WebGPU :: Javascript at the speed of Light | Highlights and Annotations by Gistr.

This video explores using WebGPU to dramatically speed up JavaScript applications. Three key techniques are demonstrated: leveraging WebGPU compute shaders for parallel processing, choosing the right tool for the job (WebGPU excels with parallelizable tasks, C++ with complex math), and understanding that data transfer between CPU and GPU significantly impacts performance. While WebGPU offers massive speedups for suitable algorithms (e.g., particle systems), it's not a universal solution; some algorithms (like depth-first search) aren't easily parallelized. works. When we get to the examples, the key in building a robust measurement tool is using both of these methods. at the same time. our goal for today is not only to win the exact speed battle, but also winning in big O. let's have a look at example number one. let's say that we have two array of numbers that have the same length. What we're going to do is just sum the numbers of these two arrays one by one. To do this, we're going to have a simple loop. and we're going to take the elements of both arrays with the same index, sum them, and store the result in another. array. Let's try to implement this algorithm using a web GPU, comput, shador comput shaders can be extremely fast. And the reason behind their speed is the architecture of the GPU. The GPU is designed to run tasks in parallel. A simple way to think about it is this CPU programs run sequentially, meaning if each CPU function is a car, this car needs to reach the destination before the next car is allowed to move a GPU program. however, is like a bunch of cars in a race. all of these cars start moving at the same time. And when all of these cars are done, we're done. And so the cost of calculating many functions is equal to the cost of calculating only one. Now in my previous video on web GPU, I left you with a lot of unanswered questions. Now Now there's an FAQ read me file in the description where I answer a lot of the questions you might have right now. Take a look after you watch this video. Web GPU offers two pipelines. The first one is the rendering pipeline, allowing you to render 2d and 3d scenes in real time. watch a previous video in the series to learn more about that pipeline. The second pipeline is called the compute pipeline. This pipeline is going to allow us to run compute cheaters on the GPU. But what exactly is a comput shader, a comput shader, is an imaginary grid of points, this grid can be 1D, 2d or 3d. And because we have one-dimensional arrays, in our example, we're going to have a one-dimensional computor. Each point has its own unique index and we're going to run a function for each one of these points. we're going to write this This segment delves into the practical application of Big O notation for performance analysis, comparing the performance of JavaScript loops and WebGPU compute shaders. It demonstrates how WebGPU's constant runtime speed for certain operations provides a significant advantage when dealing with massive datasets, even while acknowledging the overhead of data transfer between CPU and GPU. This segment explains the fundamental difference between CPU and GPU processing, highlighting how compute shaders leverage the GPU's parallel architecture to significantly accelerate calculations, particularly for large datasets. The explanation uses an analogy of cars in a race to illustrate the concept of parallel processing versus sequential processing, making the complex topic easier to grasp. It also briefly mentions a follow-up FAQ to address further questions. This segment explores the limitations of WebGPU by comparing the performance of different maze generation algorithms. It highlights that not all algorithms are suitable for parallel processing on the GPU. The segment contrasts the performance of a depth-first search (DFS) algorithm (not parallelizable) with a binary tree algorithm (parallelizable) implemented in JavaScript, C++, and WebGPU, emphasizing the importance of algorithm design for optimal WebGPU performance and the situations where C++ might be a better choice. This segment presents a performance comparison of different programming languages and approaches for computationally intensive tasks, specifically using Perlin noise generation. It contrasts the performance of JavaScript, Rust (compiled to WebAssembly), C++, and WebGPU, showcasing WebGPU's superior performance for large-scale computations, even when considering the cost of data transfer between the CPU and GPU. The video focuses primarily on WebGPU as the main technique for speeding up JavaScript applications. While three techniques aren't explicitly listed, the core idea revolves around leveraging the GPU's parallel processing capabilities for computationally intensive tasks, achieving significant speed improvements over traditional CPU-bound JavaScript. The video also implies optimization of functions and algorithms as important supporting factors for performance gains. we're going to run this computor for n amount of points n is the length of our arrays So the index that we're going to get is going to be from zero to n using this index we're going to read the data from the first array and also the second array sum these two numbers and store the result at the end of the function. When our Comput shter is done computing we need to send this data back to the CPU. With the cost of this in mind the speed comparison is going to look like this and again we're slower than normal JS. So we've learned an important lesson.