The use of graphics processing hardware to speed up computation.
GPU acceleration uses graphics processing hardware to make computation faster. In deep learning, PyTorch exposes this capability through its NumPy-like tensor API. section 1.0section 1.5.1
CSS in Depth, Second EditionGPU acceleration is rendering computation performed by a graphics processing unit instead of primarily by the central processor.
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 Inside Deep Learning: Math, Algorithms, Models. The section links point to the parts of the book it draws on.
