GPGPU
GPGPU is the use of graphics processing units to perform general-purpose computations, often by exploiting their parallel processing capabilities.
Fast Python: High performance techniques for large datasets
- How it is used
- Accelerates data analysis by assigning similar operations on many independent array or matrix elements to GPU processing units.
- How deep it goes
- Develops per-element Numba kernels, launch dimensions, indexing, and bounds checks; defers algorithms that exploit fast shared state within blocks.
- Examples
- Uses array doubling, matrix indexing, a CPU-versus-GPU latency calculation, and a Mandelbrot generator as implementation examples.
- What you can do after
- Build it from scratch with Numba by writing a per-element GPU kernel, configuring its launch, and checking its output.
Read how Fast Python: High performance techniques for large datasets explains GPGPU →
CSS in Depth, Second Edition
GPU acceleration is rendering computation performed by a graphics processing unit instead of primarily by the central processor.
- How it is used
- Explains why transforms and opacity can make animated interface rendering faster.
- 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.
Inside Deep Learning: Math, Algorithms, Models
The use of graphics processing hardware to speed up computation.
- How it is used
- Speeds deep-learning computation through PyTorch’s tensor-based programming interface.
- How deep it goes
- Introduces GPU acceleration as a PyTorch capability but does not detail hardware internals or device-management steps.
- Examples
- Uses Google Colab and PyTorch’s tensor API as the practical setting for faster code.
- What you can do after
- Apply it via PyTorch’s tensor API when running deep-learning computations.
Read how Inside Deep Learning: Math, Algorithms, Models explains GPGPU →
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.
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