JuliaInterpreter received numerous performance enhancements, and now can run step-wise through code at roughly 50× its original speed. These optimizations reduce—but come nowhere close to eliminating—the most serious disadvantage of running all code in the interpreter: slow performance. It is hoped that the performance gap between compiled and interpreted code, which can be many orders of magnitude, will narrow in the coming months. However, the interpreter will always be slower than compiled code.
It’s also worth noting that there are cases where the interpreter feels faster, at least on initial execution. Julia’s JIT compiler produces excellent results, but all that code-analysis takes time; there is interest in exploring whether running more code in the interpreter could reduce latency, a.k.a. the “time to first plot” problem. JuliaInterpreter is a potential tool for exploring that trade off, and it appears that not much additional work would be needed.
Oh wow, this is great. I've really enjoyed what little toying around with Julia I've done, but it seemed just shy of feeling truly interactive to me. But I really do love the featureset of the language, so if this gets integrated into the language and it helps in time to interactivity, I'll absolutely be moving some parts of my work to Julia.
More importantly to me, Julias combination of features means code is not very self documenting and error messages are (often) terrible.
If I need to understand what subtle assumption of DifferentialEquations I violated stepping into the point of failure is God sent. And that's a well documented package.
More importantly, I now can usually reason my way around. But my students can use this to learn what goes wrong in their codes interaction.
This is a massively important step in the tooling and ecosystem. Congratulations to the developers.
Comments
Oh wow, this is great. I've really enjoyed what little toying around with Julia I've done, but it seemed just shy of feeling truly interactive to me. But I really do love the featureset of the language, so if this gets integrated into the language and it helps in time to interactivity, I'll absolutely be moving some parts of my work to Julia.
More importantly to me, Julias combination of features means code is not very self documenting and error messages are (often) terrible.
If I need to understand what subtle assumption of DifferentialEquations I violated stepping into the point of failure is God sent. And that's a well documented package.
More importantly, I now can usually reason my way around. But my students can use this to learn what goes wrong in their codes interaction.
This is a massively important step in the tooling and ecosystem. Congratulations to the developers.