GPUfriendly

GPUfriendly is a term used to describe software, APIs, libraries, or hardware designs that are optimized to run efficiently on graphics processing units. It is not an official standard, but a descriptive label indicating that the code and workflows take advantage of the parallel architecture and memory hierarchy of GPUs. GPU-friendly software typically targets general-purpose GPU (GPGPU) computing as well as graphics pipelines.

Key characteristics include exposing substantial data parallelism, using kernels or shader programs that execute across many threads, arranging data to improve memory coalescing, and minimizing CPU-GPU round-trips. Effective GPU-friendly design also emphasizes asynchronous execution, overlapping computation with data transfer, and careful use of the GPU memory hierarchy (global memory, shared memory, caches) to reduce latency.

Common domains include scientific computing, machine learning, computer graphics, video encoding/decoding, and image processing. Popular tools and APIs that support GPU-friendly development include CUDA, OpenCL, Vulkan and DirectX compute shaders, Metal, and high-level frameworks such as TensorFlow, PyTorch, or OpenCV with GPU acceleration. Portability considerations arise because GPU architectures differ among vendors (NVIDIA, AMD, Intel).

Benefits and challenges: GPU-friendly software can deliver high throughput and energy efficiency for suitable workloads, but achieving performance requires specialized knowledge of parallel programming, memory access patterns, and hardware limits. Debugging and maintaining GPU-optimized code can be more complex, and performance portability is not guaranteed across different GPUs without tuning.