I've primarily been focused on the ergonomics of the language, so I've only tried to make performance "reasonable" for now. Longer-term performance objectives

I've primarily been focused on the ergonomics of the language, so I've only tried to make performance "reasonable" for now. Longer-term performance objectives are: 1. JIT - I designed the VM's byte code to be both interpretably and a mid-level IR to LLVM. Currently I just interpret everything since there is almost no runtime overhead for vector operations. However, compiled code will greatly speed-up any scalars in a loop. 2. SIMD - Since the VM's opcodes are already statically typed and vector-aware, integrating OpenBLAS and SLEEF (or Intel's MKL and VML) should be straightforward. 3. MIMD - Ideally I can just lean on existing libraries, though I'm not above embedding OpenMP if that gets the job done. 4. Distributed - Now comes the hard part. If we want MPI-level performance, I need to have more sophisticated scheduling. Which leads us to... 5. Streaming - This is the real holy grail. There has been a ton of research in the database community to get away from the "Volcano model" (iterators). I want to have the compiler generate streaming-aware opcodes for the VM based on the nature of how the data is to be consumed. I believe this will require a type system that can track the "context" of the computation, similar to how Koka and F* track side effects. I'm not aware of any general-purpose language that has compiled streaming.