Prospero challenge with ejit

This is my implementation of the prospero challenge using ejit.

Note that as of 2025-04-05, I've implemented some optimizations int ejit that weren't part of the original submission. Results from the original submission can be found in README.old.md.

As a quick introduction, here's an excerpt from the ejit README:

ejit is a generic JIT/bytecode interpreter with a strong focus on compilation speed and portability. It's built on top of the excellent lightening pure JIT library, but adds a bytecode layer for systems that lightening doesn't support. The bytecode layer also grants some QoL improvements, such as an (effectively) unlimited number of virtual registers and a register allocator for mapping theses virtual registers down to physical registers and a slightly easier to use API (at least IMO).

ejit is primarily inteded to be used as the main execution engine for experimental/toy scripting languages, so the prospero challenge is arguably a bit outside the intended use case of ejit, it not really being designed with relatively heavy computation in mind. Still, this was a good opportunity to see just how well/poorly the library can handle such tasks.

Running

git submodule update --init --recursive
make

The produced binary has a few command line parameters:

usage: prospero [-j threads] [-r resolution] [-c]
 -j how many threads to run
 -r length of one side
 -c enable JIT

By default, the binary runs a single worker thread, produces an image of size 1024x1024 and does not enable the JIT part of ejit.

Results

Here are some runtime results for when the JIT compiler is enabled:

I was also curious to see how the bytecode interpreter fared, since the main focus of ejit is for the JIT to be efficient, and the bytecode is mainly as a backup for more obscure hardware. Here's are some runtime results for when the JIT compiler is disabled:

For reference, I ran Aviva Ruben's Cranelift implementation which finished in 0.241 seconds when using all threads (I believe into_par_iter() uses threads, anyway...). Very impressive! Cranelift is particularly cool in that it exposes SIMD instructions, which ejit does not have.

To roughly compare the compilation speed of ejit with JIT enabled vs. Cranelift, setting the resolution of the generated image to 1 gives the following table:

This runtime is not quite the same as the compilation time, but the compilation should be the dominating factor in this case. There's a fairly obvious tradeoff here, Cranelift can produce much faster code (especially if you can take advantage of SIMD operations), whereas ejit can produce machine code much faster. This is what I was expecting, but it's nice to see some (semi)hard numbers.

To cheat a bit, I also tried compiling a version of the Cranelift implementation that didn't use SIMD (commit 55090c86e2d1a25b86a8e1f8d0e15a10b9c4f5b7) and didn't use parallelism (into_par_iter() seemed to have a bit more overhead when dividing up work than my implementation), and the Cranelift implementation finished in 4.079 seconds. Compared to my -j1 -c case of 3.149 seconds, I'm fairly pleased with the results. I'm hoping that I'm doing an apples-to-apples comparison here, and that the optimizations I did to ejit and lightening since first submitting this implementation are useful for other tasks as well, and aren't just me trying to one-up Cranelift.

Optimizations done since initial submission

Probably most significantly, I implemented the sqrt, fmax and fmin operations. Previously they were either branches or an actual function call in the case of sqrt, now they're done inline in machine code.

I also improved the register allocator somewhat. Previously each variable was mapped to a fixed location, either to a register or a stack slot, and 'locked' that location for the lifetime of the procedure being generated. I implemented a basic lifetime check, and now locations that don't overlap temporally can be placed in the same slot. I also sort the slots according to a calculated spill cost, so the slot with a higher total spill cost is more likely to be placed into a register. The code given for this challenge is in a kind-of-ssa form, which the register allocator is fairly well suited for, but I'm hoping that it also works well for more stack-like access patterns, like in posthaste (because stack-like is easier to implement in a compiler than SSA-like). More testing would be useful, though.

Limitations of ejit for this particular challenge

Not having SIMD instructions hurts performance significantly. I've been thinking about how SIMD could be implemented, but nothing concrete yet. There's a lot more variability in SIMD implementations than base ISAs, and it seems to be rather difficult to be generic over any large-ish portion of them. sljit takes the approach that the user can place custom instructions into the instruction stream and implement SIMD for specific platforms, which is cool, but is kind of at odds with the 'portability' aspect of ejit that I would like to maintain as far as possible.