[continued from previous message]
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
And about Message Passing Process Communication Model and Shared Memory
Process Communication Model:
An advantage of shared memory model is that memory communication is
faster as compared to the message passing model on the same machine.
Read the following to notice it:
Why did Windows NT move away from the microkernel?
"The main reason that Windows NT became a hybrid kernel is speed. A microkernel-based system puts only the bare minimum system components in
the kernel and runs the rest of them as user mode processes, known as
servers. A form of inter-process communication (IPC), usually message
passing, is used for communication between servers and the kernel.
Microkernel-based systems are more stable than others; if a server
crashes, it can be restarted without affecting the entire system, which couldn't be done if every system component was part of the kernel.
However, because of the overhead incurred by IPC and context-switching, microkernels are slower than traditional kernels. Due to the performance
costs of a microkernel, Microsoft decided to keep the structure of a microkernel, but run the system components in kernel space. Starting in
Windows Vista, some drivers are also run in user mode."
More about message passing..
An advantage of shared memory model is that memory communication is
faster as compared to the message passing model on the same machine.
Read the following to notice it:
"One problem that plagues microkernel implementations is relatively poor performance. The message-passing layer that connects
different operating system components introduces an extra layer of
machine instructions. The machine instruction overhead introduced
by the message-passing subsystem manifests itself as additional
execution time. In a monolithic system, if a kernel component needs
to talk to another component, it can make direct function calls
instead of going through a third party."
However, shared memory model may create problems such as synchronization
and memory protection that need to be addressed.
Message passing’s major flaw is the inversion of control–it is a moral equivalent of gotos in un-structured programming (it’s about time
somebody said that message passing is considered harmful).
Also some research shows that the total effort to write an MPI
application is significantly higher than that required to write a
shared-memory version of it.
And more about my scalable reference counting with efficient support for
weak references:
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
And now i will talk about data dependency and parallel loops..
For a loop to be parallelized, every iteration must be independent of
the others, one way to be sure of it is to execute the loop
in the direction of the incremented index of the loop and in the
direction of the decremented index of the loop and verify if the results
are the same. A data dependency happens if memory is modified: a loop
has a data dependency if an iteration writes a variable that is read or
write in another iteration of the loop. There is no data dependency if
only one iteration reads or writes a variable or if many iterations read
the same variable without modifying it. So this is the "general" "rules".
Now there remains to know that you have for example to know how to
construct the parallel for loop if there is an induction variable or if
there is a reduction operation, i will give an example of them:
If we have the following (the code looks like Algol or modern Object
Pascal):
IND:=0
For I:=1 to N
Do
Begin
IND := IND + 1;
A[I]:=B[IND];
End;
So as you are noticing since IND is an induction variable , so
to parallelize the loop you have to do the following:
For I:=1 to N
Do
Begin
IND:=(I*(I+1))/2;
A[I]:=B[IND];
End;
Now for the reduction operation example, you will notice that my
invention that is my Threadpool with priorities that scales very well (
read about it below) supports a Parallel For that scales very well that supports "grainsize", and you will notice that the grainsize can be used
in the ParallelFor() with a reduction operation and you will notice that
my following powerful scalable Adder is also used in this scenario, here
it is:
https://sites.google.com/site/scalable68/scalable-adder-for-delphi-and-freepascal
So here is the example with a reduction operation in modern Object Pascal:
TOTAL:=0.0
For I := 1 to N
Do
Begin
TOTAL:=TOTAL+A[I]
End;
So with my powerful scalable Adder and with my powerful invention that
is my ParallelFor() that scales very well, you will parallelize the
above like this:
procedure test1(j:integer;ptr:pointer);
begin
t.add(A[J]); // "t" is my scalable Adder object
end;
// Let's suppose that N is 100000
// In the following, 10000 is the grainsize
obj.ParallelFor(1,N,test1,10000,pointer(0));
TOTAL:=T.get();
And read the following to understand how to use grainsize of my Parallel
for that scales well:
About my ParallelFor() that scales very well that uses my efficient
Threadpool that scales very well:
With ParallelFor() you have to:
1- Ensure Sufficient Work
Each iteration of a loop involves a certain amount of work,
so you have to ensure a sufficient amount of the work,
read below about "grainsize" that i have implemented.
2- In OpenMP we have that:
Static and Dynamic Scheduling
One basic characteristic of a loop schedule is whether it is static or
dynamic:
• In a static schedule, the choice of which thread performs a particular iteration is purely a function of the iteration number and number of
threads. Each thread performs only the iterations assigned to it at the beginning of the loop.
• In a dynamic schedule, the assignment of iterations to threads can
vary at runtime from one execution to another. Not all iterations are
assigned to threads at the start of the loop. Instead, each thread
requests more iterations after it has completed the work already
assigned to it.
But with my ParallelFor() that scales very well, since it is using my
efficient Threadpool that scales very well, so it is using Round-robin scheduling and it uses also work stealing, so i think that this is
sufficient.
Read the rest:
My Threadpool engine with priorities that scales very well is really
powerful because it scales very well on multicore and NUMA systems, also
it comes with a ParallelFor() that scales very well on multicores and
NUMA systems.
You can download it from:
https://sites.google.com/site/scalable68/an-efficient-threadpool-engine-with-priorities-that-scales-very-well
Here is the explanation of my ParallelFor() that scales very well:
I have also implemented a ParallelFor() that scales very well, here is
the method:
procedure ParallelFor(nMin, nMax:integer;aProc: TParallelProc;GrainSize:integer=1;Ptr:pointer=nil;pmode:TParallelMode=pmBlocking;Priority:TPriorities=NORMAL_PRIORITY);
nMin and nMax parameters of the ParallelFor() are the minimum and
maximum integer values of the variable of the ParallelFor() loop, aProc parameter of ParallelFor() is the procedure to call, and GrainSize
integer parameter of ParallelFor() is the following:
The grainsize sets a minimum threshold for parallelization.
A rule of thumb is that grainsize iterations should take at least
100,000 clock cycles to execute.
For example, if a single iteration takes 100 clocks, then the grainsize
needs to be at least 1000 iterations. When in doubt, do the following experiment:
1- Set the grainsize parameter higher than necessary. The grainsize is specified in units of loop iterations.
If you have no idea of how many clock cycles an iteration might take,
start with grainsize=100,000.
The rationale is that each iteration normally requires at least one
clock per iteration. In most cases, step 3 will guide you to a much
smaller value.
2- Run your algorithm.
3- Iteratively halve the grainsize parameter and see how much the
algorithm slows down or speeds up as the value decreases.
A drawback of setting a grainsize too high is that it can reduce
parallelism. For example, if the grainsize is 1000 and the loop has 2000 iterations, the ParallelFor() method distributes the loop across only
two processors, even if more are available.
And you can pass a parameter in Ptr as pointer to ParallelFor(), and you
can set pmode parameter of to pmBlocking so that ParallelFor() is
blocking or to pmNonBlocking so that ParallelFor() is non-blocking, and
the Priority parameter is the priority of ParallelFor(). Look inside the test.pas example to see how to use it.
Thank you,
Amine Moulay Ramdane.
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