It wouldn't surprise me if they have a really expensive epic chip with 8 cores and 1gb of cache. A chip like that would be incredible for applications with per core licensing
The problem with oop is that there are some problems that come up a lot (type promotion for example) where there just is no good solution. You can write a program in Julia that defines 4 different types of numbers and adds them all together in about 30 lines of code (and which is extendable to adding more number types). That just doesn't work in any oop language. You would have to choose the number types you're supporting ahead of time, and would have to write literally exponentially more code to make it work.
If you are interested in multiple dispatch, you really should check out Julia. In my opinion, the biggest reason Julia has been as successful as it has is that everything uses multiple dispatch, and the system for it is simple enough that you might not notice.
If you want a harder guarantee, well written numerical code will be as fast as any other language. If you find a counter example, let us know (it's probably something that should be fixed).
For string processing and other gc heavy code, Julia has further to go. Julia's gc is pretty basic, and needs a lot of love. That said, for dataframes like workloads, Julia usually manages to hand with the best (data.table), and it's rarely much slower.
I think Julia actually does a pretty good job of showing that the problem isn't types. The problem is semantics. Julia's restriction is eval to the global scope is a perfect example of this. It has a pretty minor on code, but a massive effect on performance. Python has a ton of things like this where a slightly different (and totally breaking) semantic change prevents optimization.
One thing that makes bridge very difficult is you need to be able to explain your partner's bids to your opponents. This means you either have to hard-code a bidding system (lame) or make a ton of progress in explainable ai.
If you're interested in climate modeling, you should really check out Clima.jl. it's an international collaboration on building a next gen climate model in Julia that uses some cool tricks like heavy usage of surrogate models that would be really hard to do in Fortran
BLAS/LApack are pretty much the only Fortran libraries that Julia uses afaik. There is also in development work on a Julia only blas replacement (Octavian.jl) which is competitive with mkl for the basic gemm already (more work needed for some of the more obscure functions). Lots of the push behind Julia is for it's ability to let fairly simple code compete with hands optimized Fortran.
This is incorrect. Chavin is charged with felony murder, which is when a death is the result of another felony (in this case, 2nd degree assault). All the state has to prove is the assault charge (that he intentionally took action, that action was not a justified use of force, and that the action caused harm), and that his actions were a significant cause of death. They don't have to prove intent to kill or that a reasonable person would realize it would cause death. Only that he assaulted Floyd, and that his actions did cause (not did exclusively cause) death
At least for strassen, the result is slightly less stable (increases the condition number by a constant factor). I think higham shows that any sub-cubic algorithm must have done conditioning issues, but that in practice they're not that bad for most matrices.
I think Julia makes clear how nice a good macro system can be. From simple things like @btime to the domain specific languages added by libraries like jump, macros just make a language way better. The only hard part is that once you have a good macro system, you will get lots of new users try to use it who take shouldn't.
This isn't a failure. This is the quickest vaccine rollout ever. The way you do statistics for this means that the overkill speeds up how quickly you can verify effectiveness. For example, if you do a booster after 6 months, that adds 6 months to your study time. If you try to thread the needle to make the smallest possible vaccine, you need way more testing to prove it works. The approach taken is why we have 10% of the US vaccinated now instead of having a vaccine approved in 2 years.
It's a better time to first plot than matlab, which is one of the other major contenders. On my computer it is about 3 seconds, which is noticable, but far from disqualifing.
In a lot of ways, I feel like 1.0 was a backend LTS while 1.6 is more of a front end one. 1.0 had most of the basics nailed down, but it's taken a while for it to become as seemless as it now is.
You should definitely check out Julia then. There are a few parts of the language that use C/C++ libraries (blas and mpfr are the main ones), but 95% of the time, your stack will be Julia all the way down.
For anything involving differential equations, Julia is way better than anything else. I also wouldn't be at all surprised if Julia catches up to python for way more machine learning within the next year
It depends. The thing that makes pgo good is that it bases it's compile off of analysis of runtime behavior. For that to work, you have to run the problem long enough to get a representative sample. For some programs, that can be a big speed decrease.