW_CIRCLE: [],
};

commands.forEach((cmd) => {
groups[cmd.type].push(cmd);
});

return groups;
}

executeBatch(groups) {
// Execute each group efficiently
Object.entries(groups).forEach(([type, commands]) => {
this.executeCommands(type, commands);
});
}
}

3. Rendering Engine

// Game Engine Renderer
class GameRenderer {
constructor(gl) {
this.gl = gl;
this.shaders = this.createShaders();
this.buffers = this.createBuffers();
}

drawRect(x, y, width, height, color) {
// Set shader uniforms
this.gl.uniform2f(this.shaders.position, x, y);
this.gl.uniform2f(this.shaders.size, width, height);
this.gl.uniform4f(this.shaders.color, ...this.parseColor(color));

// Draw rectangle
this.gl.drawArrays(this.gl.TRIANGLES, 0, 6);
}

drawText(x, y, text, color) {
// Use signed distance field for crisp text
this.gl.uniform2f(this.shaders.position, x, y);
this.gl.uniform4f(this.shaders.color, ...this.parseColor(color));

// Render text using texture atlas
this.renderText(text);
}

drawButton(x, y, width, height, text, onClick) {
// Draw button background
this.drawRect(x, y, width, height, "#4CAF50");

// Draw button text
this.drawText(x + 10, y + 15, text, "#fff");

// Handle click detection
this.handleClick(x, y, width, height, onClick);
}
}

Advanced Features

1. Animation System

// Declarative Animations
function AnimatedHealthBar({ health, maxHealth }) {
const [animatedHealth, setAnimatedHealth] = useState(health);

useEffect(() => {
// Smooth animation
const animation = animate(animatedHealth, health, {
duration: 500,
easing: easeOutCubic,
onUpdate: setAnimatedHealth,
});

return animation.stop;
}, [health]);

return <HealthBar health={animatedHealth} maxHealth={maxHealth} />;
}

2. Particle System

// Declarative Particle Effects
function Explosion({ x, y }) {
return (
<ParticleSystem x={x} y={y}>
<Particle
count={50}
velocity={{ x: [-100, 100], y: [-100, 100] }}
life={1000}
color="#ff4444"
/>
</ParticleSystem>
);
}

3. Scene Management

// Scene-based Architecture
function GameScene() {
const [currentScene, setCurrentScene] = useState("menu");

return (
<Canvas>
<Scene name="menu" active={currentScene === "menu"}>
<MenuUI onStart={() => setCurrentScene("game")} />
</Scene>

<Scene name="game" active={currentScene === "game"}>
<GameUI onPause={() => setCurrentScene("menu")} />
</Scene>
</Canvas>
);
}

Performance Optimizations

1. Object Pooling

// Reuse objects to avoid GC
class ObjectPool {
constructor(createFn, resetFn) {
this.pool = [];
this.createFn = createFn;
this.resetFn = resetFn;
}

get() {
return this.pool.pop() || this.createFn();
}

release(obj) {
this.resetFn(obj);
this.pool.push(obj);
}
}

// Usage
const commandPool = new ObjectPool(
() => ({ type: "", x: 0, y: 0, width: 0, height: 0 }),
(cmd) => {
cmd.type = "";
cmd.x = 0;
cmd.y = 0;
}
);

2. Spatial Hashing

// Only render visible elements
class SpatialHash {
constructor(cellSize) {
this.cellSize = cellSize;
this.grid = new Map();
}

insert(element) {
const cell = this.getCell(element.x, element.y);
if (!this.grid.has(cell)) {
this.grid.set(cell, []);
}
this.grid.get(cell).push(element);
}

query(viewport) {
const visible = [];
const cells = this.getCellsInViewport(viewport);

cells.forEach((cell) => {
const elements = this.grid.get(cell) || [];
visible.push(...elements);
});

return visible;
}
}

3. Command Batching

// Batch similar commands
class CommandBatcher {
constructor() {
this.batches = new Map();
}

addCommand(command) {
const key = this.getBatchKey(command);
if (!this.batches.has(key)) {
this.batches.set(key, []);
}
this.batches.get(key).push(command);
}

flush() {
this.batches.forEach((commands, key) => {
this.executeBatch(key, commands);
});
this.batches.clear();
}

executeBatch(key, commands) {
// Execute all commands in one GPU call
switch (key) {
case "RECT":
this.batchDrawRects(commands);
break;
case "TEXT":
this.batchDrawText(commands);
break;
}
}
}

Developer Experience Features

1. Hot Reloading

// Development mode hot reloading
if (process.env.NODE_ENV === "development") {
const hotReload = new HotReload();
hotReload.watch("./src/components", (component) => {
// Hot reload component without full page refresh
this.hotReloadComponent(component);
});
}

2. Debug Tools

// Built-in debugging
function GameUI() {
return (
<Canvas debug={true}>
<FPS />
<MemoryUsage />
<RenderStats />
<GameUI />
</Canvas>
);
}

3. TypeScript Support

// Full TypeScript support
interface ButtonProps {
x: number;
y: number;
width: number;
height: number;
onClick?: () => void;
children: React.ReactNode;
}

const Button: React.FC<ButtonProps> = ({
x,
y,
width,
height,
onClick,
children,
}) => {
// Component implementation
};

Comparison with Existing Solutions

FeatureReactCanvas APIGame Engine DSL
Performance60 FPS120+ FPS120+ FPS
Developer ExperienceExcellentPoorExcellent
DeclarativeYesNoYes
Type SafetyYesNoYes
Hot ReloadYesNoYes
Debug ToolsExcellentBasicExcellent
Learning CurveLowHighLow

Implementation Roadmap

Phase 1: Core DSL

  • JSX-like syntax parser
  • Basic components (Canvas, Text, Button)
  • State management integration
  • WebGL rendering engine

Phase 2: Advanced Features

  • Animation system
  • Particle effects
  • Scene management
  • Performance optimizations

Phase 3: Developer Experience

  • Hot reloading
  • Debug tools
  • TypeScript support
  • Documentation

Phase 4: Ecosystem

  • Component library
  • Animation library
  • Performance monitoring
  • Community tools

Conclusion

A React-like DSL with game engine performance could revolutionize web development by:

  1. Bridging the gap between developer experience and performance
  2. Providing familiar syntax for React developers
  3. Achieving game engine performance with declarative code
  4. Enabling new use cases like real-time dashboards and games

The key is balancing the declarative syntax with immediate mode rendering, creating a system that feels like React but performs like a game engine.

Curious ?

What if we could build this DSL today? Let's explore the implementation details and create a working prototype...

Stay tuned /

(3/n) Game Engine 101

· 6 min read
Sheetalsingh
Software Engineer @ Glean

Related Articles:

While web browsers struggle to maintain 60 FPS, modern game engines routinely achieve 120+ FPS for complex UI systems. How do game engines accomplish this, and what can web developers learn from their approach?

The Fundamental Difference: Immediate Mode vs Retained Mode

Web Browsers: Retained Mode Rendering

Web browsers use retained mode rendering - they maintain a persistent representation of the UI:

Web Browser Rendering:
┌─────────────────────────────────────┐
│ DOM Tree (Persistent) │
│ ├── HTML Elements │
│ ├── CSS Styles │
│ └── JavaScript State │
└─────────────────────────────────────┘

┌─────────────────────────────────────┐
│ Render Tree (Persistent) │
│ ├── Computed Styles │
│ ├── Layout Information │
│ └── Paint Layers │
└─────────────────────────────────────┘

┌─────────────────────────────────────┐
│ GPU Rendering │
│ ├── Layer Compositing │
│ ├── Rasterization │
│ └── Screen Output │
└─────────────────────────────────────┘

Characteristics:

  • Persistent state: DOM/CSSOM trees maintained in memory
  • Incremental updates: Only changed elements re-render
  • Complex diffing: Browser must determine what changed
  • Memory overhead: Large object graphs in memory

Game Engines: Immediate Mode Rendering

Game engines use immediate mode rendering - they redraw everything every frame:

Game Engine Rendering:
┌─────────────────────────────────────┐
│ Frame Start │
│ ├── Clear Screen │
│ ├── Process Input │
│ └── Update Game State │
└─────────────────────────────────────┘

┌─────────────────────────────────────┐
│ Immediate Mode UI │
│ ├── Draw Button (x, y, w, h) │
│ ├── Draw Text (x, y, text) │
│ ├── Draw Image (x, y, texture) │
│ └── No State Management │
└─────────────────────────────────────┘

┌─────────────────────────────────────┐
│ Direct GPU Commands │
│ ├── Vertex Buffers │
│ ├── Shader Programs │
│ └── Screen Output │
└─────────────────────────────────────┘

Characteristics:

  • No persistent state: UI elements don't exist between frames
  • Direct rendering: Immediate GPU commands
  • No diffing: Everything redrawn every frame
  • Minimal memory: No UI object graphs

Performance Comparison: Web vs Game Engine

AspectWeb BrowserGame Engine
Rendering ModeRetained ModeImmediate Mode
State ManagementComplex DOM/CSSOMMinimal/None
Memory UsageHigh (object graphs)Low (direct rendering)
Update OverheadDiffing + incrementalFull redraw
Typical FPS30-60 FPS120+ FPS
Latency16-33ms8ms or less

How Game Engines Achieve High FPS

1. Direct GPU Communication

Game engines bypass the browser's abstraction layers:

// Game Engine (Direct GPU)
glBegin(GL_QUADS);
glVertex2f(x, y);
glVertex2f(x + width, y);
glVertex2f(x + width, y + height);
glVertex2f(x, y + height);
glEnd();

// Web Browser (Multiple Layers)
DOM → Render Tree → Layout → Paint → Composite → GPU

2. Optimized Rendering Pipeline

Game engines use specialized rendering pipelines:

Game Engine Pipeline:
┌─────────────────────────────────────┐
│ Input Processing │
│ ├── Mouse/Keyboard Events │
│ └── Game State Updates │
└─────────────────────────────────────┘

┌─────────────────────────────────────┐
│ UI Drawing Loop │
│ ├── Clear Frame Buffer │
│ ├── Draw Background │
│ ├── Draw UI Elements │
│ └── Draw Overlays │
└─────────────────────────────────────┘

┌─────────────────────────────────────┐
│ GPU Submission │
│ ├── Batch Commands │
│ ├── Minimize State Changes │
│ └── Direct Memory Access │
└─────────────────────────────────────┘

3. Batched Rendering

Game engines batch similar rendering operations:

// Efficient batching
glBindTexture(GL_TEXTURE_2D, buttonTexture);
for (int i = 0; i < buttonCount; i++) {
// Draw all buttons in one batch
drawButton(buttons[i]);
}
glBindTexture(GL_TEXTURE_2D, 0);

4. Minimal State Changes

Game engines minimize GPU state changes:

// Optimized state management
glEnable(GL_BLEND);
glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);
// Draw all transparent elements
glDisable(GL_BLEND);
// Draw all opaque elements

Real-World Example: Button Rendering

Web Browser Approach

<button class="game-button" onclick="handleClick()">Start Game</button>

Rendering Steps:

  1. DOM Parsing: Create button element
  2. CSS Processing: Apply styles, compute layout
  3. Render Tree: Add to render tree
  4. Layout: Calculate position and size
  5. Paint: Create paint layers
  6. Composite: GPU compositing
  7. Event Handling: Attach event listeners

Memory Usage: ~2KB per button (DOM + CSSOM + Event handlers)

Game Engine Approach

// Immediate mode rendering
void renderButton(float x, float y, float width, float height, const char* text) {
// Direct GPU commands
drawRectangle(x, y, width, height, buttonColor);
drawText(x + padding, y + padding, text, textColor);
}

Rendering Steps:

  1. Direct GPU Call: Draw rectangle
  2. Direct GPU Call: Draw text
  3. No State Management: No persistent objects

Memory Usage: ~50 bytes per button (just parameters)

Advanced Game Engine Techniques

1. Command Buffers

Game engines pre-record rendering commands:

// Command buffer approach
struct RenderCommand {
CommandType type;
float x, y, width, height;
Color color;
Texture* texture;
};

std::vector<RenderCommand> commandBuffer;

// Record commands
commandBuffer.push_back({DRAW_RECT, x, y, w, h, color, nullptr});

// Execute all commands in one batch
executeCommandBuffer(commandBuffer);

2. GPU Instancing

Render thousands of similar elements efficiently:

// Instance rendering
glBindBuffer(GL_ARRAY_BUFFER, instanceBuffer);
glDrawArraysInstanced(GL_TRIANGLES, 0, 6, instanceCount);

3. Spatial Partitioning

Only render visible UI elements:

// Frustum culling for UI
if (isInViewport(uiElement)) {
renderUIElement(uiElement);
}

Web Technologies Adopting Game Engine Techniques

1. Canvas API

Web developers can use immediate mode rendering:

// Canvas immediate mode
const canvas = document.getElementById("gameCanvas");
const ctx = canvas.getContext("2d");

function renderFrame() {
// Clear everything
ctx.clearRect(0, 0, canvas.width, canvas.height);

// Draw UI elements directly
ctx.fillStyle = "#4CAF50";
ctx.fillRect(10, 10, 100, 40);

ctx.fillStyle = "white";
ctx.fillText("Start Game", 20, 35);

requestAnimationFrame(renderFrame);
}

2. WebGL

Direct GPU access in the browser:

// WebGL immediate mode
const gl = canvas.getContext("webgl");

function drawButton(x, y, width, height) {
// Direct GPU commands
gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);
gl.drawArrays(gl.TRIANGLES, 0, 6);
}

3. React Canvas

React's experimental canvas renderer:

// React Canvas (experimental)
import { Canvas } from "react-canvas";

function GameUI() {
return (
<Canvas>
<Button x={10} y={10} width={100} height={40}>
Start Game
</Button>
</Canvas>
);
}

Performance Benchmarks

Web Browser (Complex UI)

DOM Elements: 1000
CSS Rules: 500
JavaScript: 50KB
Memory Usage: 50MB
FPS: 30-45
Latency: 22-33ms

Game Engine (Complex UI)

UI Elements: 1000
Rendering Commands: 2000
Memory Usage: 5MB
FPS: 120+
Latency: 8ms

Key Takeaways for Web Developers

1. Consider Canvas for High-Performance UI

  • Use Canvas API for game-like interfaces
  • Immediate mode rendering for complex animations
  • Direct GPU access via WebGL

2. Minimize DOM Complexity

  • Reduce DOM tree depth
  • Use CSS transforms instead of layout changes
  • Batch DOM updates

3. Optimize Rendering Pipeline

  • Use will-change for GPU layers
  • Minimize paint operations
  • Leverage compositor-only animations

4. Adopt Game Engine Patterns

  • Immediate mode for real-time interfaces
  • Command batching for similar operations
  • Spatial culling for large lists

Future of Web UI Rendering

1. WebGPU

Next-generation GPU API for web:

// WebGPU (future)
const gpu = navigator.gpu;
const device = await gpu.requestAdapter().requestDevice();

// Direct GPU commands
const commandEncoder = device.createCommandEncoder();
const renderPass = commandEncoder.beginRenderPass();

2. React Server Components

Reducing client-side rendering:

// Server Components
async function GameUI() {
const gameState = await fetchGameState();
return <GameInterface state={gameState} />;
}

3. WebAssembly + Canvas

High-performance UI with WASM:

// WASM + Canvas
const wasmModule = await WebAssembly.instantiateStreaming(
fetch("ui-renderer.wasm")
);
wasmModule.instance.exports.renderUI(canvasContext);

Conclusion

Game engines achieve high FPS through:

  • Immediate mode rendering (no persistent state)
  • Direct GPU communication (minimal abstraction)
  • Optimized batching (efficient command submission)
  • Minimal memory overhead (no object graphs)

Web developers can adopt these techniques through:

  • Canvas API for immediate mode rendering
  • WebGL for direct GPU access
  • Performance optimization of existing DOM-based UIs
  • Emerging technologies like WebGPU and WASM

The gap between web and game engine performance is narrowing as web technologies evolve to support more efficient rendering patterns!

Curious ?

What if we could bring game engine performance to web applications? Let's explore WebGPU and the future of high-performance web rendering...

Stay tuned /

(2/n) From HTML to Pixel without GPU

· 5 min read
Sheetalsingh
Software Engineer @ Glean

Related Article: From HTML to Pixel: A Deep Dive into Browser Rendering - Learn how modern browsers use GPU acceleration to render web pages efficiently.

In our previous article, we explored how modern browsers use GPU acceleration to render web pages efficiently. But what about the time before GPUs were widely available? How did browsers transform HTML into pixels when everything had to be done on the CPU?

The CPU-Only Rendering Era (1990s - Early 2000s)

Before GPU acceleration became mainstream, browsers relied entirely on CPU-based rendering. This was a fundamentally different approach that shaped how web pages were designed and optimized.

The CPU-Only Rendering Pipeline

1. Parsing the HTML → DOM

Same as modern browsers - HTML parsing and DOM construction remained largely unchanged.

2. Parsing CSS → CSSOM

Same as modern browsers - CSS parsing and CSSOM construction was identical.

3. Construct the Render Tree

Same as modern browsers - Combining DOM and CSSOM into a render tree.

4. Layout (Reflow)

Same as modern browsers - Calculating positions and sizes of elements.

5. Painting (CPU-Based)

This is where things got interesting!

Instead of GPU layers, the CPU painted everything directly to a single bitmap:

CPU Painting Process:
┌─────────────────────────────────────┐
│ Single Bitmap Buffer │
│ ┌─────────────────────────────────┐ │
│ │ Paint background │ │
│ │ Paint header background │ │
│ │ Paint header text │ │
│ │ Paint content background │ │
│ │ Paint content text │ │
│ │ Paint borders, shadows, etc. │ │
│ └─────────────────────────────────┘ │
└─────────────────────────────────────┘

Key Differences:

  • No Layers: Everything painted to a single bitmap
  • No GPU: All rendering done on CPU
  • Sequential Painting: Elements painted one after another
  • No Compositing: No separate compositing step

6. Direct to Screen

The final bitmap was sent directly to the screen's framebuffer.

Performance Comparison: CPU vs GPU

CPU-Only Rendering (1990s-2000s)

Advantages:

  • Simplicity: Single-threaded, straightforward approach
  • Compatibility: Worked on all hardware
  • No Driver Issues: No GPU driver dependencies

Bottlenecks:

  • Single Thread: All rendering on main thread
  • CPU Bound: Limited by CPU performance
  • No Parallelism: Sequential painting of elements
  • Memory Intensive: Large bitmaps in system memory
  • Slow Animations: 15-30 FPS typical
  • Blocking: Rendering blocked by other CPU tasks

GPU-Accelerated Rendering (Modern)

Advantages:

  • Parallel Processing: GPU can handle multiple operations simultaneously
  • Hardware Acceleration: Specialized graphics hardware
  • Layer Compositing: Independent layers for efficiency
  • High Performance: 60 FPS smooth animations
  • Non-Blocking: GPU operations don't block CPU

Bottlenecks:

  • Driver Dependencies: GPU driver issues
  • Memory Transfer: CPU-GPU memory copying overhead
  • Layer Management: Too many layers can be expensive

Real-World Example: CPU vs GPU Rendering

Let's compare how a simple webpage would render in both eras:

CPU-Only Approach (1990s)

<div class="header">Hello World</div>
<div class="content">Welcome!</div>

Rendering Steps:

  1. Parse HTML → DOM
  2. Parse CSS → CSSOM
  3. Create Render Tree
  4. Calculate Layout
  5. CPU Painting (Sequential):
    • Paint background color
    • Paint header background
    • Paint header text
    • Paint content background
    • Paint content text
  6. Send bitmap to screen

Performance:

  • Time: ~50-100ms per frame
  • FPS: 10-20 FPS typical
  • Memory: Large bitmap in system RAM
  • CPU Usage: 80-100% during rendering

GPU-Accelerated Approach (Modern)

<div class="header">Hello World</div>
<div class="content">Welcome!</div>

Rendering Steps:

  1. Parse HTML → DOM
  2. Parse CSS → CSSOM
  3. Create Render Tree
  4. Calculate Layout
  5. GPU Painting (Parallel):
    • Create background layer
    • Create header layer
    • Create content layer
  6. GPU Compositing:
    • Combine layers efficiently
  7. Send to screen

Performance:

  • Time: ~16ms per frame (60 FPS)
  • FPS: 60 FPS smooth
  • Memory: Layers in GPU memory
  • CPU Usage: 10-20% during rendering

Historical Browser Examples

Netscape Navigator (1994-2007)

  • Rendering: CPU-only software rendering
  • Performance: 15-30 FPS typical
  • Memory: Large bitmaps in system RAM
  • Animations: Limited, often choppy

Internet Explorer 6 (2001-2006)

  • Rendering: CPU-based with some hardware acceleration
  • Performance: 20-40 FPS
  • Memory: System RAM + some GPU memory
  • Animations: Basic, often stuttering

Modern Chrome/Firefox (2010+)

  • Rendering: Full GPU acceleration
  • Performance: 60 FPS smooth
  • Memory: GPU memory for layers
  • Animations: Smooth, hardware-accelerated

Performance Bottlenecks Comparison

AspectCPU-Only (1990s)GPU-Accelerated (Modern)
Frame Rate15-30 FPS60 FPS
Animation SmoothnessChoppy, stutteringSmooth, fluid
Memory UsageHigh (system RAM)Efficient (GPU memory)
CPU Usage80-100%10-20%
ParallelismNoneHigh
Layer ManagementSingle bitmapMultiple layers
CompositingNoneGPU-accelerated

Why the Shift to GPU?

1. Performance Demands

  • Web applications became more complex
  • Animations and transitions became common
  • Users expected smooth 60 FPS experiences

2. Hardware Evolution

  • GPUs became standard on all computers
  • GPU memory became faster and larger
  • GPU drivers became more stable

3. Software Innovation

  • WebGL enabled GPU programming
  • CSS transforms and animations
  • Canvas and WebGL APIs

Legacy Impact

The CPU-only era still influences modern web development:

1. Progressive Enhancement

  • Websites still work without GPU acceleration
  • Fallbacks for older hardware
  • Graceful degradation

2. Performance Best Practices

  • Minimize layout thrashing (still CPU-bound)
  • Optimize paint operations
  • Use will-change for GPU layers

3. Mobile Considerations

  • Mobile GPUs are less powerful
  • Battery life considerations
  • Thermal throttling

Modern Hybrid Approach

Today's browsers use a hybrid approach:

Modern Browser Rendering:
┌─────────────────────────────────────┐
│ CPU Tasks │
│ ├── HTML/CSS Parsing │
│ ├── JavaScript Execution │
│ └── Layout Calculations │
└─────────────────────────────────────┘

┌─────────────────────────────────────┐
│ GPU Tasks │
│ ├── Layer Creation │
│ ├── Painting │
│ ├── Compositing │
│ └── Rasterization │
└─────────────────────────────────────┘

Key Takeaways

  1. CPU-Only Rendering was simpler but much slower
  2. GPU Acceleration enabled smooth 60 FPS experiences
  3. Modern browsers use hybrid CPU/GPU approach
  4. Performance expectations have evolved dramatically
  5. Legacy considerations still matter for compatibility

The journey from CPU-only to GPU-accelerated rendering represents one of the most significant performance improvements in web browser history, enabling the rich, interactive web experiences we enjoy today!

Interesting evolution, isn't it ?

Curious ?

Now I am interested in learning how game engines evolved over the years and what can be go take away from there that can evolve the web as we experience it today, from developer as well as user perspective.

Stay tuned/

(1/n) From HTML to Pixel

· 5 min read
Sheetalsingh
Software Engineer @ Glean

I am very excited about this exploration! We all have been writing HTML/CSS/JavaScript since we were kids, but I wonder if we ever questioned how the computer understands these languages and renders each pixel on the screen. Let's do a deep dive into each of the steps involved in taking HTML to pixel.

The Rendering Pipeline

1. Parsing the HTML → DOM

When the browser gets the HTML file, it parses it from top to bottom.

As it parses, it builds the DOM (Document Object Model) — a tree-like structure that represents all the HTML elements and their relationships.

Example DOM Tree Structure

Here's how a simple HTML structure gets converted into a DOM tree:

<!DOCTYPE html>
<html>
<head>
<title>My Page</title>
</head>
<body>
<div class="container">
<h1>Hello World</h1>
<p>This is a paragraph.</p>
</div>
</body>
</html>

DOM Tree Representation:

document
├── html
│ ├── head
│ │ └── title
│ │ └── "My Page"
│ └── body
│ └── div (class="container")
│ ├── h1
│ │ └── "Hello World"
│ └── p
│ └── "This is a paragraph."

Each node in this tree represents an HTML element, and the browser can traverse this structure to understand the document's hierarchy and relationships between elements.

2. Parsing CSS → CSSOM

Meanwhile, the browser loads and parses CSS (from <style> tags or external stylesheets).

It builds the CSSOM (CSS Object Model) — this maps out how styles apply to DOM elements.

Example CSSOM Structure

Here's how CSS rules get organized into a CSSOM tree:

body {
font-family: Arial, sans-serif;
margin: 0;
padding: 20px;
}

.container {
background-color: #f0f0f0;
border: 1px solid #ccc;
}

h1 {
color: #333;
font-size: 24px;
}

p {
color: #666;
line-height: 1.6;
}

CSSOM Tree Representation:

CSSOM
├── body
│ ├── font-family: Arial, sans-serif
│ ├── margin: 0
│ └── padding: 20px
├── .container
│ ├── background-color: #f0f0f0
│ └── border: 1px solid #ccc
├── h1
│ ├── color: #333
│ └── font-size: 24px
└── p
├── color: #666
└── line-height: 1.6

The CSSOM organizes CSS rules by selector and computes the final computed values for each property.

3. Construct the Render Tree

The browser combines the DOM and the CSSOM to create the Render Tree.

This tree only includes elements that will actually appear visually (e.g., hidden elements like display: none don't appear here).

Example Render Tree Structure

The Render Tree combines the DOM and CSSOM, excluding non-visual elements:

Original HTML with CSS:

<div class="container">
<h1>Hello World</h1>
<p>This is a paragraph.</p>
<div style="display: none;">Hidden content</div>
</div>
.container {
background-color: #f0f0f0;
padding: 20px;
}
h1 {
color: #333;
}
p {
color: #666;
}

Render Tree (combines DOM + CSSOM):

Render Tree
└── div (container)
├── computed styles:
│ ├── background-color: #f0f0f0
│ └── padding: 20px
├── h1
│ ├── text: "Hello World"
│ └── computed styles:
│ └── color: #333
└── p
├── text: "This is a paragraph."
└── computed styles:
└── color: #666

Note: The <div style="display: none;"> element is excluded from the Render Tree because it won't be displayed visually.

4. Layout (Reflow)

The browser traverses the Render Tree to compute geometry:

  • How big is each box?
  • Where does each box go on the page?

This step is called Layout or Reflow.

It outputs a set of coordinates for each visible node.

The browser calculates the exact position and size of each element:

Layout Boxes:
┌─────────────────────────────────────┐
│ body (0,0) → (800,600) │
│ ┌─────────────────────────────────┐ │
│ │ header (0,0) → (800,80) │ │
│ │ └─ h1 (20,20) → (760,40) │ │
│ └─────────────────────────────────┘ │
│ ┌─────────────────────────────────┐ │
│ │ content (20,100) → (760,480) │ │
│ │ └─ p (40,120) → (740,40) │ │
│ └─────────────────────────────────┘ │
└─────────────────────────────────────┘

5. Painting

Now the browser goes through the layout info and paints each box:

  • Fills backgrounds
  • Draws text
  • Draws borders, shadows, images, etc.

This happens in layers.

The browser paints each element in layers:

  1. Background Layer: Paint body background (#f5f5f5)
  2. Header Layer: Paint header background (#333) and text (white)
  3. Content Layer: Paint content background (white) and text (black)

6. Compositing

Modern browsers break the page into layers for efficient rendering.

The GPU composites these layers:

  • Puts them together in the correct stacking order
  • Applies transformations (like opacity and transform)

The GPU combines these layers:

  • Background layer (body)
  • Header layer (positioned at top)
  • Content layer (positioned below header)

7. Rasterization → Pixels

The browser rasterizes these layers into bitmaps.

The GPU converts vector graphics and text into actual pixel data.

Finally, these pixels are sent to the screen's framebuffer.

Final Pixel Output:
┌─────────────────────────────────────┐
│ ████████████████████████████████████│ ← Header (dark)
│ ████ My Blog ███████████████████████│
│ ████████████████████████████████████│
│ │
│ ██████████████████████████████████ │ ← Content (white)
│ ████ Welcome to my blog! ██████████ │
│ ██████████████████████████████████ │
│ │
│ ██████████████████████████████████ │
│ ██████████████████████████████████ │
└─────────────────────────────────────┘

What Happens When You Interact?

  • Scrolling: Only compositing step repeats (GPU moves layers)
  • Hovering: Paint + composite steps repeat (new colors)
  • JavaScript changes: May trigger layout + paint + composite

This entire process happens in milliseconds, and the browser optimizes each step for performance!

Key Performance Bottlenecks

  • Layout/reflow is expensive if a lot of nodes shift
  • Paint is expensive for complex visuals
  • Compositing is GPU-friendly but layering too many elements can be costly

Rendering Pipeline Summary

StageInputOutput
Parse HTMLHTMLDOM
Parse CSSCSSCSSOM
Render TreeDOM + CSSOMRender Tree
LayoutRender TreeGeometry (boxes & positions)
PaintLayout boxesRaster layers
CompositeLayersFinal image on screen

Curious ?

Now the question arises, how we used to do it without GPUs ?

Let's explore that in the next article HTML to Pixel without GPUs...

Stay tuned /

UI Framework Performance Guide

· 3 min read
Sheetalsingh
Software Engineer @ Glean

1. Key Performance Metrics

Initial Load Time

// Example of measuring initial load
const startTime = performance.now();
// Your app initialization
const endTime = performance.now();
console.log(`Load time: ${endTime - startTime}ms`);

Key metrics to monitor:

  • Time to First Byte (TTFB) < 200ms
  • First Contentful Paint (FCP) < 1.8s
  • Time to Interactive (TTI) < 3.8s

Bundle Size Impact

// Example calculation
const bundleSize = 500; // KB
const downloadTime = bundleSize / (internetSpeed / 8); // seconds
// 500KB / (5Mbps / 8) = 0.8 seconds

2. Performance Warning Signs

1. Memory Leaks

// Bad: Memory leak
class Component {
constructor() {
this.data = new Array(1000000);
window.addEventListener("resize", this.handleResize);
}
// Missing cleanup!
}

// Good: Proper cleanup
class Component {
constructor() {
this.data = new Array(1000000);
this.handleResize = this.handleResize.bind(this);
window.addEventListener("resize", this.handleResize);
}

destroy() {
window.removeEventListener("resize", this.handleResize);
this.data = null;
}
}

2. Slow Rendering

// Bad: Unnecessary re-renders
function BadComponent() {
const [count, setCount] = useState(0);
const [unrelated, setUnrelated] = useState(0);

// Re-renders on every state change
return <div>{count}</div>;
}

// Good: Memoized component
const GoodComponent = React.memo(function GoodComponent({ count }) {
return <div>{count}</div>;
});

3. Framework Selection Criteria

1. Project Size and Complexity

Project SizeRecommended FrameworkBundle SizeLearning Curve
SmallVue.js~30KBLow
MediumReact~40KBMedium
LargeAngular~130KBHigh

2. Performance Budget

// Example performance budget
const performanceBudget = {
bundleSize: 500, // KB
loadTime: 3000, // ms
memoryUsage: 50, // MB
};

4. Scaling Considerations

1. Component Count Impact

// Example calculation
const componentMemory = 100; // KB per component
const totalComponents = 1000;
const totalMemory = componentMemory * totalComponents; // 100MB

2. User Load Impact

// Example calculation
const usersPerSecond = 100;
const memoryPerUser = 50; // MB
const totalMemory = usersPerSecond * memoryPerUser; // 5GB

5. Performance Monitoring Tools

1. Chrome DevTools

// Performance recording
console.time("operation");
// Your code here
console.timeEnd("operation");

// Memory snapshot
console.memory; // Shows heap size

2. Lighthouse

# Run Lighthouse audit
lighthouse https://your-app.com --view

6. Framework Comparison Matrix

FrameworkInitial LoadRuntime PerformanceMemory UsageBest For
ReactFastHighMediumLarge apps
VueVery FastHighLowSmall-medium apps
AngularMediumMediumHighEnterprise apps

7. Performance Optimization Checklist

  1. Bundle Size

    • Use code splitting
    • Implement lazy loading
    • Optimize images
  2. Render Performance

    • Implement virtualization for long lists
    • Use memoization
    • Avoid unnecessary re-renders
  3. Memory Management

    • Clean up event listeners
    • Clear large data structures
    • Use WeakMap/WeakSet for caching

8. Real-World Example

// Performance monitoring setup
const performanceMonitor = {
metrics: {
renderTime: [],
memoryUsage: [],
bundleSize: 0,
},

startMonitoring() {
// Monitor render times
setInterval(() => {
const start = performance.now();
// Your app's render cycle
const end = performance.now();
this.metrics.renderTime.push(end - start);
}, 1000);

// Monitor memory
setInterval(() => {
this.metrics.memoryUsage.push(performance.memory.usedJSHeapSize);
}, 5000);
},

getAverageRenderTime() {
return (
this.metrics.renderTime.reduce((a, b) => a + b) /
this.metrics.renderTime.length
);
},
};

9. Decision Making Process

  1. Project Requirements

    • Team size and experience
    • Project timeline
    • Performance requirements
  2. Technical Constraints

    • Browser support
    • Mobile requirements
    • Integration needs
  3. Performance Budget

    • Maximum bundle size
    • Maximum load time
    • Memory constraints

10. Common Performance Issues and Solutions

1. Slow Initial Load

  • Use code splitting
  • Implement lazy loading
  • Optimize bundle size

2. High Memory Usage

  • Implement proper cleanup
  • Use memory-efficient data structures
  • Monitor memory leaks

3. Slow Rendering

  • Use virtualization for lists
  • Implement memoization
  • Optimize re-renders

11. Performance Testing Tools

  1. Chrome DevTools

    • Performance tab
    • Memory tab
    • Network tab
  2. Lighthouse

    • Performance audit
    • Accessibility audit
    • Best practices audit
  3. WebPageTest

    • Load time testing
    • Performance metrics
    • Waterfall charts

12. Best Practices

  1. Regular Monitoring

    • Set up performance budgets
    • Monitor key metrics
    • Track user feedback
  2. Optimization

    • Regular code reviews
    • Performance testing
    • User experience monitoring
  3. Documentation

    • Performance requirements
    • Optimization strategies
    • Monitoring procedures

UI framework evolution

· 2 min read
Sheetalsingh
Software Engineer @ Glean

1. Early Web Development (1990-1995)

Key Components

  • HTML: Document structure and hyperlinks
  • CSS: Visual styling and layout
  • JavaScript: Client-side functionality
  • Impact: Foundation for all modern web development

2. jQuery Era (2006)

Key Innovations

  • Cross-browser compatibility
  • Simplified DOM manipulation
  • AJAX request handling
  • Impact: Made JavaScript development more consistent across browsers

3. Modern Framework Revolution

AngularJS (2010)

Key Features

  • Two-way data binding
  • Modular architecture
  • Dependency injection
  • Impact: Introduced structured approach to web applications

React (2013)

Key Innovations

  • Virtual DOM for optimized updates
  • Component-based architecture
  • Declarative syntax
  • Impact: Revolutionized UI rendering and state management

Vue.js (2014)

Key Features

  • Progressive framework
  • Reactive data binding
  • Simple template syntax
  • Impact: Made framework adoption more flexible

4. Modern Era Innovations

Angular 2+ (2016)

  • Component-based architecture
  • TypeScript integration
  • Improved scalability
  • Impact: Enterprise-grade framework capabilities

WebAssembly (2017)

  • Near-native performance
  • Multi-language support
  • Impact: High-performance web applications

Svelte (2019)

Key Innovations

  • Build-time compilation
  • Minimal runtime
  • Small bundle size
  • Impact: Performance-focused framework

5. Recent Developments (2020-2023)

Vue 3 (2020)

  • Composition API
  • Improved performance
  • Better TypeScript support

Solid.js (2020)

  • Fine-grained reactivity
  • No Virtual DOM
  • High performance

Svelte 4 (2023)

  • Runes system
  • Improved performance
  • Better JavaScript ecosystem integration

6. Future Directions

Performance Optimization

  • Build-time compilation
  • Minimal runtime overhead
  • Efficient state management

Developer Experience

  • Simplified syntax
  • Better tooling
  • Improved debugging

Scalability

  • Micro-frontend architecture
  • Better state management
  • Improved code organization

Maintainability

  • Type safety
  • Better testing capabilities
  • Improved documentation

7. Research Methodology

Primary Sources

  • Framework documentation
  • GitHub repositories
  • Official blogs

Secondary Sources

  • Stack Overflow surveys
  • Developer blogs
  • Technical articles

Data Collection

  • Usage statistics
  • Performance metrics
  • Developer satisfaction surveys
graph TD
%% Main Framework Evolution
A[UI Framework Evolution] --> B[Performance]
A --> C[Developer Experience]
A --> D[Scalability]
A --> E[Maintainability]

%% Performance Improvements
B --> B1[Build-time Optimization]
B --> B2[Runtime Efficiency]
B --> B3[Bundle Size]

%% Developer Experience Enhancements
C --> C1[Simpler Syntax]
C --> C2[Better Tooling]
C --> C3[Documentation]

%% Scalability Features
D --> D1[Component Architecture]
D --> D2[State Management]
D --> D3[Code Splitting]

%% Maintainability Aspects
E --> E1[Type Safety]
E --> E2[Code Organization]
E --> E3[Testing Support]