You could say yes, because the C# benchmark code is utilizing vector extensions on the CPU while Go's isn't. But you could also say no: Both are running on the same hardware (CPU and RAM). C# is simply using that hardware more efficiently here because the capabilities are exposed via the standard library. There is no magic trick involved. Even cheap consumer CPUs have had vector units for decades.

For one, the C# code is explicitly using SIMD (System.Numerics.Vector) to process blocks, whereas Go is doing it scalar. It also uses a read-only FrozenDictionary which is heavily optimized for fast lookups compared to a standard map. Parallel.For effectively maps to OS threads, avoiding the Go scheduler's overhead (like preemption every few ms) which is small but still unnecessary for pure number crunching. But a bigger bottleneck is probably synchronization: The Go version writes to a channel in every iteration. Even buffered, that implies internal locking/mutex contention. C# is just writing to pre-allocated memory indices on unrelated disjoint chunks, so there's no synchronization at all.

The focus on "100k threads" and GC overhead is a red herring. The real win isn't spawning a massive number of threads, but automatically yielding on network I/O, like e.g. goroutines do. In an I/O bound web application, you'd have a single virtual thread handling the whole request, just like a goroutine does. The GC overhead caused by the virtual thread is minuscule compared to the heap allocations caused by everything else going on in the request. If you really have a scenario for 100k virtual threads, they would not be short lived.

> But if they access limited resources (database, another HTTP service), etc. in real application you face the standard issue: you cannot hit the targeted system with any data you want

Then why would you do it? That sounds like an architectural problem, not a virtual thread problem. In an actor system, for example, you wouldn't hit the database directly from 100k different actors.

> The good thing in reactive programming is that it does not try to pretend that above problem does not exist.

This compares a high-level programming paradigm, complete with its own libraries and frameworks, to a single, low-level concurrency construct. The former is a layer of abstraction that hides complexity, while the latter is a fundamental building block that, by design, does not and cannot hide anything.

> It forces to handle errors, to handle backpressure, as those problems will not magically disappear when we switch to green threads, lightweight threads, etc.

Synchronous code handles errors in the most time-tested and understandable way there is. It is easy to reason about and easy to debug. Reactive programming requires explicit backpressure handling because its asynchronous nature creates the problem in the first place. The simplest form of "backpressure" in synchronous code with a limited amount of threads is the act of blocking. For anything more than that, there are the classic tools (blocking queues, semaphores...) or higher-level libraries built on top of them.

Or imagine those functions are part of the immutable record and create new instances. The aspect of (im)mutability is orthogonal to where you place your logic. In the context of domain models, if the logic is an inherent part of the type and its domain, then there are good reasons to model the logic as part of the type, and those have nothing to do with Java or the typical OOP dogma (Rust chrono: `let age = today.years_since(birthday)` - Yes, you could argue that the logic is part of the trait implementation, but the struct's data is still encapsulated and correct usage of the type is enforced. There is only one way to achieve this in Java.)

They deliberately took the longer route, aiming to integrate lightweight threads in a way that doesn't force developers to change their existing programming model. No need for callbacks, futures, coroutines, async/await, whatever. This required a massive effort under the hood and rework to many core APIs. Even code compiled with decade old Java versions can run on virtual threads and benefit, without any refactoring or recompilation.

> ...and/or async/await revolution of the last decade

async/await is largely syntactic sugar. Java has had the core building blocks for asynchronous programming for years, with CompletableFuture (2014, replacing the less flexible Future introduced in 2004) and NIO.2 (2011, building on the original NIO from 2002) for non-blocking I/O, along with numerous mature libraries that have been developed on top of them over time.

What makes you think that? Having a large number of actors per thread is by far the most important use case. The Actor model is commonly used in communication systems where there are hundreds of thousands of actors per machine (often one for every single user). In this context, Actors are typically extremely lightweight and not CPU-bound. Instead, they mostly focus on network I/O and are often idle, waiting for messages to arrive or be sent.

Is this an actual issue? Most people don't seem to care when talking about random UUIDs. The target platform of our applications is mostly Kubernetes on cloud environments, if that makes any difference.

Why I'm asking: UUID Version 7 looks quite interesting to me, and the document describes rand_a and rand_b just as "pseudo-random data"... which made me think that in the context of "uniqueness per millisecond", a source of entropy is conceptually not required. However, chapter 6.6 clearly advises the usage of CSPRNGs, so I guess the overall problem remains :(

From https://en.wikipedia.org/wiki/BioNTech:

It develops pharmaceutical candidates based on messenger ribonucleic acid (mRNA) for use as individualized cancer immunotherapies, as vaccines against infectious diseases and as protein replacement therapies for rare diseases, and also engineered cell therapy, novel antibodies and small molecule immunomodulators as treatment options for cancer.