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Primož Gabrijelčič

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Primož Gabrijelčič last won the day on April 30

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    Delphi 10.2 Tokyo

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  1. Primož Gabrijelčič

    ANN: Parnassus Bookmarks and Navigator will be included in the next release of RAD Studio

    I think this is Delphi problem, not Parnassus. It also happens when I right-click a breakpoint or right-click in the editor.
  2. Primož Gabrijelčič

    Using Parameters with Async/Await.

    Indeed it is.
  3. Primož Gabrijelčič

    Using Parameters with Async/Await.

    The first approach is OK. The second code fragment would not compile as the anonymous method in Await wouldn't know anything about RemindersDue and RemindersCount.
  4. Primož Gabrijelčič

    Pipeline multi runnable

    If I understand correctly, this has nothing to do with a pipeline. a) You "create a protocol" (whatever that means) b) You submit that protocol to a background thread to execute it and c) you repeat step b. And you can do all that with different protocols. As one protocol is a normal serial process, it has nothing to do with threading. Just implement "protocol runner" as a state machine. Then create a Background Worker abstraction and submit it work items. One work item = one "protocol runnner".
  5. Primož Gabrijelčič

    Pipeline multi runnable

    Pipelines are not restartable by design. You create a pipeline, run it until you don't need it anymore, then tear it down. What problem are you trying to solve, anyway?
  6. Primož Gabrijelčič

    Delegation good practice

    Yes, OnMessageis designed for this. OTL takes care that OnMessage handler is executed in the thread which created the background task (in the owner).
  7. Primož Gabrijelčič

    Delegation good practice

    I don't really understand your questions, sorry. If you run some external code, you cannot be sure if it is thread-safe. You should protect access to it with some synchronization mechanism. A whole idea of a guardian object with a "not responding" callback is new to me. I don't believe it is a commonly used pattern. I can't comment on it as I never used it in practice.
  8. Primož Gabrijelčič

    DynArraySetLength exception

    Compilable, working example, please.
  9. Primož Gabrijelčič

    OnTerminated never triggers

    By debugging, of course. Although, I have to admit, it had me quite stumped for a while. A bit of logging proves that service start and stop both come from the same thread (the first number is the thread id): 8996|ServiceCreate 8996|Servizio attivato porta = 8889 17284|ServiceStart 8996|Got message 1024 8996|Keep Alive 8996|Got message 1024 8996|Keep Alive 17284|A try to stop task... 6456|Sending COmniTaskMsg_Terminated 17284|FRunner.ExitCode = 0 17284|ServiceStop 8996|Got Terminated 8996|OnTaskTerminated? 8996 is the main service thread, 17284 is the service control thread, 6456 is the thread running the background task. 
  10. Primož Gabrijelčič

    OnTerminated never triggers

    Your task is created in one thread (main service thread) and that thread processes messages sent from the task, including the termination message. The task is stopped from a different thread. ServiceCreate and ServiceStart/ServiceStop are NOT executed from the same thread. The OtlTaskControl code expects that the task will be terminated from the same thread that it was created in. The .Terminate method therefore tries to process all messages waiting in the message queue (for example, the termination message), but it cannot do that as the message queue is not associated with the "ServiceStop" thread but with the "ServiceCreate" thread. After the ServiceStop exits, the main service thread is destroyed and that destroys the IOmniTaskControl object. At that moment, messages from the inter-thread message queue are processed, but they cannot be dispatched anywhere as the main service thread is being terminated. (Because of a DSiWin32.pas bug, messages will still be dispatched and may cause the program to crash. This will be fixed today.) Solution? Just do your OnTerminated processing in ServiceStop.
  11. Primož Gabrijelčič

    OnTerminated never triggers

    I was able to repeat the problem and I'm looking into it.
  12. Primož Gabrijelčič

    OnTerminated never triggers

    I can't tell the reason for your problem from the example you've given. Please post a minimal, compilable, and complete project that exhibits the problem and I'll look into it. It would be also good if you can drop dependency on WebBroker when preparing that project.
  13. Primož Gabrijelčič

    FastMM4 large memory allocation–benchmarking VirtualAlloc

    An interesting result from Windows 2012 Server, dual E5-2620v3 @ 2.4 GHz. MEM_TOP_DOWN is indeed much slower here - but only compared to the same machine. All allocations are blindingly fast compared to Windows 10 computers I've tested before. Windows Server is quite obviously optimized for different usage than Windows 10.
  14. Primož Gabrijelčič

    FastMM4 large memory allocation–benchmarking VirtualAlloc

    ... or my recent additions for finding allocation/deallocation bottlenecks: https://www.thedelphigeek.com/2016/02/finding-memory-allocation-bottlenecks.html You'll also get much better leak reporting. That alone is worth using the fresh github version.
  15. Earlier this week a long-time customer asked me why FastMM allocates large memory blocks from the top of the memory and if that option could safely be turned off. Their reasoning was that such allocations are much slower than normal ones. The question surprised me as I didn’t know of any such difference in speed so I did what I usually do–I wrote a benchmark application and measured it. TL;DR Yes, allocating from the top is slower. No, the difference is not big and in most cases you’ll not even notice it. There were, however, other interesting results that my simple benchmark pointed out. More on that in a moment, but first… Allocating from bottom and top In Windows, the code can ask for a memory block by calling VirtualAlloc with flag MEM_COMMIT and Windows will give you a suitable chunk of memory. This memory will usually be allocated from the start of the virtual memory visible to the program. The code can, however, call VirtualAlloc with flag MEM_COMMIT OR MEM_TOP_DOWN and Windows will return a block from the end of virtual memory available to the process. In a typical 32-bit Delphi program first such memory block will have address close to $7FF00000 (but a bit lower). You may want to allocate memory “from the top” if your program has two very distinct modes of allocating memory and you don’t want to mix them. For example, a frequently reallocated memory could come “from the bottom” and large blocks that are used for long periods of time “from the top”. This can reduce memory fragmentation, but the potential advantages are specific to each program. In other words – maybe it will help, maybe it will hurt. Another good scenario for MEM_TOP_BOTTOM is testing 64-bit code ported from 32-bits. For example, a typical “from the top” allocated block in a 64-bit program will have an address like this: $7FF4FDE30000. If your code at some point stores pointers into 4-byte integers, part of the address will be lost and as soon as that integer is converted back into a pointer and the code accesses that pointer, you’ll quite probably get an access violation. If a memory comes “from the bottom”, such problems would not be so easily detected. FastMM4 allocates large blocks (with sizes greater or equal to 258.5 KB) “from the top”. If I recall correctly, this was done to prevent memory fragmentation. Additionally, it can allocate all other block “from the top” if you define conditional symbol AlwaysAllocateTopDown and rebuild. (You have to use FastMM4 from github instead of the built-in Delphi version to use this functionality.) You can use this mode to test 32-bit programs ported to 64-bit code. MEM_TOP_DOWN is slower? The article my customer pointed to claimed that allocating from the top works much slower than allocating from the bottom. Even worse, the allocation algorithm was supposed to work in O(n^2) time so each additional allocation needs more time to execute. To top that off, the official documentation for the MEM_TOP_DOWN flag mentions: This can be slower than regular allocations, especially when there are many allocations. To verify that claim, I wrote a trivial benchmarking app (download it here). It allocates from 1 to 6000 blocks of size 264752 and measures the time needed. Block size 264752 was picked because at that size FastMM4 starts allocating memory “from the top”. 6000 blocks can safely be allocated in a 32-bit application (6000 * 264752 = 1.5 GB). In my tests I could allocate 6105 such blocks before I ran “out of memory” but just to be on the safe side I reduced the number in the released application. Results, measured on my fresh new notebook with a i7-8750 processor, were much closer to my expectations than to some O(n^2) algorithm. The “Top” algorithm is slightly slower (needs more time to execute) but the difference is not drastically large. What’s going on then? Is MEM_TOP_DOWN slow or not? As it turned out, the article I was referring to was written in 2011 and Windows have improved a lot since then. I don’t know which Windows version has fixed the “top allocation” problem, but it definitely doesn’t appear in Windows 7 and 10. Another interesting result is that the first 200 MB (approximately) are almost “free”. Somewhere around that number, the execution time jumps from around 3 ms to 50 ms and then continues to grow in more-or-less linear fashion. The benchmarking program measures each test only once and is therefore very susceptible to measurement errors but the result clearly shows an O(n) algorithm. Why are allocations smaller than 200 MB so fast? I’m guessing that Windows maps such amount of physical memory into the process’ virtual space when the process is started. When you exceed that limit, the allocator needs more time to allocate physical memory and map it into the process’ virtual space. That’s, however, just a guess. If you know better, please let me know in the comments. How fast are YOUR allocations? Just for the sake of completeness I rerun tests on my main PC (HP z820 with two E5 Xeons) and the results completely surprised me. The shape of the curve is almost the same–but notice the difference in speed! On the laptop, 4000 allocations execute in 250 ms. On the Xeon machine, over 1000 ms is needed for the same job. This machine is quite old (around 4 years IIRC) and it obviously contains a much slower memory. I know that computers can have faster or slower memory chips, but I never expected to see such a big difference in VirtualAllocspeed. (And yes, both machines are running latest Windows 10.) Now the whole shebang started to interest me even more, and I did some further tests on a few PCs used by fellow programmers. All of them were running Windows 10. As you can see below, there is some difference between them but none are so slow than my main computer 😞 Maybe the time has come to upgrade… If you want to download raw data and compare it to your own results, you can access it here. MEM_TOP_DOWN or not? The difference in speed is not that big–and most programs will not notice it–but I have to agree with the customer. The time has come to remove hard-coded MEM_TOP_DOWN from FastMM4 and replace it with a conditional {$IFDEF AllocateLargeBlocksTopDown}MEM_TOP_DOWN{$ENDIF}. I have created pull request for that change: https://github.com/pleriche/FastMM4/pull/75 (Original blog post: https://www.thedelphigeek.com/2019/04/fastmm4-large-memory.html)
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