parallel | Parallel processing for PHP based on Amp | Architecture library

Have you tried FLANN?

This code doesn't solve your problem completely. It simply finds the nearest 50 neighbors to each point in your 500000 point dataset:

You can download the ASP.NET Core Runtime or .NET 5.0 SDK to allow you run to run ASP.NET applications on Windows, Mac or Linux. See https://support.microsoft.com/en-us/windows/downloads-for-windows-32490f9b-01ee-c13e-b2af-b5057c2d34e8

File->Settings->Tools->Emulator, and uncheck Launch in a tool window Then they will open in their own stand alone windows again.

There is a super convenient way to build, test and aggregate the outcome of changes of some branch before merging using pull requests.

Its common to create a pull request and trigger a workflow doing the checks. Just add "pull_request:" to reuse your existing workflow, to build and test your changes.

Reading the docs better, I think there's the answer:

The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines.

However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.

I don't know the internals, but guess each line or block of this bytecode is executed alone, and other threads are waiting (which makes it slow). But some lines consist of multiple blocks, and aren't atomic.

Here's what you get if run dis.dis('x[0] += 1'):

You are better off avoiding serializable isolation level. The way the serializable guarantee is provided is often deadlock prone.

If you can't alter your stored procs to use more targeted locking hints that guarantee the results you require at a lesser isolation level then you can prevent this particular deadlock scenario shown by ensuring that all locks are taken out on ServiceChange first before any are taken out on ServiceChangeParameter.

One way of doing this would be to introduce a table variable in spGetManageServicesRequest and materialize the results of

Thread safety does not necessarily imply being accessed by only one thread. The important aspect is that there is no concurrent access, i.e. no access by more than one thread at the same time. If the access by different threads is temporally ordered and this ordering also ensures the necessary memory visibility, which is the responsibility of the caller, it still is a thread safe usage.

The Spliterator documentation says:

Despite their obvious utility in parallel algorithms, spliterators are not expected to be thread-safe; instead, implementations of parallel algorithms using spliterators should ensure that the spliterator is only used by one thread at a time. This is generally easy to attain via serial thread-confinement, which often is a natural consequence of typical parallel algorithms that work by recursive decomposition.

The spliterator doesn’t need to be confined to the same thread throughout its lifetime, but there should be a clear handover at the caller’s side ensuring that the old thread stops using it before the new thread starts using it.

But the important takeaway is, the spliterator doesn’t need to be thread safe, hence, the iterator wrapped by a spliterator also doesn’t need to be thread safe.

Note that a typical behavior is splitting and handing over before starting traversal, but since an ordinary Iterator doesn’t support splitting, the wrapping spliterator has to iterate and buffer elements to implement splitting. Therefore, the Iterator experiences traversal by different threads (but one at a time) when the traversal has not been started from the Stream implementation’s perspective.

That said, the lines() implementation of BufferedReader is a bad example which you should not follow. Since it’s centered around a single readLine() call, it would be natural to implement Spliterator directly instead of implementing a more complicated Iterator and have it wrapped via spliteratorUnknownSize(…).

Since your example is likewise centered around a single poll() call, it’s also straight-forward to implement Spliterator directly:

Such data types are called "Higher-Kinded Data" (HKD). Manipulating them is often done with Generics or Template Haskell.

There are libraries like higgledy which provide built-in functionality for HKD. I believe construct is the function you are looking for:

There appears to be something very wrong with the calculated GC CPU time. It's 41010 secs compared to 2737 sec elapsed, which doesn't make sense if you're only running on three capabilities.

This miscalculation means that the calculated MUT CPU time, which is just total CPU time minus INIT, GC, and EXIT time, is actually a large negative number (5073-41010-2 = -35939). This gives a productivity of -35939/5073=-708%. When the MUT seconds are displayed, negative numbers are truncated at zero, to avoid reporting small negative numbers when MUT is very low and there's a clock precision error, which is why the displayed MUT time is 0 instead of -35939.

I don't know why the GC time is so badly miscalculated. My best guess is this. If you're running on Windows, there are known issues with CPU time clock precision, and it's possible that certain unusual patterns of garbage collection timing might result in precision errors occuring in only one direction, slightly overestimating the actual GC time more often than it underestimates it. Over 2.4 million collections (see your GC stats), this difference could accumulate to a huge positive error.

I looked through GitLab issues, and except for the report on general Windows CPU time imprecision and a couple of probably unrelated negative MUT reports here and here, I didn't see anything helpful.