The Nvidia CPUs are designed for a very specific use case. They are designed for high performance with less concern about cost control.

The newer AmpereOne CPUs use DDR5 with the AmpereOne M supporting even higher memory bandwidth. Even then, I doubt the AmpereOne CPUs will match the performance of the Nvidia Rubin CPUs. But the Ampere processors are available for general use. I am guessing that Nvidia is only going to sell the complete rack system and only to high-volume customers.

1. Very load overhead calling of native libraries. Wrapping native libraries from Java using JNI requires quite a bit of complex code, configuring the build system, and the overhead of the calls. So, most projects only use libraries written in a JVM-language -- the integration is not nearly as widespread as seen in the Python world. The Foreign Function and Memory (FFM) API is supposed to make this a lot easier and faster. We'll see if projects start to integrate native libraries more frequently. My understanding is that foreign function calls in Go are also expensive.

2. Doesn't require a VM. Java and C# require a VM. D (like Go) generate native binaries.

As such, D is a really great choice when you need to write glue code around native libraries. D makes it easy, the calls are low overhead, and there isn't much need for data marshaling and un-marshaling because the data type representations are consistent. D has lower cognitive overhead, more guardrails (which are useful when quickly prototyping code), and a faster / more convenient compile-debug loop, especially wrt to C++ templates versus D generics.

https://recsys.acm.org/

MCUs are lower power, have less overhead, and can perform hard real-time tasks. Most of what Arduino focuses on are MCUs. The equivalent is the Raspberry Pi Pico.

In my experience, the key thing is the library ecosystem for the C++ runtime environment. There are a large number of Arduino and third-party high-level libraries provided through their package management system that make it really easy to use sensors and other hardware without needing to write intermediate level code that uses SPI or I2C. And it all integrates and works together. The Pico C/C++ SDK is lower level and doesn’t have a good library / package management story, so you have to read vendor data sheets to figure out how to communicate with hardware and then write your own libraries.

It’s much more common for less experienced users to use MicroPython. It has a package management and library ecosystem. But it’s also harder to write anything of any complexity that fits within the small RAM available without calling gc.collect() in every other line.

https://en.wikipedia.org/wiki/Kernel_method

I find that I tend to package one-off tasks as containers as well. For example, create database tables and users. Compose supports these sort of things. Ansible actually makes it easy to use and block on container tasks that you don’t detach.

I’m not interested in running kubernetes, even locally.

https://en.wikipedia.org/wiki/Environment_Modules_(software)

Containerized apps need a lot of special boilerplate to determine how much CPU and memory they are allowed to use. It’s a lot easier to control resource limits with virtual machines because the application in the system resources are all dedicated to the application.

Orchestration of multiple containers for dev environments is just short of feature complete. With Compose, it’s hard to bring down specific services and their dependencies so you can then rebuild and rerun. I end up writing Ansible playbooks to start and stop components that are designed to be executed in particular sequences. Ansible makes it hard to detach a container, wait a specified time, and see if it’s running. Compose just needs to be updated to support management of shutting down and restarting containers, so I can move away from Ansible.

Services like Kafka that query the host name and broadcast it are difficult to containerize since the host name inside the container doesn’t match the external host name. Requires manual overrides which are hard to specify at run time because the orchestrators don’t make it easy to pass in the host name to the container. (This is more of a Kafka issue, though.)

Comments URL: https://news.ycombinator.com/item?id=42638275

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This is more about syntax differences. Even then, I'd be curious how well both languages accommodate themselves to teams and long term projects. In both cases, you will have multiple people working on parts of the code base. Are people able to read and modify code they haven't written -- for example, when fixing bugs? When incorporating new sub components, how well did the type systems prevent errors due to refactoring? It would be interesting to know if Haskell prevents a number of practical problems that occurred with OCaml or if, in practice, there was no difference for the types of bugs they encountered.

This blog post feels more like someone is comparing basic language features found in reviews for new users rather than sharing deep experience and gotchas that only come from long-term use.