What is the overhead of generating a lot of temporary objects (i.e. for interim results) that "die young" (never promoted to the next generation during a garbage collection interval)? I'm assuming that the "new" operation is very cheap, as it is really just a pointer increment. However, what are the hidden costs of dealing with this temporary "litter"?
Not a lot - the garbage collector is very fast for gen0. It also tunes itself, adjusting the size of gen0 depending on how much it manages to collect each time it goes. (If it's managed to collect a lot, it will reduce the size of gen0 to collect earlier next time, and vice versa.)
The ultimate test is how your application performs though. Perfmon is very handy here, showing how much time has been spent in GC, how many collections there have been of each generation etc.
As you say the allocation itself is very inexpensive. The cost of generating lots of short lived objects is more frequent garbage collections as they are triggered when generation 0's budget is exhausted. However, a generation 0 collection is fairly cheap, so as long as your object really are short lived the overhead is most likely not significant.
On the other hand the common example of concatenating lots of strings in a loop pushes the garbage collector significantly, so it all depends on the number of objects you create. It doesn't hurt to think about allocation.
The cost of garbage collection is that managed threads are suspended during compaction.
In general, this isn't something you should probably be worrying about and sounds like it starts to fall very close to "micro-optimization". The GC was designed with an assumption that a "well tuned application" will have all of it's allocations in Gen0 - meaning that they all "die young". Any time you allocate a new object it is always in Gen0. A collection won't occur until the Gen0 threshold is passed and there isn't enough available space in Gen0 to hold the next allocation.
The "new" operation is actually a bunch of things:
allocating memory
running the types constructor
returning a pointer to the memory
incrementing the next object pointer
Although the new operation is designed and written efficiently it is not free and does take time to allocate new memory. The memory allocation library needs to track what chunks are available for allocation and the newly allocated memory is zeroed.
Creating a lot of objects that die young will also trigger garbage collection more often and that operation can be expensive. Especially with "stop the world" garbage collectors.
Here's an article from the MSDN on how it works:
http://msdn.microsoft.com/en-us/magazine/bb985011.aspx
Note: that it describes how calling garbage collection is expensive because it needs to build the object graph before it can start garbage collection.
If these objects are never promoted out of Generation 0 then you will see pretty good performance. The only hidden cost I can see is that if you exceed your Generation 0 budget you will force the GC to compact the heap but the GC will self-tune so this isn't much of a concern.
Garbage collection is generational in .Net. Short lived objects will collect first and frequently. Gen 0 collection is cheap, but depending on the scale of the number of objects you're creating, it could be quite costly. I'd run a profiler to find out if it is affecting performance. If it is, consider switching them to structs. These do not need to be collected.
Related
I have a method as such
public void MainMethod()
{
while(true)
{
var largeObject = GetLargeObject();
............
//some work with this largeObject
............
//how to release the memory from largeObject here
}
}
obviously, I will use break in loop.
In that usage of this code, my memory will be full of trash pretty fast.
Is there a way to free memory that uses some object (largeObject, for example) without running garbage collector by GC.Collect()? Not just mark this object as one that can be collect by GC, but free the memory? Or GC collects largeObject as soon as iteration is over, cause it's not in use anymore?
As long as I understand the realization of IDisposable and call for Dispose() just mark the object for GC but not free the memory instantly (so the memory will be released when the GC runs)
P.S. don't say me "GC will collect everything for your", I know that
first of all. Large objects, i.e. larger than 85kb, will be allocated on the large object heap (LOH). The large object heap is only collected in gen 2 collections, i.e. collections are expensive. Because of this it is generally recommended to avoid frequent LOH allocations. If at all possible, reuse the same object, or use a memory pool to avoid frequent allocations.
There are no way to explicitly free managed memory without letting the GC do its thing. While the standard recommendation is to leave the GC alone, there are cases where it may make sense to run it manually. Like if you have recently released large amount of memory, and the performance impact of a collection is acceptable at that point in time.
If the object is small enough to fit in the Small object heap then you should not need to worry about it. Gen 1 collections are fairly cheap, and while collections will need to run more frequently if you do many allocations, the time to run a collection is not proportional to the amount of freed memory. That said, reusing memory might still be a good idea.
In the end, if you suspect it is a problem, do some profiling. Some of the profilers give you the time spent in garbage collection, and also the allocation rate. Do not try to fix a imaginary problem before confirming that it actually is a problem, and you can verify that the fix actually is an improvement.
From my anecdotal knowledge, short-lived object creation isn't too troublesome in terms of GC - implying, gen0 collections are extremely fast. Gen1/gen2 collections, however, appear to be a little more "dreaded", i.e. said to usually be a whole lot slower than gen0.
Why is that? What makes, say, a gen2 collection on average significantly slower than gen0?
I'm not aware of any structural differences between the collection approaches itself (i.e., things done in the mark/sweep/compaction phase), am I missing something? Or is it just that e.g. gen2 tends to be larger than gen0, hence more objects to check?
To amplify on canton7's answer, it's worthwhile to note a couple of additional things, one of which increases the cost of all collections (but especially gen1 and gen2) but reduces the cost of allocations between them, and one of which reduces the cost of gen0 and gen1 collections:
Many garbage collectors behave in a fashion somewhat analogous to cleaning out a building by moving everything of value to another building, dynamiting the original, and rebuilding the empty shell. A gen0 collection, which moves things from the gen0 building to the gen1 building, will be fairly fast because the gen0 "building" won't have much stuff in it. A gen2 collection would have to move everything that was in the much larger gen2 building. Garbage collection systems may use a separate building for smaller gen2 objects and larger ones, and manage the larger buildings by tracking individual regions of free space, but moving smaller objects and reclaiming storage wholesale is less work than trying to manage all the individual regions of storage that would become eligible for reuse. A key point to observe about generations here, however, is that even when it's necessary to scan a gen1 or gen2 object, it won't be necessary to move it since the "building" it's in isn't targeted for immediate demolition.
Many systems use a "card table" which can record whether each 4K chunk of memory has been written, or contains a reference that was used to modify an object, since the last gen0 or gen1 collection. This significantly slows down the first write to any such region of storage, but during a gen0 and gen1 collections, it makes it possible to skip the examination of a lot of objects. The details of how the card table are used vary, but the basic concept is that if code has a large array of references, but most of it falls within 4K blocks that aren't tagged, the GC can know without even looking in those blocks that any newer objects which would be accessible through them will also be accessible in other ways, and thus it will be possible to find all gen0 objects without bothering to look in those blocks at all.
Note that even simplistic garbage-collection systems without card tables can be simply and easily made to benefit from the principle of generational GC. For example, on Commodore 64 BASIC, whose garbage collector is horrendously slow, a program that has created lots of long-lived strings can avoid lengthy garbage-collection cycles by using a couple peek and poke statements to adjust the top-of-string-heap pointer just below the bottom of the long-lived strings so they won't be considered for relocation/reclamation. If a program uses hundreds of strings that will last throughout program execution (e.g. a table of two-digit hex strings from 00 to FF), and just a handful of other strings, this may slash garbage-collection times by more than an order of magnitude.
A couple of reasons which come to mind:
They're bigger. Collecting gen1 means also collecting gen0, and doing a gen2 collection means collecting all three. The lower generations are sized smaller as well, as gen0 is collected most frequently and so needs to be cheap.
The main cost of a collection is a function of the number of objects which survive, not the number which die. Generational garbage collectors are built around the generational hypothesis, which says that objects tend to live for a short time, or a long time, but not often in the middle. Gen0 collections by their very definition are comprised mainly of objects which die in that generation, and so collections are cheap: gen1 and gen2 collections have a higher proportion of objects which survive (gen2 should ideally be comprised only of objects which survive), and so are more expensive.
If an object is in gen0, then it can only be referenced by other gen0 objects, or by objects in higher generations which were updated to refer to it. Therefore to see whether an object in gen0 is referenced, the GC needs to check other gen0 objects, as well as only those objects in higher generations which have been updated to point to lower-generation objects (which the GC tracks, see "card tables"). To see whether a gen1 object is referenced it needs to check all of gen0 and gen1, and updated objects in gen2.
I was profiling the memory usage of a Windows Forms application in dotmemory and I noticed that for my application there were 0-4 heaps all of varying sizes as well as the large object heap.
I was just wondering if anyone had a good explanation of what each heap is for and what is typically stored in each heap?
The other answers seem to be missing the fact that there is a difference between heaps and generations. I don't see why a commercial profiler would confuse the two concepts, so I strongly suspect it's heaps and not generations after all.
When the CLR GC is using the server flavor, it creates a separate heap for each logical processor in the process' affinity mask. The reason for this breakdown is mostly to improve scalability of allocations, and to perform in GC in parallel. These are separate memory regions, but you can of course have object references between the heaps and can consider them a single logical heap.
So, assuming that you have four logical processors (e.g. an i5 CPU with HyperThreading enabled), you'll have four heaps under server GC.
The Large Object Heap has an unfortunate, confusing name. It's not a heap in the same sense as the per-processor heaps. It's a logical abstraction on top of multiple memory regions that contain large objects.
You have different heaps because of how the C# garbage collector works. It uses a generational GC, which separates data based on how recently it was used. The use of different heaps allows the garbage collector to clean up memory more efficiently.
According to MSDN:
The heap is organized into generations so it can handle long-lived and short-lived objects. Garbage collection primarily occurs with the reclamation of short-lived objects that typically occupy only a small part of the heap.
Generation 0. This is the youngest generation and contains short-lived objects. An example of a short-lived object is a temporary variable. Garbage collection occurs most frequently in this generation.
Newly allocated objects form a new generation of objects and are implicitly generation 0 collections, unless they are large objects, in which case they go on the large object heap in a generation 2 collection.
Most objects are reclaimed for garbage collection in generation 0 and do not survive to the next generation.
Generation 1. This generation contains short-lived objects and serves as a buffer between short-lived objects and long-lived objects.
Generation 2. This generation contains long-lived objects. An example of a long-lived object is an object in a server application that contains static data that is live for the duration of the process.
Objects that are not reclaimed in a garbage collection are known as survivors, and are promoted to the next generation.
Important data quickly gets put on the garbage collector's back burner (higher generations) and is checked for deletion less often. This lowers the amount of time wasted checking memory that truly needs to persist, which lets you see performance gains from an efficient garbage collector.
When it comes to managed objects, there are three Small Object Heaps(SOH) and one Large Object Heap(LOH).
Large Object Heap (LOH)
Objects that are larger than 85KB are going to LOH straight away. There are some risks if you have too many large objects. That's a different discussion, for more details have a look at The Dangers of the Large Object Heap
Small Object Heap (SOH) : Gen0, Gen1, Gen2
Garbage collector uses a clever algorithm to execute the garbage collecton only when it is required. Full garbage collection process is an expensive operation which shouldn't happen too often. So, it has broken its SOH into three parts and as you have noticed each Gen has a specified amount of memory.
Every small object (<85KB) initially going to Gen0. When Gen0 is full, garbage collection executes only for Gen0. It checks all instances that are in Gen0 and clears/releases memory that is used by any unnecessary objects(non-referenced, out of scoped or disposed objects). And then it copies all the required (in used) instances to Gen1.
Above process is actually occurs even when you execute below: (not required to call manually)
// Perform a collection of generation 0 only.
GC.Collect(0);
In this way, Garbage collector clears the memory that are allocated for short lived instances first (strings which is immutable, variables in methods or smaller scopes).
When GC is keep doing this operation at one stage, Gen1 overflows. Then it does the same operation to Gen1. It clears all the unnecessary memory in Gen1 and copies all required ones to Gen2.
Above process is occurs when you execute below manually (not required to call manually)
// Perform a collection of all generations up to and including 1.
GC.Collect(1);
When GC is keep doing this operation at one stage if Gen2 overflows it tries to clean Gen2.
Above process is occurs even when you execute below manually (not required to do manually)
// Perform a collection of all generations up to and including 2.
GC.Collect(2);
If the amount of memory needs to be copy from Gen1 to Gen2 is greater than the amount of memory available in Gen2, GC throws out of memory exception.
What is meant by Generations of Garbage Collector in C#? Is it different from the concept or is GENERATION only a term used to represent the time period?
A GC generation relates to how many garbage collections an object survives.
All objects start in generation 0. When a garbage collection occurs, and a generation N object cannot be collected, it is moved to generation N+1.
The generations are used to performance optimize garbage collection. It is generally true that generation 0:
Is a small fraction of the entire heap in size
Has a lot of short-lived objects.
Therefore, when garbage collection occurs, the garbage collector starts by collecting generation 0, which will be quick. If enough memory could be released, no need to look at the older generations, and therefore, collection can happen quickly.
Books could be written about the subject; but to start with, there is some great details in this article, or the reference here.
5% of execution time spent on GC? 10%? 25%?
Thanks.
This blog post has an interesting investigation into this area.
The posters conclusion? That the overhead was negligible for his example.
So the GC heap is so fast that in a real program, even in tight loops, you can use closures and delegates without even giving it a second’s thought (or even a few nanosecond’s thought). As always, work on a clean, safe design, then profile to find out where the overhead is.
It depends entirely on the application. The garbage collection is done as required, so the more often you allocate large amounts of memory which later becomes garbage, the more often it must run.
It could even go as low as 0% if you allocate everything up front and the never allocate any new objects.
In typical applications I would think the answer is very close to 0% of the time is spent in the garbage collector.
The overhead varies widely. It's not really practical to reduce the problem domain into "typical scenarios" because the overhead of GC (and related functions, like finalization) depend on several factors:
The GC flavor your application uses (impacts how your threads may be blocked during a GC).
Your allocation profile, including how often you allocate (GC triggers automatically when an allocation request needs more memory) and the lifetime profile of objects (gen 0 collections are fastest, gen 2 collections are slower, if you induce a lot of gen 2 collections your overhead will increase).
The lifetime profile of finalizable objects, because they must have their finalizers complete before they will be eligible for collection.
The impact of various points on each of those axes of relevancy can be analyzed (and there are probably more relevant areas I'm not recalling off the top of my head) -- so the problem is really "how can you reduce those axes of relevancy to a 'common scenario?'"
Basically, as others said, it depends. Or, "low enough that you shouldn't worry about it until it shows up on a profiler report."
In native C/C++ there is sometimes a large cost of allocating memory due to finding a block of free memory that is of the right size, there is also a none 0 cost of freeing memory due to having to linked the freed memory into the correct list of blocks, and combine small blocks into large blocks.
In .NET it is very quick to allocate a new object, but you pay the cost when the garbage collector runs. However to cost of garbage collection short lived object is as close to free as you can get.
I have always found that if the cost of garbage collection is a problem to you, then you are likely to have over bigger problems with the design of your software. Paging can be a big issue with any GC if you don’t have enough physical RAM, so you may not be able to just put all your data in RAM and depend on the OS to provide virtual memory as needed.
It really can vary. Look at this demonstration short-but-complete program that I wrote:
http://nomorehacks.wordpress.com/2008/11/27/forcing-the-garbage-collector/
that shows the effect of large gen2 garbage collections.
Yes, the Garbage Collector will spend some X% of time collecting when averaged over all applications everywhere. But that doesn't necessarily means that time is overhead. For overhead, you can really only count the time that would be left after releasing an equivalent amount of memory on an unmanaged platform.
With that in mind, the actual overhead is negative, but the Garbage collector will save time by release several chunks of memory in batches. That means fewer context switches and an overall improvement in efficiency.
Additionally, starting with .Net 4 the garbage collector does a lot of it's work on a different thread that doesn't interrupt your currently running code as much. As we work more and more with mutli-core machines where a core might even be sitting idle now and then, this is a big deal.