Path: csiph.com!eternal-september.org!reader02.eternal-september.org!.POSTED!not-for-mail From: World90 Newsgroups: comp.lang.pascal.misc Subject: More about my powerful inventions of scalable reference counting algorithm and of my scalable algorithms.. Date: Fri, 19 Feb 2021 12:39:38 -0800 Organization: A noiseless patient Spider Lines: 474 Message-ID: Mime-Version: 1.0 Content-Type: text/plain; charset=utf-8; format=flowed Content-Transfer-Encoding: 8bit Injection-Date: Fri, 19 Feb 2021 20:39:38 -0000 (UTC) Injection-Info: reader02.eternal-september.org; posting-host="f07339999635bb875e5a2b547741a7ee"; logging-data="5895"; mail-complaints-to="abuse@eternal-september.org"; posting-account="U2FsdGVkX193x4WXGaepIfJwxt456Kn/k2n3Gbf7bdE=" User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:78.0) Gecko/20100101 Thunderbird/78.7.1 Cancel-Lock: sha1:DlSxzu0yW8QH/p4/sKcZSbqVA9Q= Content-Language: en-US X-Mozilla-News-Host: news://news.eternal-september.org:119 Xref: csiph.com comp.lang.pascal.misc:2767 Hello... More about my powerful inventions of scalable reference counting algorithm and of my scalable algorithms.. I invite you to read the following web page: Why is memory reclamation so important? https://concurrencyfreaks.blogspot.com/search?q=resilience+and+urcu Notice that it is saying the following about RCU: "Reason number 4, resilience Another reason to go with lock-free/wait-free data structures is because they are resilient to failures. On a shared memory system with multiples processes accessing the same data structure, even if one of the processes dies, the others will be able to progress in their work. This is the true gem of lock-free data structures: progress in the presence of failure. Blocking data structures (typically) do not have this property (there are exceptions though). If we add a blocking memory reclamation (like URCU) to a lock-free/wait-free data structure, we are loosing this resilience because one dead process will prevent further memory reclamation and eventually bring down the whole system. There goes the resilience advantage out the window." So i think that RCU can not be used as reference counting, since it is blocking on the writer side, so it is not resilient to failures since it is not lock-free on the writer side. So this is why i have invented my powerful Scalable reference counting with efficient support for weak references that is lock-free for its scalable reference counting, and here it is: https://sites.google.com/site/scalable68/scalable-reference-counting-with-efficient-support-for-weak-references And my scalable reference counting algorithm is of the SCU(0,1) Class of Algorithms, so under scheduling conditions which approximate those found in commercial hardware architectures, it becomes wait-free with a system latency of time O(sqrt(k)) and with an individual latency of O(k*sqrt(k)), and k number of threads. The proof is here on the following PhD paper: https://arxiv.org/pdf/1311.3200.pdf This paper suggests a simple solution to this problem. We show that, for a large class of lock- free algorithms, under scheduling conditions which approximate those found in commercial hardware architectures, lock-free algorithms behave as if they are wait-free. In other words, programmers can keep on designing simple lock-free algorithms instead of complex wait-free ones, and in practice, they will get wait-free progress. It says on the Analysis of the Class SCU(q, s): "Given an algorithm in SCU(q, s) on k correct processes under a uniform stochastic scheduler, the system latency is O(q + s*sqrt(k), and the individual latency is O(k(q + s*sqrt(k))." More precision about my new inventions of scalable algorithms.. And look at my below powerful inventions of LW_Fast_RWLockX and Fast_RWLockX that are two powerful scalable RWLocks that are FIFO fair and Starvation-free and costless on the reader side (that means with no atomics and with no fences on the reader side), they use sys_membarrier expedited on Linux and FlushProcessWriteBuffers() on windows, and if you look at the source code of my LW_Fast_RWLockX.pas and Fast_RWLockX.pas inside the zip file, you will notice that in Linux they call two functions that are membarrier1() and membarrier2(), the membarrier1() registers the process's intent to use MEMBARRIER_CMD_PRIVATE_EXPEDITED and membarrier2() executes a memory barrier on each running thread belonging to the same process as the calling thread. Read more here to understand: https://man7.org/linux/man-pages/man2/membarrier.2.html Here is my new powerful inventions of scalable algorithms.. I have just updated my powerful inventions of LW_Fast_RWLockX and Fast_RWLockX that are two powerful scalable RWLocks that are FIFO fair and Starvation-free and costless on the reader side (that means with no atomics and with no fences on the reader side), they use sys_membarrier expedited on Linux and FlushProcessWriteBuffers() on windows, and now they work with both Linux and Windows, and i think my inventions are really smart, since read the following PhD researcher, he says the following: "Until today, there is no known efficient reader-writer lock with starvation-freedom guarantees;" Read more here: http://concurrencyfreaks.blogspot.com/2019/04/onefile-and-tail-latency.html So as you have just noticed he says the following: "Until today, there is no known efficient reader-writer lock with starvation-freedom guarantees;" So i think that my above powerful inventions of scalable reader-writer locks are efficient and FIFO fair and Starvation-free. LW_Fast_RWLockX that is a lightweight scalable Reader-Writer Mutex that uses a technic that looks like Seqlock without looping on the reader side like Seqlock, and this has permitted the reader side to be costless, it is fair and it is of course Starvation-free and it does spin-wait, and also Fast_RWLockX a lightweight scalable Reader-Writer Mutex that uses a technic that looks like Seqlock without looping on the reader side like Seqlock, and this has permitted the reader side to be costless, it is fair and it is of course Starvation-free and it does not spin-wait, but waits on my SemaMonitor, so it is energy efficient. You can read about them and download them from my website here: https://sites.google.com/site/scalable68/scalable-rwlock Also my other inventions are the following scalable RWLocks that are FIFO fair and starvation-free: Here is my invention of a scalable and starvation-free and FIFO fair and lightweight Multiple-Readers-Exclusive-Writer Lock called LW_RWLockX, it works across processes and threads: https://sites.google.com/site/scalable68/scalable-rwlock-that-works-accross-processes-and-threads And here is my inventions of New variants of Scalable RWLocks that are FIFO fair and Starvation-free: https://sites.google.com/site/scalable68/new-variants-of-scalable-rwlocks More about the energy efficiency of Transactional memory and more.. I have just read the following PhD paper, it is also about energy efficiency of Transactional memory, here it is: Techniques for Enhancing the Efficiency of Transactional Memory Systems http://kth.diva-portal.org/smash/get/diva2:1258335/FULLTEXT02.pdf And i think it is the best known energy efficient algorithm for Transactional memory, but i think it is not good, since look at how for 64 cores the Beta parameter can be 16 cores, so i think i am smart and i have just invented a much more energy efficient and powerful scalable fast Mutex and i have also just invented scalable RWLocks that are starvation-free and fair, read about them in my below writing and thoughts: More about deadlocks and lock-based systems and more.. I have just read the following from an software engineer from Quebec Canada: A deadlock-detecting mutex https://faouellet.github.io/ddmutex/ And i have just understood rapidly his algorithm, but i think his algorithm is not efficient at all, since we can find if a graph has a strongly connected component in around a time complexity O(V+E), so then the algorithm above of the engineer from Quebec Canada takes around a time complexity of O(n*(V+E)), so it is not good. So a much better way is to use my following way of detecting deadlocks: DelphiConcurrent and FreepascalConcurrent are here Read more here in my website: https://sites.google.com/site/scalable68/delphiconcurrent-and-freepascalconcurrent And i will soon enhance much more DelphiConcurrent and FreepascalConcurrent to support both Communication deadlocks and Resource deadlocks. About Transactional memory and locks.. I have just read the following paper about Transactional memory and locks: http://sunnydhillon.net/assets/docs/concurrency-tm.pdf I don't agree with the above paper, since read my following thoughts to understand: I have just invented a new powerful scalable fast mutex, and it has the following characteristics: 1- Starvation-free 2- Tunable fairness 3- It keeps efficiently and very low its cache coherence traffic 4- Very good fast path performance 5- And it has a good preemption tolerance. 6- It is faster than scalable MCS lock 7- It solves the problem of lock convoying So my new invention also solves the following problem: The convoy phenomenon https://blog.acolyer.org/2019/07/01/the-convoy-phenomenon/ And here is my other new invention of a Scalable RWLock that works across processes and threads that is starvation-free and fair and i will soon enhance it much more and it will become really powerful: https://sites.google.com/site/scalable68/scalable-rwlock-that-works-accross-processes-and-threads And about Lock-free versus Lock, read my following post: https://groups.google.com/forum/#!topic/comp.programming.threads/F_cF4ft1Qic And about deadlocks, here is also how i have solved it, and i will soon enhance much more DelphiConcurrent and FreepacalConcurrent: DelphiConcurrent and FreepascalConcurrent are here Read more here in my website: https://sites.google.com/site/scalable68/delphiconcurrent-and-freepascalconcurrent So i think with my above scalable fast mutex and my scalable RWLocks that are starvation-free and fair and by reading the following about composability of lock-based systems, you will notice that lock-based systems are still useful. "About composability of lock-based systems.. Design your systems to be composable. Among the more galling claims of the detractors of lock-based systems is the notion that they are somehow uncomposable: “Locks and condition variables do not support modular programming,” reads one typically brazen claim, “building large programs by gluing together smaller programs[:] locks make this impossible.”9 The claim, of course, is incorrect. For evidence one need only point at the composition of lock-based systems such as databases and operating systems into larger systems that remain entirely unaware of lower-level locking. There are two ways to make lock-based systems completely composable, and each has its own place. First (and most obviously), one can make locking entirely internal to the subsystem. For example, in concurrent operating systems, control never returns to user level with in-kernel locks held; the locks used to implement the system itself are entirely behind the system call interface that constitutes the interface to the system. More generally, this model can work whenever a crisp interface exists between software components: as long as control flow is never returned to the caller with locks held, the subsystem will remain composable. Second (and perhaps counterintuitively), one can achieve concurrency and composability by having no locks whatsoever. In this case, there must be no global subsystem state—subsystem state must be captured in per-instance state, and it must be up to consumers of the subsystem to assure that they do not access their instance in parallel. By leaving locking up to the client of the subsystem, the subsystem itself can be used concurrently by different subsystems and in different contexts. A concrete example of this is the AVL tree implementation used extensively in the Solaris kernel. As with any balanced binary tree, the implementation is sufficiently complex to merit componentization, but by not having any global state, the implementation may be used concurrently by disjoint subsystems—the only constraint is that manipulation of a single AVL tree instance must be serialized." Read more here: https://queue.acm.org/detail.cfm?id=1454462 About mathematics and about abstraction.. I think my specialization is also that i have invented many software algorithms and software scalable algorithms and i am still inventing other software scalable algorithms and algorithms, those scalable algorithms and algorithms that i have invented are like inventing mathematical theorems that you prove and present in a higher level abstraction, but not only that but those algorithms and scalable algorithms of mine are presented in a form of higher level software abstraction that abstract the complexity of my scalable algorithms and algorithms, it is the most important part that interests me, for example notice how i am constructing higher level abstraction in my following tutorial as methodology that, first, permits to model the synchronization objects of parallel programs with logic primitives with If-Then-OR-AND so that to make it easy to translate to Petri nets so that to detect deadlocks in parallel programs, please take a look at it in my following web link because this tutorial of mine is the way of learning by higher level abstraction: How to analyse parallel applications with Petri Nets https://sites.google.com/site/scalable68/how-to-analyse-parallel-applications-with-petri-nets So notice that my methodology is a generalization that solves communication deadlocks and resource deadlocks in parallel programs. 1- Communication deadlocks that result from incorrect use of event objects or condition variables (i.e. wait-notify synchronization). 2- Resource deadlocks, a common kind of deadlock in which a set of threads blocks forever because each thread in the set is waiting to acquire a lock held by another thread in the set. This is what interests me in mathematics, i want to work efficiently in mathematics in a much higher level of abstraction, i give you an example of what i am doing in mathematics so that you understand, look at how i am implementing mathematics as a software parallel conjugate gradient system solvers that scale very well, and i am presenting them in a higher level of abstraction, this is how i am abstracting the mathematics of them, read the following about it to notice it: About SOR and Conjugate gradient mathematical methods.. I have just looked at SOR(Successive Overrelaxation Method), and i think it is much less powerful than Conjugate gradient method, read the following to notice it: COMPARATIVE PERFORMANCE OF THE CONJUGATE GRADIENT AND SOR METHODS FOR COMPUTATIONAL THERMAL HYDRAULICS https://inis.iaea.org/collection/NCLCollectionStore/_Public/19/055/19055644.pdf?r=1&r=1 This is why i have implemented in both C++ and Delphi my Parallel Conjugate Gradient Linear System Solver Library that scales very well, read my following thoughts about it to understand more: About the convergence properties of the conjugate gradient method The conjugate gradient method can theoretically be viewed as a direct method, as it produces the exact solution after a finite number of iterations, which is not larger than the size of the matrix, in the absence of round-off error. However, the conjugate gradient method is unstable with respect to even small perturbations, e.g., most directions are not in practice conjugate, and the exact solution is never obtained. Fortunately, the conjugate gradient method can be used as an iterative method as it provides monotonically improving approximations to the exact solution, which may reach the required tolerance after a relatively small (compared to the problem size) number of iterations. The improvement is typically linear and its speed is determined by the condition number κ(A) of the system matrix A: the larger is κ(A), the slower the improvement. Read more here: http://pages.stat.wisc.edu/~wahba/stat860public/pdf1/cj.pdf So i think my Conjugate Gradient Linear System Solver Library that scales very well is still very useful, read about it in my writing below: Read the following interesting news: The finite element method finds its place in games Read more here: https://translate.google.com/translate?hl=en&sl=auto&tl=en&u=https%3A%2F%2Fhpc.developpez.com%2Factu%2F288260%2FLa-methode-des-elements-finis-trouve-sa-place-dans-les-jeux-AMD-propose-la-bibliotheque-FEMFX-pour-une-simulation-en-temps-reel-des-deformations%2F But you have to be aware that finite element method uses Conjugate Gradient Method for Solution of Finite Element Problems, read here to notice it: Conjugate Gradient Method for Solution of Large Finite Element Problems on CPU and GPU https://pdfs.semanticscholar.org/1f4c/f080ee622aa02623b35eda947fbc169b199d.pdf This is why i have also designed and implemented my Parallel Conjugate Gradient Linear System Solver library that scales very well, here it is: My Parallel C++ Conjugate Gradient Linear System Solver Library that scales very well version 1.76 is here.. Author: Amine Moulay Ramdane Description: This library contains a Parallel implementation of Conjugate Gradient Dense Linear System Solver library that is NUMA-aware and cache-aware that scales very well, and it contains also a Parallel implementation of Conjugate Gradient Sparse Linear System Solver library that is cache-aware that scales very well. Sparse linear system solvers are ubiquitous in high performance computing (HPC) and often are the most computational intensive parts in scientific computing codes. A few of the many applications relying on sparse linear solvers include fusion energy simulation, space weather simulation, climate modeling, and environmental modeling, and finite element method, and large-scale reservoir simulations to enhance oil recovery by the oil and gas industry. Conjugate Gradient is known to converge to the exact solution in n steps for a matrix of size n, and was historically first seen as a direct method because of this. However, after a while people figured out that it works really well if you just stop the iteration much earlier - often you will get a very good approximation after much fewer than n steps. In fact, we can analyze how fast Conjugate gradient converges. The end result is that Conjugate gradient is used as an iterative method for large linear systems today. Please download the zip file and read the readme file inside the zip to know how to use it. You can download it from: https://sites.google.com/site/scalable68/scalable-parallel-c-conjugate-gradient-linear-system-solver-library Language: GNU C++ and Visual C++ and C++Builder Operating Systems: Windows, Linux, Unix and Mac OS X on (x86) -- As you have noticed i have just written above about my Parallel C++ Conjugate Gradient Linear System Solver Library that scales very well, but here is my Parallel Delphi and Freepascal Conjugate Gradient Linear System Solvers Libraries that scale very well: Parallel implementation of Conjugate Gradient Dense Linear System solver library that is NUMA-aware and cache-aware that scales very well https://sites.google.com/site/scalable68/scalable-parallel-implementation-of-conjugate-gradient-dense-linear-system-solver-library-that-is-numa-aware-and-cache-aware PARALLEL IMPLEMENTATION OF CONJUGATE GRADIENT SPARSE LINEAR SYSTEM SOLVER LIBRARY THAT SCALES VERY WELL https://sites.google.com/site/scalable68/scalable-parallel-implementation-of-conjugate-gradient-sparse-linear-system-solver Thank you, Amine Moulay Ramdane.