papers-we-love VS compiler-explorer

Papers We Love (PWL) is a community built around reading, discussing and learning more about academic computer science papers. This repository serves as a directory of some of the best papers the community can find, bringing together documents scattered across the web. You can also visit the Papers We Love site for more info.

You might find the paper Out of the Tar Pit interesting if you haven't already read it: https://github.com/papers-we-love/papers-we-love/blob/main/d...

The ideas and approaches you talk about evoked some of the concepts from that paper for me. It talks a lot about separating accidental complexity and infrastructure so you can focus only on what is essential to define your solutions.

Sure he was expecting a practical language and was designing one. Lisp was from day zero a project to implement a real programming language for a computer.

Earlier he experimented with IPL and also list processing programming on Fortran. The plan was to implement a Lisp compiler. At first the Lisp code McCarthy was experimenting with, was manually translated to machine code.

Then came up the idea to use EVAL as a base for an interpreter, which was implemented by manually translating the Lisp code to machine language. Around 1962 then a compiler followed.

https://github.com/papers-we-love/papers-we-love/blob/main/c...

I'm in a 4th camp: we should be writing our applications against a relational data model and _not_ marshaling query results into and out of Objects at all.

Elaborations on this approach:

- https://github.com/papers-we-love/papers-we-love/blob/main/d...

- https://riffle.systems/essays/prelude/

The short ask: Anyone know of any projects that bring incremental view maintenance to SQLite?

The why:

Applications are usually read heavy. It is a sad state of affairs that, for these kinds of apps, we don't put more work on the write path to allow reads to benefit.

Would the whole No-SQL movement ever even have been a thing if relational databases had great support for materialized views that updated incrementally? I'd like to think not.

And more context:

I'm working to push the state of "functional relational programming" [1], [2] further forward. Materialized views with incremental updates are key to this. Bringing them to SQLite so they can be leveraged one the frontend would solve this whole quagmire of "state management libraries." I've been solving the data-sync problem in SQLite (https://vlcn.io/) and this piece is one of the next logical steps.

If nobody knows of an existing solution, would love to collaborate with someone on creating it.

[1] - https://github.com/papers-we-love/papers-we-love/blob/main/design/out-of-the-tar-pit.pdf

Here is a great Repo on GitHub named paers-we-love. You will surely find some great papers there and also some good other resources. Hope this helps.

However, f and g are interchangeable anywhere else (this is not actually true because their addresses can be obtained and compared; showing that a C-like language retains its referential transparency despite the existence of so-called l-values was the point of what I think is the first paper to introduce the notion referential transparency to the study of programming languages: https://github.com/papers-we-love/papers-we-love/blob/main/l...)

You said You won't get "extreme performance" from C++ because it is buried under the weight of decades of compatibility hacks.

Now your whole comment is about vector behavior. You haven't talked about what 'decades of compatibility hacks' are holding back performance. Whatever behavior you want from a vector is not a language limitation.

You could write your own vector and be done with it, although I'm still not sure what you mean, since once you reserve capacity a vector still doubles capacity when you overrun it. The reason this is never a performance obstacle is that if you're going to use more memory anyway, you reserve more up front. This is what any normal programmer does and they move on.

Show what you mean here:

https://godbolt.org/

I've never used ISPC. It's somewhat interesting although since it's Intel focused of course it's not actually portable.

I guess now the goal posts are shifting. First it was that "C++ as a language has performance limitations" now it's "rust has a vector that has a function I want and also I want SIMD stuff that doesn't exist. It does exist? not like that!"

Try to stay on track. You said there were "decades of compatibility hacks" holding back C++ performance then you went down a rabbit hole that has nothing to do with supporting that.

C++ Insights is available online at https://cppinsights.io/

It is also available at a touch of a button within the most excellent https://godbolt.org/

along side the button that takes your code sample to https://quick-bench.com/

Those sites and https://cppreference.com/ are what I'm using constantly while coding.

I recently discovered https://whitebox.systems/ It's a local app with a $69 one-time charge. And, it only really works with "C With Classes" style functions. But, it looks promising as another productivity boost.

[P&H RISC] https://www.google.com/books/edition/_/e8DvDwAAQBAJ

Compiler Explorer by Matt Godbolt [Godbolt] can help better understand what code a compiler generates under different circumstances.

[Godbolt] https://godbolt.org

The official CPU architecture manuals from CPU vendors are surprisingly readable and information-rich. I only read the fragments that I need or that I am interested in and move on. Here is the Intel’s one [Intel]. I use the Combined Volume Set, which is a huge PDF comprising all the ten volumes. It is easier to search in when it’s all in one file. I can open several copies on different pages to make navigation easier.

Intel also has a whole optimization reference manual [Intel] (scroll down, it’s all on the same page). The manual helps understand what exactly the CPU is doing.

[Intel] https://www.intel.com/content/www/us/en/developer/articles/t...

Personally, I believe in automated benchmarks that measure end-to-end what is actually important and notify you when a change impacts performance for the worse.

Let's compile it with https://godbolt.org/, turn on some optimisations and inspect the IR (-O2 -emit-llvm). Copying out the part that corresponds to the while loop:

  4:

resources were extra useful when building deeper intuitions about GPU performance for ML models at work and in graduate school.

- CMU's "Deep Learning Systems" Course is hosted online and has YouTube lectures online. While not generally relevant to software performance, it is especially useful for engineers interested in building strong fundamentals that will serve them well when taking ML models into production environments: https://dlsyscourse.org/

- Compiler Explorer is a tool that allows you easily input some code in and check how the assembly output maps to the source. I think this is exceptionally useful for beginner/intermediate programmers who are familiar with one compiled high-level language and have not been exposed to reading lots of assembly. It is also great for testing how different compiler flags affect assembly output. Many people used to coding in C and C++ probably know about this, but I still run into people who haven't so I share it whenever performance comes up: https://godbolt.org/

To really understand what's going on here we can look at the compiled assembly code. I'm working on a Mac and can do this using the objdump tool. Compiler Explorer is also a handy tool but doesn't seem to support Arm assembly which is what Rust will use when compiling on Apple Silicon.

Play around with it in godbolt if you're really curious: https://godbolt.org/

It sounds like you are very new to programming and C++. If you'll allow me to make a recommendation: trying to set up a C++ in VS Code is quite a difficult task for a beginner. There are a lot of trip ups -- the compiler you're using, how your Code Runner or tasks.json or launch.json are set up, whether you're using Makefiles or Cmake, etc. For beginning with C++, I would really recommend messing around with Compiler Explorer instead (https://godbolt.org/). It was originally designed to turn C++ code into assembly for debugging, but you can use it like a fast scratchpad for learning, and it auto rebuilds as you make changes so you can see errors quickly. Good luck!