D programming without the garbage collector - garbage-collection

I've been looking at D today and on the surface it looks quite amazing. I like how it includes many higher level constructs directly in the language so silly hacks or terse methods don't have to be used. One thing that really worries me if the GC. I know this is a big issues and have read many discussions about it.
My own simple tests sprouted from a question here shows that the GC is extremely slow. Over 10 times slower than straight C++ doing the same thing. (obviously the test does not directly convert into real world but the performance hit is extreme and would slow down real world happens that behave similarly(allocating many small objects quickly)
I'm looking into writing a real time low latency audio application and it is possible that the GC will ruin the performance of the application to make it nearly useless. In a sense, if it has any issues it will ruin the real time audio aspect which is much more crucial since, unlike graphics, audio runs at a much higher frame rate(44000+ vs 30-60). (due to it's low latency it is more crucial than a standard audio player which can buffer significant amounts of data)
Disabling the GC improved the results to within about 20% of the C++ code. This is significant. I'll give the code at the end for analysis.
My questions are:
How difficult is it to replace D's GC with a standard smart pointers implementation so that libraries that rely on the GC can still be used. If I remove GC completely I'll lose a lot of grunt work, as D already has limit libraries compared to C++.
Does GC.Disable only halt the garbage collection temporarily(preventing the GC thread from running) and GC.Enable pick back up where it left off. So I could potentially disable the GC from running in high cpu usage moments to prevent latency issues.
Is there any way to enforce a pattern to not use GC consistently. (this is because I've not programming in D and when I start writing my glasses that do not use the GC I would like to be sure I don't forget to implement their own clean up.
Is it possible to replace the GC in D easily? (not that I want to but it might be fun to play around with different methods of GC one day... this is similar to 1 I suppose)
What I'd like to do is trade memory for speed. I do not need the GC to run every few seconds. In fact, if I can properly implement my own memory management for my data structures then chances are it will not need to run very often at all. I might need to run it only when memory becomes scarce. From what I've read, though, the longer you wait to call it the slower it will be. Since there generally will be times in my application where I can get away with calling it without issues this will help alleviate some of the pressure(but then again, there might be hours when I won't be able to call it).
I am not worried about memory constraints as much. I'd prefer to "waste" memory over speed(up to a point, of course). First and foremost is the latency issues.
From what I've read, I can, at the very least, go the route of C/C++ as long as I don't use any libraries or language constructs that rely on the GC. The problem is, I do not know the ones that do. I've seen string, new, etc mentioned but does that mean I can't use the build in strings if I don't enable the GC?
I've read in some bug reports that the GC might be really buggy and that could explain its performance problems?
Also, D uses a bit more memory, in fact, D runs out of memory before the C++ program. I guess it is about 15% more or so in this case. I suppose that is for the GC.
I realize the following code is not representative of your average program but what it says is that when programs are instantiating a lot of objects(say, at startup) they will be much slower(10 times is a large factor). Of the GC could be "paused" at startup then it wouldn't necessarily be an issue.
What would really be nice is if I could somehow have the compiler automatically GC a local object if I do not specifically deallocate it. This almost give the best of both worlds.
e.g.,
{
Foo f = new Foo();
....
dispose f; // Causes f to be disposed of immediately and treats f outside the GC
// If left out then f is passed to the GC.
// I suppose this might actually end up creating two kinds of Foo
// behind the scenes.
Foo g = new manualGC!Foo(); // Maybe something like this will keep GC's hands off
// g and allow it to be manually disposed of.
}
In fact, it might be nice to actually be able to associate different types of GC's with different types of data with each GC being completely self contained. This way I could tailor the performance of the GC to my types.
Code:
module main;
import std.stdio, std.conv, core.memory;
import core.stdc.time;
class Foo{
int x;
this(int _x){x=_x;}
}
void main(string args[])
{
clock_t start, end;
double cpu_time_used;
//GC.disable();
start = clock();
//int n = to!int(args[1]);
int n = 10000000;
Foo[] m = new Foo[n];
foreach(i; 0..n)
//for(int i = 0; i<n; i++)
{
m[i] = new Foo(i);
}
end = clock();
cpu_time_used = (end - start);
cpu_time_used = cpu_time_used / 1000.0;
writeln(cpu_time_used);
getchar();
}
C++ code
#include <cstdlib>
#include <iostream>
#include <time.h>
#include <math.h>
#include <stdio.h>
using namespace std;
class Foo{
public:
int x;
Foo(int _x);
};
Foo::Foo(int _x){
x = _x;
}
int main(int argc, char** argv) {
int n = 120000000;
clock_t start, end;
double cpu_time_used;
start = clock();
Foo** gx = new Foo*[n];
for(int i=0;i<n;i++){
gx[i] = new Foo(i);
}
end = clock();
cpu_time_used = (end - start);
cpu_time_used = cpu_time_used / 1000.0;
cout << cpu_time_used;
std::cin.get();
return 0;
}

D can use pretty much any C library, just define the functions needed. D can also use C++ libraries, but D does not understand certain C++ constructs. So... D can use almost as many libraries as C++. They just aren't native D libs.
From D's Library reference.
Core.memory:
static nothrow void disable();
Disables automatic garbage collections performed to minimize the process footprint. Collections may continue to occur in instances where the implementation deems necessary for correct program behavior, such as during an out of memory condition. This function is reentrant, but enable must be called once for each call to disable.
static pure nothrow void free(void* p);
Deallocates the memory referenced by p. If p is null, no action occurs. If p references memory not originally allocated by this garbage collector, or if it points to the interior of a memory block, no action will be taken. The block will not be finalized regardless of whether the FINALIZE attribute is set. If finalization is desired, use delete instead.
static pure nothrow void* malloc(size_t sz, uint ba = 0);
Requests an aligned block of managed memory from the garbage collector. This memory may be deleted at will with a call to free, or it may be discarded and cleaned up automatically during a collection run. If allocation fails, this function will call onOutOfMemory which is expected to throw an OutOfMemoryError.
So yes. Read more here: http://dlang.org/garbage.html
And here: http://dlang.org/memory.html
If you really need classes, look at this: http://dlang.org/memory.html#newdelete
delete has been deprecated, but I believe you can still free() it.
Don't use classes, use structs. Structs are stack allocated, classes are heap. Unless you need polymorphism or other things classes support, they are overhead for what you are doing. You can use malloc and free if you want to.
More or less... fill out the function definitions here: https://github.com/D-Programming-Language/druntime/blob/master/src/gcstub/gc.d . There's a GC proxy system set up to allow you to customize the GC. So it's not like it is something that the designers do not want you to do.
Little GC knowledge here:
The garbage collector is not guaranteed to run the destructor for all unreferenced objects. Furthermore, the order in which the garbage collector calls destructors for unreference objects is not specified. This means that when the garbage collector calls a destructor for an object of a class that has members that are references to garbage collected objects, those references may no longer be valid. This means that destructors cannot reference sub objects. This rule does not apply to auto objects or objects deleted with the DeleteExpression, as the destructor is not being run by the garbage collector, meaning all references are valid.
import std.c.stdlib; that should have malloc and free.
import core.memory; this has GC.malloc, GC.free, GC.addroots, //add external memory to GC...
strings require the GC because they are dynamic arrays of immutable chars. ( immutable(char)[] ) Dynamic arrays require GC, static do not.
If you want manual management, go ahead.
import std.c.stdlib;
import core.memory;
char* one = cast(char*) GC.malloc(char.sizeof * 8);.
GC.free(one);//pardon me, I'm not used to manual memory management.
//I am *asking* you to edit this to fix it, if it needs it.
why create a wrapper class for an int? you are doing nothing more than slowing things down and wasting memory.
class Foo { int n; this(int _n){ n = _n; } }
writeln(Foo.sizeof); //it's 8 bytes, btw
writeln(int.sizeof); //Its *half* the size of Foo; 4 bytes.
Foo[] m;// = new Foo[n]; //8 sec
m.length=n; //7 sec minor optimization. at least on my machine.
foreach(i; 0..n)
m[i] = new Foo(i);
int[] m;
m.length=n; //nice formatting. and default initialized to 0
//Ooops! forgot this...
foreach(i; 0..n)
m[i] = i;//.145 sec
If you really need to, then write the Time-sensitive function in C, and call it from D.
Heck, if time is really that big of a deal, use D's inline assembly to optimize everything.

I suggest you read this article: http://3d.benjamin-thaut.de/?p=20
There you will find a version of the standard library that does own memory management and completely avoids garbage collection.

D's GC simply isn't as sophisticated as others like Java's. It's open-source so anyone can try to improve it.
There is an experimental concurrent GC named CDGC and there is a current GSoC project to remove the global lock: http://www.google-melange.com/gsoc/project/google/gsoc2012/avtuunainen/17001
Make sure to use LDC or GDC for compilation to get better optimized code.
The XomB project also uses a custom runtime but it's D version 1 I think.
http://wiki.xomb.org/index.php?title=Main_Page

You can also just allocate all memory blocks you need then use a memory pool to get blocks without the GC.
And by the way, it’s not as slow as you mentionned. And GC.disable() doesn’t really disable it.

We might look at the problem from a bit different view. Suboptimal performance of allocating many little objects, which you mention as a rationale for the question, has little to do with GC alone. Rather, it's a matter of balance between general-purpose (but suboptimal) and highly-performant (but task-specialised) memory management tools. The idea is: presence of GC doesn't prevent you from writing a real-time app, you just have to use more specific tools (say, object pools) for special cases.

Since this hasn't been closed yet, recent versions of D have the std.container library which contains an Array data structure that is significantly more efficient with respect to memory than the built-in arrays. I can't confirm that the other data structures in the library are also efficient, but it may be worth looking into if you need to be more memory conscious without having to resort to manually creating data structures that don't require garbage collection.

D is constantly evolving. Most of the answers here are 9+ years old, so I figured I'd answer these questions again for anyone curious what the current situation is.
(...) replace D's GC with a standard smart pointers implementation so that libraries that rely on the GC can still be used. (...)
Replacing the GC itself with smart pointers is not something I've looked into (i.e. where new creates a smart pointer). There are several D libraries that add smart pointers. You can interface with any C library. Interfacing with C++ and even Objective-C is also supported to some degree, so that should cover you pretty well.
Does GC.disable only halt the garbage collection temporarily (preventing the GC thread from running) and GC.enable pick back up where it left off. (...)
"Collections may continue to occur in instances where the implementation deems necessary for correct program behaviour, such as during an out of memory condition."
[source]
So mostly, yes. You can also manually invoke collection during down-time.
Is there any way to enforce a pattern to not use GC consistently. (...) when I start writing my classes that do not use the GC I would like to (...)
Classes are always allocated on the GC and are reference types. Structs should be used instead. However, keep in mind that structs are value types, so by default they're copied when being moved. You can #disable the copy constructor if you don't like this behaviour, but then your struct won't be POD.
What you're probably looking for is #nogc, which is a function attribute that stops a function from using the GC. You can't mark a struct type as #nogc, but you can mark each of its methods as #nogc. Just keep in mind that #nogc code can't call GC code. There's also nothrow.
If you intend to never use GC, you ought to look into Better C. It's a D language setting that removes all of D's runtime, standard library (Phobos), GC and all GC-reliant features (namely associative arrays and exceptions) in favour of using C's runtime and the C Standard Library.
Is it possible to replace the GC in D (...)
Yes it is: https://dlang.org/spec/garbage.html#gc_registry
And you can configure the pre-existing GC to better suit your needs if you don't want to make your own GC.

Related

Memory barrier in the implementation of single producer single consumer

The following implementation from Wikipedia:
volatile unsigned int produceCount = 0, consumeCount = 0;
TokenType buffer[BUFFER_SIZE];
void producer(void) {
while (1) {
while (produceCount - consumeCount == BUFFER_SIZE)
sched_yield(); // buffer is full
buffer[produceCount % BUFFER_SIZE] = produceToken();
// a memory_barrier should go here, see the explanation above
++produceCount;
}
}
void consumer(void) {
while (1) {
while (produceCount - consumeCount == 0)
sched_yield(); // buffer is empty
consumeToken(buffer[consumeCount % BUFFER_SIZE]);
// a memory_barrier should go here, the explanation above still applies
++consumeCount;
}
}
says that a memory barrier must be used between the line that accesses the buffer and the line that updates the Count variable.
This is done to prevent the CPU from reordering the instructions above the fence along-with that below it. The Count variable shouldn't be incremented before it is used to index into the buffer.
If a fence is not used, won't this kind of reordering violate the correctness of code? The CPU shouldn't perform increment of Count before it is used to index into buffer. Does the CPU not take care of data dependency while instruction reordering?
Thanks
If a fence is not used, won't this kind of reordering violate the correctness of code? The CPU shouldn't perform increment of Count before it is used to index into buffer. Does the CPU not take care of data dependency while instruction reordering?
Good question.
In c++, unless some form of memory barrier is used (atomic, mutex, etc), the compiler assumes that the code is single-threaded. In which case, the as-if rule says that the compiler may emit whatever code it likes, provided that the overall observable effect is 'as if' your code was executed sequentially.
As mentioned in the comments, volatile does not necessarily alter this, being merely an implementation-defined hint that the variable may change between accesses (this is not the same as being modified by another thread).
So if you write multi-threaded code without memory barriers, you get no guarantees that changes to a variable in one thread will even be observed by another thread, because as far as the compiler is concerned that other thread should not be touching the same memory, ever.
What you will actually observe is undefined behaviour.
It seems, that your question is "can incrementing Count and assigment to buffer be reordered without changing code behavior?".
Consider following code tansformation:
int count1 = produceCount++;
buffer[count1 % BUFFER_SIZE] = produceToken();
Notice that code behaves exactly as original one: one read from volatile variable, one write to volatile, read happens before write, state of program is the same. However, other threads will see different picture regarding order of produceCount increment and buffer modifications.
Both compiler and CPU can do that transformation without memory fences, so you need to force those two operations to be in correct order.
If a fence is not used, won't this kind of reordering violate the correctness of code?
Nope. Can you construct any portable code that can tell the difference?
The CPU shouldn't perform increment of Count before it is used to index into buffer. Does the CPU not take care of data dependency while instruction reordering?
Why shouldn't it? What would the payoff be for the costs incurred? Things like write combining and speculative fetching are huge optimizations and disabling them is a non-starter.
If you're thinking that volatile alone should do it, that's simply not true. The volatile keyword has no defined thread synchronization semantics in C or C++. It might happen to work on some platforms and it might happen not to work on others. In Java, volatile does have defined thread synchronization semantics, but they don't include providing ordering for accesses to non-volatiles.
However, memory barriers do have well-defined thread synchronization semantics. We need to make sure that no thread can see that data is available before it sees that data. And we need to make sure that a thread that marks data as able to be overwritten is not seen before the thread is finished with that data.

Speed-up from multi-threading

I have a highly parallelizable problem. Hundreds of separate problems need to be solved by the same function. The problems each take an average of perhaps 120 ms (0.12 s) on a single core, but there is substantial variation, and some extreme and rare ones may take 10 times as long. Each problem needs memory, but this is allocated ahead of time. The problems do not need disk I/O, and they do not pass back and forth any variables once they are running. They do access different parts (array elements) of the same global struct, though.
I have C++ code, based on someone else's code, that works. (The global array of structs is not shown.) It runs 20 problems (for instance) and then returns. I think 20 is enough to even out the variability on 4 cores. I see the execution time flattening out from about 10 already.
There is a Win32 and an OpenMP version, and they behave almost identically in terms of execution time. I run the program on a 4-core Windows system. I include some OpenMP code below since it is shorter. (I changed names etc. to make it more generic and I may have made mistakes -- it won't compile stand-alone.)
The speed-up over the single-threaded version flattens out at about a factor of 2.3. So if it takes 230 seconds single-threaded, it takes 100 s multi-threaded. I am surprised that the speed-up is not a lot closer to 4, the number of cores.
Am I right to be disappointed?
Is there anything I can do to get closer to my theoretical expectation?
int split_bigtask(Inputs * inputs, Outputs * results)
{
for (int k = 0; k < MAXNO; k++)
results->solved[k].value = 0;
int res;
#pragma omp parallel shared(inputs, outputs)
{
#pragma omp for schedule(dynamic)
for (int k = 0; k < inputs->no; k++)
{
res = bigtask(inputs->values[k],
outputs->solved[k],
omp_get_thread_num()
);
}
}
return TRUE;
}
I Assume that there is no synchronization done within bigtask() (Obvious, but I'd still check it first).
It's possible that you run into a "dirty cache" problem: If you manipulate data that is close to each other (e.g. same cache line!) from multiple cores each manipulation will mark the cache line as dirty (which means that the processor needs to signal this to all other processeors which in turn involves synchronization again...).
you create too many threads (allocating a thread is quite an overhead. So creating one thread for each core is a lot more efficient than creating 5 threads each).
I personally would assume that you have case 2 ("Big Global Array").
Solution to the problem (if it's indeed case 2):
Write the results to a local array which is merged into the "Big Global Array" by the main thread after the end of the work
Split the global array into several smaller arrays (and give each thread one of these arrays)
Ensure that the records within the structure align on Cache-Line boundaries (this is a bit a hack since cache line boundaries may change for future processors)
You may want to try to create a local copy of the array for each thread (at least for the results)

How/when to release memory in wait-free algorithms

I'm having trouble figuring out a key point in wait-free algorithm design. Suppose a data structure has a pointer to another data structure (e.g. linked list, tree, etc), how can the right time for releasing a data structure?
The problem is this, there are separate operations that can't be executed atomically without a lock. For example one thread reads the pointer to some memory, and increments the use count for that memory to prevent free while this thread is using the data, which might take long, and even if it doesn't, it's a race condition. What prevents another thread from reading the pointer, decrementing the use count and determining that it's no longer used and freeing it before the first thread incremented the use count?
The main issue is that current CPUs only have a single word CAS (compare & swap). Alternatively the problem is that I'm clueless about waitfree algorithms and data structures and after reading some papers I'm still not seeing the light.
IMHO Garbage collection can't be the answer, because it would either GC would have to be prevented from running if any single thread is inside an atomic block (which would mean it can't be guaranteed that the GC will ever run again) or the problem is simply pushed to the GC, in which case, please explain how the GC would figure out if the data is in the silly state (a pointer is read [e.g. stored in a local variable] but the the use count didn't increment yet).
PS, references to advanced tutorials on wait-free algorithms for morons are welcome.
Edit: You should assume that the problem is being solved in a non-managed language, like C or C++. After all if it were Java, we'd have no need to worry about releasing memory. Further assume that the compiler may generate code that will store temporary references to objects in registers (invisible to other threads) right before the usage counter increment, and that a thread can be interrupted between loading the object address and incrementing the counter. This of course doesn't mean that the solution must be limited to C or C++, rather that the solution should give a set of primitives that allowing the implementation of wait-free algorithms on linked data structures. I'm interested in the primitives and how they solve the problem of designing wait-free algorithms. With such primitives a wait-free algorithm can be implemented equally well in C++ and Java.
After some research I learned this.
The problem is not trivial to solve and there are several solutions each with advantages and disadvantages. The reason for the complexity comes from inter CPU synchronization issues. If not done right it might appear to work correctly 99.9% of the time, which isn't enough, or it might fail under load.
Three solutions that I found are 1) hazard pointers, 2) quiescence period based reclamation (used by the Linux kernel in the RCU implementation) 3) reference counting techniques. 4) Other 5) Combinations
Hazard pointers work by saving the currently active references in a well-known per thread location, so any thread deciding to free memory (when the counter appears to be zero) can check if the memory is still in use by anyone. An interesting improvement is to buffer request to release memory in a small array and free them up in a batch when the array is full. The advantage of using hazard pointers is that it can actually guarantee an upper bound on unreclaimed memory. The disadvantage is that it places extra burden on the reader.
Quiescence period based reclamation works by delaying the actual release of the memory until it's known that each thread has had a chance to finish working on any data that may need to be released. The way to know that this condition is satisfied is to check if each thread passed through a quiescent period (not in a critical section) after the object was removed. In the Linux kernel this means something like each task making a voluntary task switch. In a user space application it would be the end of a critical section. This can be achieved by a simple counter, each time the counter is even the thread is not in a critical section (reading shared data), each time the counter is odd the thread is inside a critical section, to move from a critical section or back all the thread needs to do is to atomically increment the number. Based on this the "garbage collector" can determine if each thread has had a chance to finish. There are several approaches, one simple one would be to queue up the requests to free memory (e.g. in a linked list or an array), each with the current generation (managed by the GC), when the GC runs it checks the state of the threads (their state counters) to see if each passed to the next generation (their counter is higher than the last time or is the same and even), any memory can be reclaimed one generation after it was freed. The advantage of this approach is that is places the least burden on the reading threads. The disadvantage is that it can't guarantee an upper bound for the memory waiting to be released (e.g. one thread spending 5 minutes in a critical section, while the data keeps changing and memory isn't released), but in practice it works out all right.
There is a number of reference counting solutions, many of them require double compare and swap, which some CPUs don't support, so can't be relied upon. The key problem remains though, taking a reference before updating the counter. I didn't find enough information to explain how this can be done simply and reliably though. So .....
There are of course a number of "Other" solutions, it's a very important topic of research with tons of papers out there. I didn't examine all of them. I only need one.
And of course the various approaches can be combined, for example hazard pointers can solve the problems of reference counting. But there's a nearly infinite number of combinations, and in some cases a spin lock might theoretically break wait-freedom, but doesn't hurt performance in practice. Somewhat like another tidbit I found in my research, it's theoretically not possible to implement wait-free algorithms using compare-and-swap, that's because in theory (purely in theory) a CAS based update might keep failing for non-deterministic excessive times (imagine a million threads on a million cores each trying to increment and decrement the same counter using CAS). In reality however it rarely fails more than a few times (I suspect it's because the CPUs spend more clocks away from CAS than there are CPUs, but I think if the algorithm returned to the same CAS on the same location every 50 clocks and there were 64 cores there could be a chance of a major problem, then again, who knows, I don't have a hundred core machine to try this). Another results of my research is that designing and implementing wait-free algorithms and data-structures is VERY challenging (even if some of the heavy lifting is outsourced, e.g. to a garbage collector [e.g. Java]), and might perform less well than a similar algorithm with carefully placed locks.
So, yeah, it's possible to free memory even without delays. It's just tricky. And if you forget to make the right operations atomic, or to place the right memory barrier, oh, well, you're toast. :-) Thanks everyone for participating.
I think atomic operations for increment/decrement and compare-and-swap would solve this problem.
Idea:
All resources have a counter which is modified with atomic operations. The counter is initially zero.
Before using a resource: "Acquire" it by atomically incrementing its counter. The resource can be used if and only if the incremented value is greater than zero.
After using a resource: "Release" it by atomically decrementing its counter. The resource should be disposed/freed if and only if the decremented value is equal to zero.
Before disposing: Atomically compare-and-swap the counter value with the minimum (negative) value. Dispose will not happen if a concurrent thread "Acquired" the resource in between.
You haven't specified a language for your question. Here goes an example in c#:
class MyResource
{
// Counter is initially zero. Resource will not be disposed until it has
// been acquired and released.
private int _counter;
public bool Acquire()
{
// Atomically increment counter.
int c = Interlocked.Increment(ref _counter);
// Resource is available if the resulting value is greater than zero.
return c > 0;
}
public bool Release()
{
// Atomically decrement counter.
int c = Interlocked.Decrement(ref _counter);
// We should never reach a negative value
Debug.Assert(c >= 0, "Resource was released without being acquired");
// Dispose when we reach zero
if (c == 0)
{
// Mark as disposed by setting counter its minimum value.
// Only do this if the counter remain at zero. Atomic compare-and-swap operation.
if (Interlocked.CompareExchange(ref _counter, int.MinValue, c) == c)
{
// TODO: Run dispose code (free stuff)
return true; // tell caller that resource is disposed
}
}
return false; // released but still in use
}
}
Usage:
// "r" is an instance of MyResource
bool acquired = false;
try
{
if (acquired = r.Acquire())
{
// TODO: Use resource
}
}
finally
{
if (acquired)
{
if (r.Release())
{
// Resource was disposed.
// TODO: Nullify variable or similar to let GC collect it.
}
}
}
I know this is not the best way but it works for me:
for shared dynamic data-structure lists I use usage counter per item
for example:
struct _data
{
DWORD usage;
bool delete;
// here add your data
_data() { usage=0; deleted=true; }
};
const int MAX = 1024;
_data data[MAX];
now when item is started to be used somwhere then
// start use of data[i]
data[i].cnt++;
after is no longer used then
// stop use of data[i]
data[i].cnt--;
if you want to add new item to list then
// add item
for (i=0;i<MAX;i++) // find first deleted item
if (data[i].deleted)
{
data[i].deleted=false;
data[i].cnt=0;
// copy/set your data
break;
}
and now in the background once in a while (on timer or whatever)
scann data[] an all undeleted items with cnt == 0 set as deleted (+ free its dynamic memory if it has any)
[Note]
to avoid multi-thread access problems implement single global lock per data list
and program it so you cannot scann data while any data[i].cnt is changing
one bool and one DWORD suffice for this if you do not want to use OS locks
// globals
bool data_cnt_locked=false;
DWORD data_cnt=0;
now any change of data[i].cnt modify like this:
// start use of data[i]
while (data_cnt_locked) Sleep(1);
data_cnt++;
data[i].cnt++;
data_cnt--;
and modify delete scan like this
while (data_cnt) Sleep(1);
data_cnt_locked=true;
Sleep(1);
if (data_cnt==0) // just to be sure
for (i=0;i<MAX;i++) // here scan for items to delete ...
if (!data[i].cnt)
if (!data[i].deleted)
{
data[i].deleted=true;
data[i].cnt=0;
// release your dynamic data ...
}
data_cnt_locked=false;
PS.
do not forget to play with the sleep times a little to suite your needs
lock free algorithm sleep times are sometimes dependent on OS task/scheduler
this is not really an lock free implementation
because while GC is at work then all is locked
but if ather than that multi access is not blocking to each other
so if you do not run GC too often you are fine

Is this a safe version of double-checked locking?

Slightly modified version of canonical broken double-checked locking from Wikipedia:
class Foo {
private Helper helper = null;
public Helper getHelper() {
if (helper == null) {
synchronized(this) {
if (helper == null) {
// Create new Helper instance and store reference on
// stack so other threads can't see it.
Helper myHelper = new Helper();
// Atomically publish this instance.
atomicSet(helper, myHelper);
}
}
}
return helper;
}
}
Does simply making the publishing of the newly created Helper instance atomic make this double checked locking idiom safe, assuming that the underlying atomic ops library works properly? I realize that in Java, one could just use volatile, but even though the example is in pseudo-Java, this is supposed to be a language-agnostic question.
See also:
Double checked locking Article
It entirely depends on the exact memory model of your platform/language.
My rule of thumb: just don't do it. Lock-free (or reduced lock, in this case) programming is hard and shouldn't be attempted unless you're a threading ninja. You should only even contemplate it when you've got profiling proof that you really need it, and in that case you get the absolute best and most recent book on threading for that particular platform and see if it can help you.
I don't think you can answer the question in a language-agnostic fashion without getting away from code completely. It all depends on how synchronized and atomicSet work in your pseudocode.
The answer is language dependent - it comes down to the guarantees provided by atomicSet().
If the construction of myHelper can be spread out after the atomicSet() then it doesn't matter how the variable is assigned to the shared state.
i.e.
// Create new Helper instance and store reference on
// stack so other threads can't see it.
Helper myHelper = new Helper(); // ALLOCATE MEMORY HERE BUT DON'T INITIALISE
// Atomically publish this instance.
atomicSet(helper, myHelper); // ATOMICALLY POINT UNINITIALISED MEMORY from helper
// other thread gets run at this time and tries to use helper object
// AT THE PROGRAMS LEISURE INITIALISE Helper object.
If this is allowed by the language then the double checking will not work.
Using volatile would not prevent a multiple instantiations - however using the synchronize will prevent multiple instances being created. However with your code it is possible that helper is returned before it has been setup (thread 'A' instantiates it, but before it is setup thread 'B' comes along, helper is non-null and so returns it straight away. To fix that problem, remove the first if (helper == null).
Most likely it is broken, because the problem of a partially constructed object is not addressed.
To all the people worried about a partially constructed object:
As far as I understand, the problem of partially constructed objects is only a problem within constructors. In other words, within a constructor, if an object references itself (including it's subclass) or it's members, then there are possible issues with partial construction. Otherwise, when a constructor returns, the class is fully constructed.
I think you are confusing partial construction with the different problem of how the compiler optimizes the writes. The compiler can choose to A) allocate the memory for the new Helper object, B) write the address to myHelper (the local stack variable), and then C) invoke any constructor initialization. Anytime after point B and before point C, accessing myHelper would be a problem.
It is this compiler optimization of the writes, not partial construction that the cited papers are concerned with. In the original single-check lock solution, optimized writes can allow multiple threads to see the member variable between points B and C. This implementation avoids the write optimization issue by using a local stack variable.
The main scope of the cited papers is to describe the various problems with the double-check lock solution. However, unless the atomicSet method is also synchronizing against the Foo class, this solution is not a double-check lock solution. It is using multiple locks.
I would say this all comes down to the implementation of the atomic assignment function. The function needs to be truly atomic, it needs to guarantee that processor local memory caches are synchronized, and it needs to do all this at a lower cost than simply always synchronizing the getHelper method.
Based on the cited paper, in Java, it is unlikely to meet all these requirements. Also, something that should be very clear from the paper is that Java's memory model changes frequently. It adapts as better understanding of caching, garbage collection, etc. evolve, as well as adapting to changes in the underlying real processor architecture that the VM runs on.
As a rule of thumb, if you optimize your Java code in a way that depends on the underlying implementation, as opposed to the API, you run the risk of having broken code in the next release of the JVM. (Although, sometimes you will have no choice.)
dsimcha:
If your atomicSet method is real, then I would try sending your question to Doug Lea (along with your atomicSet implementation). I have a feeling he's the kind of guy that would answer. I'm guessing that for Java he will tell you that it's cheaper to always synchronize and to look to optimize somewhere else.

examples of garbage collection bottlenecks

I remembered someone telling me one good one. But i cannot remember it. I spent the last 20mins with google trying to learn more.
What are examples of bad/not great code that causes a performance hit due to garbage collection ?
from an old sun tech tip -- sometimes it helps to explicitly nullify references in order to make them eligible for garbage collection earlier:
public class Stack {
private static final int MAXLEN = 10;
private Object stk[] = new Object[MAXLEN];
private int stkp = -1;
public void push(Object p) {stk[++stkp] = p;}
public Object pop() {return stk[stkp--];}
}
rewriting the pop method in this way helps ensure that garbage collection gets done in a timely fashion:
public Object pop() {
Object p = stk[stkp];
stk[stkp--] = null;
return p;
}
What are examples of bad/not great code that causes a performance hit due to garbage collection ?
The following will be inefficient when using a generational garbage collector:
Mutating references in the heap because write barriers are significantly more expensive than pointer writes. Consider replacing heap allocation and references with an array of value types and an integer index into the array, respectively.
Creating long-lived temporaries. When they survive the nursery generation they must be marked, copied and all pointers to them updated. If it is possible to coalesce updates in order to reuse of an old version of a collection, do so.
Complicated heap topologies. Again, consider replacing many references with indices.
Deep thread stacks. Try to keep stacks shallow to make it easier for the GC to collate the global roots.
However, I would not call these "bad" because there is nothing objectively wrong with them. They are only inefficient when used with this kind of garbage collector. With manual memory management, none of the issues arise (although many are replaced with equivalent issues, e.g. performance of malloc vs pool allocators). With other kinds of GC some of these issues disappear, e.g. some GCs don't have a write barrier, mark-region GCs should handle long-lived temporaries better, not all VMs need thread stacks.
When you have some loop involving the creation of new object's instances: if the number of cycles is very high you procuce a lot of trash causing the Garbage Collector to run more frequently and so decreasing performance.
One example would be object references that are kept in member variables oder static variables. Here is an example:
class Something {
static HugeInstance instance = new HugeInstance();
}
The problem is the garbage collector has no way of knowing, when this instance is not needed anymore. So its usually better to keep things in local variables and have small functions.
String foo = new String("a" + "b" + "c");
I understand Java is better about this now, but in the early days that would involve the creation and destruction of 3 or 4 string objects.
I can give you an example that will work with the .Net CLR GC:
If you override a finalize method from a class and do not call the super class Finalize method such as
protected override void Finalize(){
Console.WriteLine("Im done");
//base.Finalize(); => you should call him!!!!!
}
When you resurrect an object by accident
protected override void Finalize(){
Application.ObjJolder = this;
}
class Application{
static public object ObjHolder;
}
When you use an object that uses Finalize it takes two GC collections to get rid of the data, and in any of the above codes you won't delete it.
frequent memory allocations
lack of memory reusing (when dealing with large memory chunks)
keeping objects longer than needed (keeping references on obsolete objects)
In most modern collectors, any use of finalization will slow the collector down. And not just for the objects that have finalizers.
Your custom service does not have a load limiter on it, so:
A lot requests come in for some reason at the same time (everyone logs on in the morning say)
The service takes longer to process each requests as it now has 100s of threads (1 per request)
Yet more part processed requests builds up due to the longer processing time.
Each part processed request has created lots of objects that live until the end of processing that request.
The garbage collector spends lots of time trying to free memory it, however it can’t due to the above.
Yet more part processed requests builds up due to the longer processing time…. (including time in GC)
I have encountered a nice example while doing some parallel cell based simulation in Python. Cells are initialized and sent to worker processes after pickling for running. If you have too many cells at any one time the master node runs out of ram. The trick is to make a limited number of cells pack them and send them off to cluster nodes before making some more, remember to set the objects already sent off to "None". This allows you to perform large simulations using the total RAM of the cluster in addition to the computing power.
The application here was cell based fire simulation, only the cells actively burning were kept as objects at any one time.

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