NS | Natural Selection mod for Half-life

I've hit this same issue and have temporarily resolved it by uninstalling react-native-video (npm uninstall --save react-native-video). That's not a great answer as I need that component, but I don't have a full solution yet. I think somehow com.yqritc:android-scalablevideoview:1.0.4. is required by react-native-video but has gotten lost or removed. Other thoughts are welcome.

UPDATE: Resolved! In your build.gradle in your Android folder you need to add the repository "jcenter()" in allprojects (not in build dependencies) like this...

Those samples depend on JDK internals.

Looks like since JDK 9 and JDK-8152907, Math.log is no longer intrinsified into C2 intermediate representation. Instead, a direct call to a quick LIBM-backed stub is made. This is usually faster for the code that actually uses the result. Notice how measureCorrect is faster in JDK 17 output in your case.

But for JMH samples, it limits the the compiler optimizations around the Math.log, and dead code / folding samples do not work properly. The fix it to make samples that do not rely on JDK internals without a good reason, and instead use a custom written payload.

This is being done in JMH here:

• https://bugs.openjdk.java.net/browse/CODETOOLS-7903094
• https://github.com/openjdk/jmh/pull/60

To measure the branch you are interested in and particularly the scenario when while loop becomes hot, I've used the following benchmark:

It not happen becuase of you have two build-tools version installed. It happens because of caches so on android studio just invalidating caches and restarting will fix this.

The basic answer is that for an a = Array(T) Julia always allocates sizeof(T)*length(a)+40 bytes. Since sizeof(Missing) == 0, this means it doesn't allocate any bytes related to the length.

Indexing occurs on line 569 of array.c, and the relevant line is

As of Spring Boot 2.6, the property spring.mongodb.embedded.version must be set to use the auto-configured embedded MongoDB. It's mentioned in the release notes: https://github.com/spring-projects/spring-boot/wiki/Spring-Boot-2.6-Release-Notes#embedded-mongo

This is also what the error message you posted, advises to do: Set the spring.mongodb.embedd ed.version property or define your own MongodConfig bean to use embedded MongoDB

The annotation @DataMongoTest is meta-annotated with @ImportAutoConfiguration and @AutoConfigureDataMongo, and is designed to trigger auto-configuration of MongoDB unless explicitly disabled as you do in the working configuration examples.

In your first configuration example, the annotation @EnableAutoConfiguration(exclude = EmbeddedMongoAutoConfiguration.class) does not override this effect of @DataMongoTest.

With Spring Boot 2.5.6, the auto-configured MongodConfig bean is most likely also part of the application context but not effectively used. But this depends on the rest of the code and in particular on the MongodbContainerInitializer.

TL;DR: You are observing what happens when JIT compiler cannot trust that values are not changing inside the loop. Additionally, in the tiny benchmark like this, Blackhole.consume costs dominate, obscuring the results.

Simplifying the test:

Looks like this was fixed in v4.4.3

TL;DR: I made an experimental analysis on Numpy 1.21.1. Experimental results show that np.sum does NOT (really) make use of SIMD instructions: no SIMD instruction are used for integers, and scalar SIMD instructions are used for floating-point numbers! Moreover, Numpy converts the integers to 64-bits values for smaller integer types by default so to avoid overflows!

Note that this may not reflect all Numpy versions since there is an ongoing work to provide SIMD support for commonly used functions (the version Numpy 1.22.0rc1 not yet released continue this long-standing work). Moreover, the compiler or the processor used may significantly impact the results. The following experiments have been done using a Numpy retrieved from pip on a Debian Linux with a i5-9600KF processor.

For floating-point numbers, Numpy uses a pairwise algorithm which is known to be quite numerically stable while being relatively fast. This can be seen in the code, but also simply using a profiler: TYPE_pairwise_sum is the C function called to compute the sum at runtime (where TYPE is DOUBLE or FLOAT).

For integers, Numpy use a classical naive reduction. The C function called is ULONG_add_avx2 on AVX2-compatible machines. It also surprisingly convert items to 64-bit ones if the type is not np.int64.

Here is the hot part of the assembly code executed by the DOUBLE_pairwise_sum function