GPUintensive

GPUintensive describes software, tasks, or workloads that rely primarily on graphics processing units (GPUs) to perform the bulk of computation. In GPU-intensive workloads, the GPU executes parallel computations, often on large data sets or complex graphics tasks, while the central processing unit (CPU) handles orchestration and less parallel work. The term signals that performance is constrained more by the GPU's capabilities than by the CPU.

Typical GPU-intensive domains include real-time 3D rendering and ray tracing, video encoding and image processing, scientific simulations, and machine learning for training and inference. GPUs excel at data-parallel operations across thousands of cores, high memory bandwidth, and specialized units for matrix math, which makes these tasks substantially faster on GPUs than on CPUs.

Key metrics for GPU-intensive workloads are floating-point performance (FLOPS), memory bandwidth, and available VRAM, as well as power efficiency and thermals. Bottlenecks often arise from data transfer between CPU and GPU, PCIe latency, memory access patterns, or shader/compute unit occupancy, rather than raw compute power alone.

Developing GPU-intensive software typically involves GPU-accelerated APIs and libraries such as CUDA, OpenCL, Vulkan, DirectX, and Metal, plus domain libraries like cuDNN, cuBLAS, and ROCm. Optimization strategies include maximizing parallelism, improving memory coalescing, minimizing host-device data transfers, using appropriate precision, and batching work. Portability across vendors and platforms can be a consideration.

In practice, system designers select hardware with ample GPU compute capability, memory, and bandwidth for GPU-intensive workloads, balancing cost, power, and heat. Cloud and high-performance computing environments often host GPU-intensive tasks on dedicated GPUs or accelerator clusters.