gt | Easily generate information-rich , publication-quality tables | Grid library

I ran into the same issue today and came up with the following solution based on this very helpful article by Andrew Luca

In PrivateRoute.js:

I think the problem is that you are trying to use the deprecated babel-eslint parser, last updated a year ago, which looks like it doesn't support ES6 modules. Updating to the latest parser seems to work, at least for simple linting.

So, do this:

• In package.json, update the line "babel-eslint": "^10.0.2", to "@babel/eslint-parser": "^7.5.4",. This works with the code above but it may be better to use the latest version, which at the time of writing is 7.16.3.
• Run npm i from a terminal/command prompt in the folder
• In .eslintrc, update the parser line "parser": "babel-eslint", to "parser": "@babel/eslint-parser",
• In .eslintrc, add "requireConfigFile": false, to the parserOptions section (underneath "ecmaVersion": 8,) (I needed this or babel was looking for config files I don't have)
• Run the command to lint a file

Then, for me with just your two configuration files, the error goes away and I get appropriate linting errors.

For insecure HTTP connections in Gradle 7+ versions, we need to specify a boolean allowInsecureProtocol as true to MavenArtifactRepository closure.
Since you have received this error for sonatype repository, you need to set the repositories as below:

1. Groovy DSL

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

I'm not sure what you're using to code, but in order to set it in Android Studio, open the manifest of your project and under the "activity" section, put android:exported="true"(or false if that is what you prefer). I have attached an example.

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

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.,

Cache misses. When N int objects are allocated back-to-back, the memory reserved to hold them tends to be in a contiguous chunk. So crawling over the list in allocation order tends to access the memory holding the ints' values in sequential, contiguous, increasing order too.

Shuffle it, and the access pattern when crawling over the list is randomized too. Cache misses abound, provided there are enough different int objects that they don't all fit in cache.

At r==1, and r==2, CPython happens to treat such small ints as singletons, so, e.g., despite that you have 10 million elements in the list, at r==2 it contains only (at most) 100 distinct int objects. All the data for those fit in cache simultaneously.

Beyond that, though, you're likely to get more, and more, and more distinct int objects. Hardware caches become increasingly useless then when the access pattern is random.

Illustrating: