GPGPU

As explained in CSS in Depth, Second Edition

GPU acceleration is rendering computation performed by a graphics processing unit instead of primarily by the central processor.

GPU acceleration moves suitable rendering work from the main CPU to the graphics processing unit. Browsers can promote an element into its own paint layer, send that layer to the GPU, and composite it into the final image. section 16.3.1

The practical trade-offs are:

• Opacity and transform changes can avoid repeatedly repainting the main layer during animation. section 16.3.1
• Layer promotion can speed rendering, but additional layers consume more memory. section 16.3.1
• The benefit matters most for animations that update many times per second, not usually for one-time changes. section 16.3.1

Figure 16.12 The three stages of the rendering pipeline

Trace how layout, painting, and compositing combine painted layers into the displayed image. section 16.3.1

How CSS in Depth, Second Edition covers it

How deep it goes

Explains layers, repainting, compositing, and memory trade-offs, but stops short of implementation code.

Examples

Uses background-color changes, element resizing, and animated opacity or transforms as contrasting rendering cases.

What you can do after

Evaluate when layer promotion and GPU acceleration can improve animated rendering and when memory costs may outweigh benefits.

How it connects

Connects GPU acceleration with rendering pipelines, paint layers, compositing, transforms, opacity, and animation.

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Related concepts

• GPUuses · A graphics processing unit (GPU) is a processor designed for highly parallel computation, especially graphics and data-parallel workloads.
• FlashAttentionused by · FlashAttention is an exact, input-output-aware attention algorithm that tiles computation to reduce memory traffic and avoid materializing the full attention matrix in high-bandwidth memory.
• Numbaused by · Numba is a Python compiler that uses just-in-time compilation to generate optimized machine code for numerical code, with support for CPU and GPU targets.
• parallelismuses · Parallelism is the concurrent execution of multiple computational operations, often across multiple processing resources.
• CUDAproduced by · CUDA is NVIDIA’s parallel computing platform and programming model for performing general-purpose computations on NVIDIA GPUs.

This explainer was generated by Manning's knowledge graph from the text of CSS in Depth, Second Edition. The section links point to the parts of the book it draws on.