- What about garbage collection? - 1 Update
- More about compile time and build time.. - 1 Update
| aminer68@gmail.com: Sep 30 11:41AM -0700 Hello, What about garbage collection? Read what said this serious specialist called Chris Lattner: "One thing that I don't think is debatable is that the heap compaction behavior of a GC (which is what provides the heap fragmentation win) is incredibly hostile for cache (because it cycles the entire memory space of the process) and performance predictability." "Not relying on GC enables Swift to be used in domains that don't want it - think boot loaders, kernels, real time systems like audio processing, etc." "GC also has several *huge* disadvantages that are usually glossed over: while it is true that modern GC's can provide high performance, they can only do that when they are granted *much* more memory than the process is actually using. Generally, unless you give the GC 3-4x more memory than is needed, you'll get thrashing and incredibly poor performance. Additionally, since the sweep pass touches almost all RAM in the process, they tend to be very power inefficient (leading to reduced battery life)." Read more here: https://lists.swift.org/pipermail/swift-evolution/Week-of-Mon-20160208/009422.html Here is Chris Lattner's Homepage: http://nondot.org/sabre/ And here is Chris Lattner's resume: http://nondot.org/sabre/Resume.html#Tesla This why i have invented the following scalable algorithm and its implementation that makes Delphi and FreePascal more powerful: My invention that is my scalable reference counting with efficient support for weak references version 1.37 is here.. Here i am again, i have just updated my scalable reference counting with efficient support for weak references to version 1.37, I have just added a TAMInterfacedPersistent that is a scalable reference counted version, and now i think i have just made it complete and powerful. Because I have just read the following web page: https://www.codeproject.com/Articles/1252175/Fixing-Delphis-Interface-Limitations But i don't agree with the writting of the guy of the above web page, because i think you have to understand the "spirit" of Delphi, here is why: A component is supposed to be owned and destroyed by something else, "typically" a form (and "typically" means in english: in "most" cases, and this is the most important thing to understand). In that scenario, reference count is not used. If you pass a component as an interface reference, it would be very unfortunate if it was destroyed when the method returns. Therefore, reference counting in TComponent has been removed. Also because i have just added TAMInterfacedPersistent to my invention. To use scalable reference counting with Delphi and FreePascal, just replace TInterfacedObject with my TAMInterfacedObject that is the scalable reference counted version, and just replace TInterfacedPersistent with my TAMInterfacedPersistent that is the scalable reference counted version, and you will find both my TAMInterfacedObject and my TAMInterfacedPersistent inside the AMInterfacedObject.pas file, and to know how to use weak references please take a look at the demo that i have included called example.dpr and look inside my zip file at the tutorial about weak references, and to know how to use delegation take a look at the demo that i have included called test_delegation.pas, and take a look inside my zip file at the tutorial about delegation that learns you how to use delegation. I think my Scalable reference counting with efficient support for weak references is stable and fast, and it works on both Windows and Linux, and my scalable reference counting scales on multicore and NUMA systems, and you will not find it in C++ or Rust, and i don't think you will find it anywhere, and you have to know that this invention of mine solves the problem of dangling pointers and it solves the problem of memory leaks and my scalable reference counting is "scalable". And please read the readme file inside the zip file that i have just extended to make you understand more. You can download my new scalable reference counting with efficient support for weak references version 1.37 from: https://sites.google.com/site/scalable68/scalable-reference-counting-with-efficient-support-for-weak-references Thank you, Amine Moulay Ramdane. |
| aminer68@gmail.com: Sep 30 11:04AM -0700 Hello... More about compile time and build time.. Look here about Java it says: "Java Build Time Benchmarks I'm trying to get some benchmarks for builds and I'm coming up short via Google. Of course, build times will be super dependent on a million different things, but I'm having trouble finding anything comparable. Right now: We've got ~2 million lines of code and it takes about 2 hours for this portion to build (this excludes unit tests). What do your build times look like for similar sized projects and what did you do to make it that fast?" Read here to notice it: https://www.reddit.com/r/java/comments/4jxs17/java_build_time_benchmarks/ So 2 million lines of code of Java takes about 2 hours to build. And what do you think that 2 millions lines of code takes to Delphi ? Answer: Just about 20 seconds. Here is the proof from Embarcadero, read and look at the video to be convinced about Delphi: https://community.idera.com/developer-tools/b/blog/posts/compiling-a-million-lines-of-code-with-delphi C++ also takes "much" more time to compile than Delphi. This is why i said previously the following: I think Delphi is a single pass compiler, it is very fast at compile time, and i think C++ and Java and C# are multi pass compilers that are much slower than Delphi in compile time, but i think that the generated executable code of Delphi is still fast and is faster than C#. And what about the Advantages and disadvantages of single and multi pass compiler? And From Automata Theory we get that any Turing Machine that does 2 (or more ) pass over the tape, can be replaced with an equivalent one that makes only 1 pass, with a more complicated state machine. At the theoretical level, they the same. At a practical level, all modern compilers make only one pass over the source code. It typically translated into an internal representation that the different phases analyze and update. During flow analysis basic blocks are identified. Common sub expression are found and precomputed and results reused. During loop analysis, invariant code will be moved out the loop. During code emission registers are assigned and peephole analysis and code reduction is applied. Thank you, Amine Moulay Ramdane. |
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