optim | numerical optimization methods for nonlinear functions

This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference

update to

Add mavenCentral() in Build Script

Reading a memory address does not clear the cache line.

Writing does.

So in a modern computer, there is RAM, and there are multiple layers of cache "around" the CPU (they are called L1, L2 and L3 cache, but the important part is that they are layers, and the CPU is at the middle). In a multi-core system, often the outer layers are shared; the innermost layer is usually not, and is specific to a given CPU.

Clearing the cache line means informing every other cache holding this memory "the data you own may be stale, throw it out".

Test and set writes true and atomically returns the old value. It clears the cache line, because it writes.

Test does not write. If you have another thread unsynchronized with this one, it reading the cache of this memory doesn't have to be poked.

The outer loop writes true, and exits if it replaced false. The inner loop waits until there is a false visible, then falls to outer loop. The inner loop need not clear every other cpu's cache status of the value of the atomic flag, but the outer has to (as it could change the false to true). As spinning could go on for a while, avoiding continuous cache clearing seems like a good idea.

Just remove the "extractCss": true from your production environment, it will resolve the problem.

The reason about it is extractCss is deprecated, and it's value is true by default. See more here: Extracting CSS into JS with Angular 11 (deprecated extractCss)

This is CWG 2426. The destructor is potentially invoked within this context, because even after the initialization of the return A object, it's still possible that the function fails to complete successfully: any temporaries created during the return statement, and automatic local variables that are in scope, must be destroyed, and if the destruction throws, then as part of stack unwinding, the A object is destroyed. Compilers should require the destructor to be accessible at this point.

Note 1: exceptions thrown by the destructors of local variables in the outermost scope of the function can be caught by a function try block.

Note 2: after the return object is destroyed, the handler is allowed to execute another return statement. There is an example of this in the standard.

There's an issue with vuetify I think. But if you use yarn, you can use

TL;DR: the C++ code and Numpy do not use the same matrix-multiplication library.

The matrix multiplication of the GSL library is not optimized. On my machine, it runs sequentially, does not use SIMD instructions (SSE/AVX), does not efficiently unroll the loops to perform register tiling. I also suspect it also does not use the CPU cache efficiently due to the lack of tiling. These optimizations are critical to achieve high-performance and widely used in fast linear algebra libraries.

Numpy uses a BLAS library installed on your machine. On many Linux platform, its uses OpenBLAS or the Intel MKL. Both are very fast (they use all the methods described above) and should run in parallel.

You can find which implementation of BLAS is used by Numpy here. On my Linux machine, Numpy use by default CBLAS which internally use OpenBLAS (OpenBLAS is strangely not directly detected by Numpy).

There are many fast parallel BLAS implementations (GotoBLAS, ATLAS, BLIS, etc.). The open-source BLIS library is great because its matrix multiplication is very fast on many different architectures.

As a result, the simplest way to improve your C++ code is to use the cblas_sgemm CBLAS function and link a fast BLAS library like OpenBLAS or BLIS for example.

For more information:

One simple way to see how bad the GSL perform is to use a profiler (like perf on Linux or VTune on Windows). In your case Linux perf, report that >99% of the time is spent in libgslcblas.so (ie. the GSL library). More specifically, most of the execution time is spent in this following assembly loop:

Make sure that your gradle is using proper JDK. Try running ./gradlew --version in your project's directory, output should be something like this:

This is an example of argument-dependent lookup.

Even though ::main is in global namespace, A::f is callable without using the fully-qualified name, because it is looked up in the namespace A of its argument, A::B. As a result, there is an ambiguity between ::f and A::f.

To resolve the ambiguity, you would need to call either A::f(b) (as you did), or ::f(b).