GPU Computing

Short Definition

GPU Computing is the use of Graphics Processing Units for general-purpose parallel computation, particularly for training and running deep learning models. GPUs contain thousands of cores optimized for simultaneous matrix operations, making them essential hardware for modern artificial intelligence workloads.

Full Definition

Graphics Processing Units have become the dominant hardware platform for artificial intelligence, transforming from specialized graphics rendering chips into general-purpose parallel computing engines. Modern AI training involves massive tensor computations — matrix multiplications, convolutions, and element-wise operations — that map naturally to GPU architecture. A single modern GPU contains thousands of cores that can execute thousands of threads simultaneously, achieving orders of magnitude higher throughput than CPUs for parallel workloads. NVIDIA’s CUDA programming platform and libraries like cuDNN created the software ecosystem that made GPUs the standard for deep learning. Today, training large language models like GPT-4 requires clusters of thousands of high-end GPUs working together, representing investments of hundreds of millions of dollars in compute infrastructure. The GPU computing landscape has expanded beyond NVIDIA to include AMD’s ROCm ecosystem and Intel’s oneAPI, though NVIDIA maintains dominant market share in AI. Specialized AI accelerators like Google’s TPUs and custom chips from Amazon and Microsoft are emerging as alternatives. The demand for GPU computing power has driven a global shortage affecting chip markets, data center construction, and the pace of AI research. Understanding GPU computing is essential for anyone working with modern AI systems.

Technical Explanation

A modern GPU like NVIDIA H100 contains over 18,000 CUDA cores executing thousands of threads simultaneously using Single Instruction Multiple Thread (SIMT) architecture. Key operations accelerated by GPUs include General Matrix Multiply (GEMM), convolutions, and mixed-precision training using Tensor Cores that operate on FP16, BF16, or FP8 formats. Multi-GPU training uses data parallelism (splitting batches across GPUs), model parallelism (splitting layers across GPUs), and pipeline parallelism (splitting sequential layers). High-speed interconnects like NVLink (900 GB/s) and InfiniBand minimize communication overhead. Memory hierarchy includes HBM (High Bandwidth Memory) for main GPU memory and shared memory for fast intra-block communication.

Use Cases

Training large language models | Deep learning model training | Computer vision inference | Scientific simulations | Cryptocurrency mining | Rendering and graphics | Genomics and drug discovery | Financial modeling

Advantages

Massive parallelism for matrix operations | Orders of magnitude faster than CPUs for AI | Mature ecosystem with CUDA and cuDNN | Excellent scalability with multi-GPU clusters | Continuous hardware improvements | Broad software framework support

Disadvantages

High power consumption and cooling requirements | Extremely expensive hardware | Requires specialized programming knowledge | Limited memory per single GPU | Global supply shortages | Vendor lock-in with CUDA ecosystem

Related Terms

GPU Computing

Primary Keyword

GPU Computing

Schema Type

DefinedTerm

Last Verified Date

17/04/2026

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Difficulty Level

Beginner