Lua is more dynamic than Wren which makes its job harder. Lua also tries very hard to be compatible across a wide range of hardware and compilers. If you have a C89 compiler for it, odds are very good that you can run Lua on it. Wren cares about compatibility, but it requires C99 or C++98 and IEEE double precision floats. That may exclude some edge case hardware, but makes things like NaN tagging, computed gotos, and some other tricks possible.

Languages come in four rough performance buckets, from slowest to fastest

This makes questions like “how fast is WebAssembly” a bit hard to answer. You don’t ask how fast algebraic notation is—it’s not a very sensible question. Taken in the context of something like JavaScript, the language is only as fast as the engine running it. JavaScript the language has no speed, but you can benchmark JS engines like V8, SpiderMonkey, and JavaScriptCore. You can benchmark the IO libraries of JS runtimes like Bun, Deno, and Node. What people actually mean is “how useful are the constructs of this language to efficient mappings of modern hardware” and “what is the current landscape of systems taking advantage of these constructs”.

Vite's job is to get your source modules into a form that can run in the browser as fast as possible. To that end, we recommend separating static analysis checks from Vite's transform pipeline.

Vite uses esbuild to transpile TypeScript into JavaScript which is about 20~30x faster than vanilla tsc, and HMR updates can reflect in the browser in under 50ms.

Above: the time to do a production bundle

Simply choosing a more efficient programming language speeds up this calculation

If one profiles what is going on in this whole process, it becomes clear that I/O dominates. Of the cpucycles expended, most go into conversion to and from intermediate file formats.

A more efficient but more complicated way to simulate perfect guessing is to guess both options simultaneously

Serializing the data with a function specialized to your data shape can be more than 10x compared with JSON.stringify.

I tried precompiling the JavaScript code to QuickJS bytecode (to avoid parsing overhead), but that only saved about 40 milliseconds (I guess parsing is really fast!).

a factor of 10 did go into faster responses to the user’s actions

only a small fraction of the features of each component, and your program con-sumes 10 or 100 times the hardware resources of a fully custom program, butyou write 10% or 1% of the code you would have written 30 years ago.

If you write an algorithm in a straightforward way in Node, you can expect it to run about as fast as if you write it in a vectorized way using Numpy, or twenty times as fast as if you write it in a straightforward way in CPython.

current JavaScript tools could be faster than they are today

Rust lets us explicitly state our desires to the compiler

The lack of CPU power in those days also meant there was deep skepticism about the performance of interpreters in general, and in the user interface in particular. Mention "interpreted language" and what sprung to mind was BASIC or UCSD Pascal, neither regarded as fast.

The more I think about it, the less I think there is a meaningful definition of the one true run time. I have put significant effort into making sure that runtimes are consistent but, however we do this, it makes our experiments less realistic. With rare exceptions (perhaps some safety critical systems) our algorithms will not be used in highly controlled situations, moderated to reduce variability between runs or to maximise speed of this particular process. Algorithms will be run as part of a larger system, in a computer sometimes with many other processes and sometimes with few and, because of the way that computing has developed, sometimes in raw silicon and sometimes in virtual machines. So the more I think about it, the more I think that what matters is the distribution of run times. For this, your experiment on your hardware is important, but so are future recomputations on a wide variety of VMs running on a variety of different hardware.

If I can’t run your experiment at all, then I can’t reproduce your times.

Another key idea here is to separate meaning from tactics. E.g. the meaning of sorting is much simpler and more compact than the dozens of most useful sorting algorithms, each one of which uses different strategies and tactics to achieve the same goal. If the “meanings” of a program could be given in a way that the system could run them as programs, then a very large part of the difficulties of program design would be solved in a very compact fashion. The resulting “meaning code” would constitute a debuggable, runnable specification that allows practical testing. If we can then annotate the meanings with optimizations and keep them separate, then we have also created a much more controllable practical system.

The law of the "Wild West of Programming" was still held in too high esteem! The same inertia that kept many assembly code programmers from ad- vancing to use FORTRAN is now the principal obstacle against moving from a "FORTRAN style" to a structured style.

The amount of resistance and prejudices which the farsighted originators of FORTRAN had to overcome to !gMn acceptance of their product is a memorable indication of the degree to which programmers were pre- occupied with efficiency, and to which trick- ology had already become an addiction

I just dislike how non-native it feels and looks.

This still would not eliminate all delay, but I think this could be faster (no render blocking), and cleaner than having inline scripts scattered all over the parent document.

it's much faster—the stack frame does not have to be carried along the "thrown symbol", and no object is created. Lightweight nonlinear flow control.

You can return the record directly from the UPDATE, which is much faster than calling an additional SELECT statement. Use RETURN QUERY and UPDATE with a RETURNING clause.

You could also use the NodeIterator API, but TreeWalker is faster

All those 'modern' and 'super-modern' querySelectorAll("*") need to process all nodes and do string comparisons on every node.

the fastest solution because the main workload is done by the Browser Engine NOT the JavaScript Engine

Just saying “snaps are slow” is not helpful to anyone. Because frankly, they’re not. Some might be, but others aren’t. Using blanket statements which are wildly inaccurate will not help your argument. Bring data to the discussion, not hearsay or hyperbole.

I don’t find the software slow, I find the startup time for snap packages when the start for the first time on a session slow, but that has been improved, and it’s public that the snapcraft team has been working hard to improve that.

The template language's restrictions compared to JavaScript/JSX-built views are part of Svelte's performance story. It's able to optimize things ahead of time that are impossible with dynamic code because of the constraints. Here's a couple tweets from the author about that

Because Jamstack projects don’t rely on server-side code, they can be distributed instead of living on a single server. Serving directly from a CDN unlocks speeds and performance that can’t be beat. The more of your app you can push to the edge, the better the user experience.

Better PerformanceWhy wait for pages to build on the fly when you can generate them at deploy time? When it comes to minimizing the time to first byte, nothing beats pre-built files served over a CDN.

It's fast. The Dart VM is highly optimized, and getting faster all the time (for the latest performance numbers, see perf.md). It's much faster than Ruby, and close to par with C++.

Virtual DOM is valuable because it allows you to build apps without thinking about state transitions, with performance that is generally good enough

In the vast majority of cases there’s nothing wrong about wasted renders. They take so little resources that it is simply undetectable for a human eye. In fact, comparing each component’s props to its previous props shallowly (I’m not even talking about deeply) can be more resource extensive then simply re-rendering the entire subtree.

If you need to call the function repeatedly, this is much, much faster than using eval.

For example, to search for text occurrences, I used ack-grep. Later on, I found that there is a faster approach using ag. Then, there is an even faster alternative called ripgrep.

Speed example -- over 829 files, the "find -exec" method took 26 seconds while the "find -print0 | xargs --null" method tool 0.7 seconds. Significant difference.

Building blocks that ensure performance It takes a lot of time and effort to build a great website. AMP components are already optimized for the best performance.

uses a pre-clone step to seed the project with a recent archive of the repository. This is done for several reasons: It speeds up builds because a 800 MB download only takes seconds, as opposed to a full Git clone.

GIT_DEPTH: '1' helps a little bit but we could speed up our pipelines massively for jobs that only rely on an image that is the sole dependency to run the job.

“I feel like I’m just talking HTTP at last, with nothing in the middle. Falcon seems like the requests of backend.”

It is also good practice to make sure that your load testing is functionally correct. Both the performance and functional goals can be codified using thresholds and checks (like asserts).

This is especially true for online gaming