GPUprogrammering

GPU programming refers to writing software that executes on graphics processing units (GPUs) to perform general-purpose computation beyond traditional graphics rendering. GPUs contain thousands of cores designed for data-parallel execution, making them well suited for workloads such as linear algebra, image and video processing, Monte Carlo simulations, and machine learning.

Most GPU programming today relies on APIs that expose compute capabilities. CUDA (NVIDIA), OpenCL (vendor-neutral), Metal (Apple), Vulkan Compute, and DirectCompute enable writing kernels that run on the device and launching them from host code. The execution model typically uses a large grid of threads organized into blocks or workgroups, with a memory hierarchy that includes global memory, fast on-chip shared (scratchpad) memory, constant memory, and texture memory. Performance depends on memory bandwidth and effective occupancy of the compute units.

Key programming concepts include data-parallel kernel design, coalesced memory accesses, avoiding branch divergence within a warp or wavefront, minimizing transfers between host and device, and overlapping computation with data transfers using streams or command queues. Debugging and profiling tools—such as NVIDIA Nsight, AMD ROCm tools, Intel VTune, and IDE-integrated profilers—aid optimization and correctness.

Applications span graphics processing and real-time rendering, image and video processing, scientific simulations, and large-scale machine learning workloads. The field continues to evolve toward heterogeneous and portable computing, with ongoing refinements in parallel programming models, memory hierarchies, and cross-device compatibility.