prettyprint | python module to output list | Data Manipulation library

You will have to clear the npx cache to make it work.

You can locate the location of the folder where create-react-app is installed using npm ls -g create-react-app.

Also, to clear the cache, refer to this answer in How can I clear the central cache for `npx`?

Failed to construct transformer: Error: error:0308010C:digital envelope routines::unsupported

The simplest and easiest solution to solve the above error is to downgrade Node.js to v14.18.1. And then just delete folder node_modules and try to rebuild your project and your error must be solved.

WebApplicationBuilder returned by WebApplication.CreateBuilder(args) exposes Configuration and Environment properties:

node-fetch v3 recently stopped support for the require way of importing it in favor of ES Modules. You'll need to use ESM imports now, like:

I am also stuck with the same problem because I installed the latest version of Node.js (v17.0.1).

Just go for node.js v14.18.1 and remove the latest version just use the stable version v14.18.1

Although I can't provide a formal definition of why/how the rounding modes were chosen as they were, the citation about compatibility with the % operator, which you have included, does make sense when you consider that % is not quite the same thing in C++ and Python.

In C++, it is the remainder operator, whereas, in Python, it is the modulus operator – and, when the two operands have different signs, these aren't necessarily the same thing. There are some fine explanations of the difference between these operators in the answers to: What's the difference between “mod” and “remainder”?

Now, considering this difference, the rounding (truncation) modes for integer division have to be as they are in the two languages, to ensure that the relationship you quoted, (m/n)*n + m%n == m, remains valid.

Here are two short programs that demonstrate this in action (please forgive my somewhat naïve Python code – I'm a beginner in that language):

C++:

Go to your package.json file and delete as many dependencies as you can until the project builds successfully. Then start adding back the dependencies one by one to detect which ones have troubles.

Then you can manually patch those dependencies by acceding them on node_modules/[dependencie]/android/build.gradle and setting androidx.core:core-ktx: or androidx.core:core: to a specific version (1.6.0 in my case).

It looks like GCC's naïveté about store-forwarding stalls is hurting its auto-vectorization strategy here. See also Store forwarding by example for some practical benchmarks on Intel with hardware performance counters, and What are the costs of failed store-to-load forwarding on x86? Also Agner Fog's x86 optimization guides.

(gcc -O3 enables -ftree-vectorize and a few other options not included by -O2, e.g. if-conversion to branchless cmov, which is another way -O3 can hurt with data patterns GCC didn't expect. By comparison, Clang enables auto-vectorization even at -O2, although some of its optimizations are still only on at -O3.)

It's doing 64-bit loads (and branching to store or not) on pairs of ints. This means, if we swapped the last iteration, this load comes half from that store, half from fresh memory, so we get a store-forwarding stall after every swap. But bubble sort often has long chains of swapping every iteration as an element bubbles far, so this is really bad.

(Bubble sort is bad in general, especially if implemented naively without keeping the previous iteration's second element around in a register. It can be interesting to analyze the asm details of exactly why it sucks, so it is fair enough for wanting to try.)

Anyway, this is pretty clearly an anti-optimization you should report on GCC Bugzilla with the "missed-optimization" keyword. Scalar loads are cheap, and store-forwarding stalls are costly. (Can modern x86 implementations store-forward from more than one prior store? no, nor can microarchitectures other than in-order Atom efficiently load when it partially overlaps with one previous store, and partially from data that has to come from the L1d cache.)

Even better would be to keep buf[x+1] in a register and use it as buf[x] in the next iteration, avoiding a store and load. (Like good hand-written asm bubble sort examples, a few of which exist on Stack Overflow.)

If it wasn't for the store-forwarding stalls (which AFAIK GCC doesn't know about in its cost model), this strategy might be about break-even. SSE 4.1 for a branchless pmind / pmaxd comparator might be interesting, but that would mean always storing and the C source doesn't do that.

If this strategy of double-width load had any merit, it would be better implemented with pure integer on a 64-bit machine like x86-64, where you can operate on just the low 32 bits with garbage (or valuable data) in the upper half. E.g.,

Overflow in the iterator state.

The C++ version will loop forever when given a large enough input: