Hello...
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 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 abuot Lock-free versus Lock, read my following post:
https://groups.google.com/forum/#!topic/comp.programming.threads/F_cF4ft1Qic
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.
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