Portable GPU frameworks such as Kokkos and RAJA reduce the burden of cross-architecture development but typically incur measurable overhead on fundamental parallel primitives relative to vendor-optimized libraries. We present KernelForge.jl, a Julia library that implements scan, mapreduce, and matrix-vector primitives through a two-layer portable architecture: KernelIntrinsics.jl provides backend-agnostic abstractions for warp-level shuffles, memory fences, and vectorized memory access, while KernelForge.jl builds high-performance algorithms exclusively on top of these interfaces. Evaluated on an NVIDIA A40 and an AMD MI300X, KernelForge.jl matches or exceeds CUB kernel execution time on scan and mapreduce on the A40, and matches cuBLAS throughput on matrix-vector operations across most tested configurations-demonstrating, as a proof of concept, that portable JIT-compiled abstractions can achieve vendor-level throughput without sacrificing generality.

翻译：可移植GPU框架（如Kokkos与RAJA）虽能降低跨架构开发的负担，但在基本并行原语上通常相对于厂商优化库产生可测量的开销。我们提出KernelForge.jl——一个Julia库，通过双层可移植架构实现扫描、映射归约及矩阵向量原语：KernelIntrinsics.jl提供跨后端抽象的线程束级洗牌、内存栅栏与向量化内存访问接口，而KernelForge.jl则完全基于这些接口构建高性能算法。在NVIDIA A40与AMD MI300X上的评估表明，KernelForge.jl在A40上的扫描与映射归约核函数执行时间可与CUB持平或更优，在多数测试配置下的矩阵向量运算吞吐量可媲美cuBLAS——这作为概念验证表明，可移植的即时编译抽象能在不牺牲通用性的前提下实现厂商级吞吐量。