CUDAn

CUDAn is a hypothetical GPU programming framework inspired by CUDA, proposed in academic and industry discussions as a cross-vendor extension intended to standardize kernel programming and memory management across different accelerators. The term does not refer to an official standard or widely implemented toolkit, but to a design concept used to explore portability without sacrificing performance.

The core abstraction is a kernel function executed by many parallel execution contexts, organized into a grid of blocks (or teams). CUDAn preserves a C-like syntax and a host-device separation, with explicit qualifiers for device and host code. It introduces a portable memory hierarchy that includes global memory, fast on-chip memory, and caches, with guidance for memory coalescing and access patterns so code can run efficiently on diverse GPUs and other accelerators. Synchronization primitives and cooperative groups are supported at the kernel level to enable complex parallel algorithms.

At runtime, the system allocates device memory, handles transfers between host and device, and schedules kernels across available devices. The model emphasizes predictable latency and bandwidth, aided by memory hints, unified or explicit memory management, and optional dynamic parallelism for launching work from device code when supported.

A CUDAn compiler translates high-level kernel code to device-specific representations, accompanied by a host runtime that manages launches, synchronization, and resource allocation. Reference libraries for linear algebra, random number generation, and transforms are envisioned to support rapid development.

Status and reception: As a concept rather than a formal standard, CUDAn informs ongoing discussions about portability versus performance in GPU programming. It helps frame debates on abstraction levels, compiler optimizations, and cross-vendor interoperability.