GPGPU

GPGPU, short for general-purpose computing on graphics processing units, is the use of GPU hardware and software to perform computations beyond graphics rendering. GPUs offer many parallel cores and high memory bandwidth, which can deliver major speedups for data-parallel tasks. Not all workloads benefit, however; tasks with little parallelism or strict low-latency requirements may run better on CPUs.

GPU programming evolved from graphics pipelines to general computation with interfaces such as CUDA (NVIDIA) and OpenCL (Khronos). Other options include DirectCompute and Metal, with ROCm by AMD providing alternative runtimes. These frameworks allow developers to write kernels that execute on many GPU cores, enabling scalable acceleration across diverse hardware.

Common domains include scientific simulations, linear algebra, image and video processing, and machine learning. Deep learning in particular relies on GPUs for training and inference, due to large-scale parallelism and fast matrix operations. Performance depends on memory bandwidth, numerical precision (FP16, FP32, FP64), and the overhead of transferring data between host and device.

Key considerations for GPGPU programming include the GPU’s memory hierarchy, bandwidth, and occupancy, as well as portability across devices. Effective use requires algorithms suited to parallel execution and optimized data access patterns. When applied appropriately, GPGPU can substantially accelerate compute-heavy workloads on desktops, servers, and embedded systems.