mem | Proportional and monospaced sans light pixel font family | User Interface library

Your title says you are using Java 9. With Activiti 6 you will have to use JDK 1.8 (Java 8).

Elastic flatten objects. Such that

Well, this is interesting. I did not think of searching for lsof's COMMAND column, before.

Turns out, ControlCe means "Control Center" and beginning with Monterey, macOS does listen ports 5000 & 7000 on default.

1. Go to System Preferences > Sharing
2. Uncheck AirPlay Receiver.
3. Now, you should be able to restart puma as usual.

Source: https://developer.apple.com/forums/thread/682332

Prior to C++20, there was no way to invoke a deallocation function (operator delete) that didn't call your class' destructor first, making it impossible for you to clean up extra explicitly allocated resources owned by your class (without hacky code like your static pointer)

If you have access to C++20, then I encourage you to use destroying delete which was created to solve problems like this.

• Your class can hold onto an instance of std::pmr::memory_resource* (injected through the constructor)
• Change your operator delete into e.g., void operator delete(Node *ptr, std::destroying_delete_t) noexcept
  • destroying_delete is a tag that, when you use it, indicates that you will take responsibility for invoking the appropriate destructor.
• Derived classes should also implement a similar deleter.

Without making too many changes to your code, we can do the following in Node:

Your question boils down to: "how can I convert my parse tree to an abstract syntax tree?". The simple answer to that is: "you can't" :). At least, not using a built-in ANTLR mechanism. You'll have to traverse the parse tree (using ANTLR's visitor- or listener mechanism) and construct your AST manually.

The feature to more easily create AST's from a parse tree often pops up both in ANTLR's Github repo:

• https://github.com/antlr/antlr4/issues/2428

as well as on stackoverflow:

• Generate AST using ANTLR4 generated visitor
• ANTLR4 AST Creation - How to create an AstVistor

The TLDR is that loads which miss all levels of the TLB (and so require a page walk) and which are separated by address unknown stores can't execute in parallel, i.e., the loads are serialized and the memory level parallelism (MLP) factor is capped at 1. Effectively, the stores fence the loads, much as lfence would.

The slow version of your insert function results in this scenario, while the other two don't (the store address is known). For large region sizes the memory access pattern dominates, and the performance is almost directly related to the MLP: the fast versions can overlap load misses and get an MLP of about 3, resulting in a 3x speedup (and the narrower reproduction case we discuss below can show more than a 10x difference on Skylake).

The underlying reason seems to be that the Skylake processor tries to maintain page-table coherence, which is not required by the specification but can work around bugs in software.

For those who are interested, we'll dig into the details of what's going on.

I could reproduce the problem immediately on my Skylake i7-6700HQ machine, and by stripping out extraneous parts we can reduce the original hash insert benchmark to this simple loop, which exhibits the same issue:

The Rust documentation mentions this behavior for array:

Prior to Rust 1.53, arrays did not implement IntoIterator by value, so the method call array.into_iter() auto-referenced into a slice iterator. Right now, the old behavior is preserved in the 2015 and 2018 editions of Rust for compatibility, ignoring IntoIterator by value. In the future, the behavior on the 2015 and 2018 edition might be made consistent to the behavior of later editions.

• Rust documentation, "Primitive Type array"

You will get references if you're using Rust 2018, but for the time being you can use IntoIterator::into_iter(array)

Dereferencing the b within the loop will hint at this:

Probably this answer will be useful for you. This nvidia_smi python module have some useful tools like checking the gpu total memory. Here I reproduce the code of the answer I mentioned earlier.

try adding