These two goals are not mutually exclusive. The Co-dfns compiler is a compiler that is designed to self-host on the GPU, but it is a compiler for the Co-dfns language, which is a lexically scoped syntax in Dyalog APL. The compiler compiles dfns programs and supports Mac, Windows, and Linux, targeting CUDA, OpenCL, and Intel CPUs. The compiler itself is written as a dfns program, hence the idea of self-hosting on the GPU.
Target applications for the compiler include document processors, cryptographic libraries, neural networks programming, data analytics, high-performance web applications, vector databases, financial algorithms, bioinformatics, and HPC/Scientific computing tasks.
So, in short, yes, it compiles apps to run on the GPU, or the CPU, whichever you prefer. And yes, it is meant to compile itself on the GPU (so, you compile a GPU program on the GPU).
Target applications for the compiler include document processors, cryptographic libraries, neural networks programming, data analytics, high-performance web applications, vector databases, financial algorithms, bioinformatics, and HPC/Scientific computing tasks.
So, in short, yes, it compiles apps to run on the GPU, or the CPU, whichever you prefer. And yes, it is meant to compile itself on the GPU (so, you compile a GPU program on the GPU).