But it can be very useful in certain contexts embedded devices, games, etc. The custom allocator can free all temporary data at once once the response has been generated. Another possible use case for a custom allocator which I have used is writing a unit test to prove that that a function's behavior doesn't depend on some part of its input. The custom allocator can fill up the memory region with any pattern.
I'm using custom allocators here; you might even say it was to work around other custom dynamic memory management. This lets us do things like automatic small object pooling, leak detection, alloc fill, free fill, padding allocation with sentries, cache-line alignment for certain allocs, and delayed free.
The problem is, we're running in an embedded environment -- there isn't enough memory around to actually do leak detection accounting properly over an extended period. At least, not in the standard RAM -- there's another heap of RAM available elsewhere, through custom allocation functions. This avoids the tracker tracking itself and provides a bit of extra packing functionality too, we know the size of tracker nodes.
We also use this to keep function cost profiling data, for the same reason; writing an entry for each function call and return, as well as thread switches, can get expensive fast. Custom allocator again gives us smaller allocs in a larger debug memory area. When working with GPUs or other co-processors it is sometimes beneficial to allocate data structures in main memory in a special way. This special way of allocating memory can implemented in a custom allocator in a convenient fashion.
The reason why custom allocation through the accelerator runtime can be beneficial when using accelerators is the following:. There are other ways this could be achieved but this method is very convenient for me. It is especially useful that I can use the custom allocator for only a subset of my containers. Where I ran into this was a Plugin architecture on Windows. It is essential that, for example, if you pass a std:: But if each DLL has a static link to the CRT you are heading to a world of pain, where phantom allocation errors continually occur.
One example of I time I have used these was working with very resource constrained embedded systems. Lets say you have 2k of ram free and your program has to use some of that memory.
You need to store say sequences somewhere that's not on the stack and additionally you need to have very precise access over where these things get stored, this is a situation where you might want to write your own allocator. The default implementations can fragment the memory, this might be unacceptable if you don't have enough memory and cannot restart your program.
We had to store 8 sequences of variable length but with a known maximum. When allocating a new piece of memory the standard allocator has to walk over each of the pieces of memory to find the next block that is available where the requested size of memory will fit. On a desktop platform this would be very fast for this few items but you have to keep in mind that some of these microcontrollers are very slow and primitive in comparison. Additionally the memory fragmentation issue was a massive problem that meant we really had no choice but to take a different approach.
So what we did was to implement our own memory pool. Each block of memory was big enough to fit the largest sequence we would need in it. This allocated fixed sized blocks of memory ahead of time and marked which blocks of memory were currently in use. We did this by keeping one 8 bit integer where each bit represented if a certain block was used.
We traded off memory usage here for attempting to make the whole process faster, which in our case was justified as we were pushing this microcontroller chip close to it's maximum processing capacity. There's a number of other times I can see writing your own custom allocator in the context of embedded systems, for example if the memory for the sequence isn't in main ram as might frequently be the case on these platforms.
For shared memory it is vital that not only the container head, but also the data it contains are stored in shared memory. The allocator of Boost:: Interprocess is a good example. However, as you can read here this allone does not suffice, to make all STL containers shared memory compatible Due to different mapping offsets in different processes, pointers might "break". Sometime ago I found this solution very useful to me: It is a special purpose allocator based on memory pool.
Thanks for that second link. The use of allocators to implement thread-private heaps is clever. I like that this is a good example of where custom allocators have a clear advantage in a scenario that is not resource-limited embed or console.
I have to ask: And yes, as you already might have guessed, the whole memory management is done through the so-called allocator. If you think about how you usually allocate memory dynamically using the 'new' operator , you could ask why the STL provides such a thing called allocator that does all the memory management of the container classes.
The concept of allocators was originally introduced to provide an abstraction for different memory models to handle the problem of having different pointer types on certain bit operating systems such as near, far, and so forth.
However, this approach failed. Nowadays, allocators serve as an abstraction to translate the need to use memory into a raw call for memory. Thus, allocators simply separate the implementation of containers, which need to allocate memory dynamically, from the details of the underlying physical memory management. Thus, you can simply apply different memory models such as shared memory, garbage collections, and so forth to your containers without any hassle because allocators provide a common interface.
To completely understand why allocators are an abstraction, you have to think about how they are integrated into the container classes. If you take a look at the constructor of 'std:: The internal implementation of the allocator is completely irrelevant to the vector itself. It is simply relying on the standardized public interface every allocator has to provide.
The vector does not need to care any longer whether it would need to call 'malloc' , 'new' , and so on to allocate some memory; it simply calls a standardized function of the allocator object named 'allocate ' that will simply return a pointer to the newly allocated memory. Whether this function internally uses 'malloc' , 'new' , or something else, is not of any interest to the vector.
After reading the background and purpose of allocators, you might wonder whether you need to provide your own allocator every time you want to use a container from the STL.
You can breathe a sigh of relief The standard provides an allocator that internally uses the global operators 'new' and 'delete'. All constructors and the destructor are trivial for the standard allocator as in, empty.
For user-defined allocators, they might be non-trivial. However, they are not allowed to throw any exception at all. In case you are wondering why we count the free template operators to the public interface of the 'allocator': We use the term public interface in a wider sense—we are not referring strictly to the class members declared as 'public: The 'allocator' class won't be complete without the two free template operators because the operators would not have any meaning without the allocator class.
They belong together and represent the public interface. The allocator needs to be specialized for 'void' because you cannot have references to 'void'. The default allocator that comes with your implementation of the STL will do a very good job in nearly all cases.
That is not surprising because the implementations of the STL are written by very experienced people. In other words, the assumption of being able to write an allocator that outperforms the standard one in the general case is at least questionable. So, why implement an allocator on your own in the first place? There are a couple of reasons justifying that:. As you can see, the reasons to write your own allocator are very dependent on your application.
In this article, we will ignore the fact that we probably cannot out-perform the default allocator, and write a couple of all-purpose custom allocators. Believe it or not, the devil lies in the details If you need or want to write a user-defined allocator, you have to follow some basic requirements:.
Unsigned integral value that can represent the difference between two pointers in the memory model. A template structure, which allows any allocator might allocate memory of another type indirectly. Copies an allocator, so that storage allocated by the original can be released by the copy and vice-versa. Deallocates the storage for n elements of the element type being used in the memory model,beginning at location p. Returns true if storage allocated by allocator a1 can be deallocated by allocator a2 and vice-versa.
If the allocator should be used with STL containers, the above is required. Thus, this function should always return true. Returns true if storage allocated by allocator a1 cannot be deallocated by allocator a2 and vice-versa.
If the allocator should be used with STL containers, the opposite of the above is required. Thus, this function should always return false. On a more practical level, there are two steps when implementing a custom allocator actually there are three steps, but we will come to that later.
Design a memory management mechanism. For example, grab a huge chunk of memory and manage that by yourself. Although it is possible to have both the storage management and the allocator interface in one and the same class, we prefer to separate them.
That is, we prefer to implement a class that does the memory management, and to implement the allocator in terms of this class, by composition and NOT inheritance!
If you did an Internet search, you would find dozens of high-tuned or special purpose memory management implementations. Just have a look at the Boost library. This is a vast topic that is beyond the scope of this article. What we want to demonstrate is the link between memory management schemes and standard-like allocators.
So let's start simple The concept of our first memory management class is that it grabs a large chunk of raw memory on construction, and emulates allocations and deallocations on this chunk.
We will call it a memory pool. We will keep track of the allocated and free blocks of memory by using a linked list of nodes, that are directly embedded into the memory chunk. For those who are curious, we saw this kind of memory management in the source code of Doom by ID Software—the source code used to be available under the GPL.
We have no idea who invented it, we suspect that it was many, many years ago though. And this is deadly. There are rumors that the next revision of the standard will address this problem, but until then However, despite growing the chunk would not be impossible anymore, it would for sure be a mess—complicated, error prone, and so on.
What about this approach:. It is up to you to find a better one. Now that we have decided how to handle the raw storage, we also need to organize it. The client is likely to ask for a block of memory, or to release a block of memory it previously allocated. We will keep track of this blocks by prepending a structure at the beginning of each block:.
Click here for a larger image. The entire chunk will be described by one single block, marked as free. When the client allocates some memory, a new block is created:.
This allocation process can continue until the chunk is exhausted. Of course, allocations and deallocations happen randomly, in general. When deallocating a block, we check the previous and the next blocks, to see whether they are free.
In this case, we glue them together into one larger block. This avoids the fragmentation of the chunks. When an allocation request is received, we will travel along the blocks until we find one that is large enough and free. Finally, when allocating from a block, we check the remaining part assuming that the block is larger than the amount of memory to allocate. If the remaining part is smaller than a threshold value, we do not split the block, but mark it allocated entirely.
This ensures that we won't have useless small blocks. We could add a check for completely free chunks, and delete them, but that would have a performance impact. We will leave that to you as an exercise read: We are too lazy to implement that too It cannot allocate contiguous memory blocks larger than 'size - sizeof block '. This could be addressed by making 'size' large enough we do not like this large enough solution —it is a poor man's approach , or, we could pass the 'size' as a template or constructor parameter.
Allocating and deallocating a large number of small blocks is slow. There is nothing to do here—just do not use a pool allocator in such a case. With the design in place, implementing the 'pool' class is not that hard.
You just have to keep in mind what you want to use it for, that is, an allocator. Thus, a couple of implementation decisions: We plan to use pool by composition—there will be no virtual functions. The destructor also is not virtual, signaling that 'pool' is not meant to be inherited from. We have implemented 'pool' directly in the header pool. It makes no sense to show the entire implementation here—all we need to know for now, is, that it defines the public interface:.
The second argument of 'pool:: We will just pass the second argument of the allocators 'deallocate ' , but won't use it because 'pool' does not need this information. It is more of an aesthetic issue. We will come back to this later. Let's have a look at the second memory management scheme first The concept of a simple segregated storage also is not new.
The idea behind this memory management technique is to grab a large chunk of memory and to partition it into equally large blocks of a known size—that is, the size of the type you want to allocate. As opposed to the memory pool, we will use a simple linked list. That is, each block contains a pointer that points to the next free block.
Another difference is that, when allocating a block, the pointer will be trashed. Well, as Pulitzer said, an image is worth a thousand words:. The image above shows the initial state of the storage. After allocating four blocks, it will look like this:. Now, assume that the second and third blocks are deallocated in this order: Of course, we want to be able to grow the storage, so we will do the same thing we did with 'pool' —we will use a 'std:: This corresponds more or less to Bjarne Stroustrup's example.
There is one more point, though: We should also be able to allocate blocks of arbitrary size. Stroustrup leaves that as an exercise to the reader. When allocating a block of a size different from the segregation size, we first check whether it is by chance smaller than it.
In this case, we allocate a block as we usually do, and If the size to allocate is larger than the segregation size, we compute the number of blocks needed.
I haven't written C++ code with a custom STL allocator, but I can imagine a webserver written in C++, which uses a custom allocator for automatic deletion of temporary data needed for responding to a HTTP request. There's a number of other times I can see writing your own custom allocator in the context of embedded systems, for example .
Writing a custom allocator Ask Question. C++ found an example and adpated it When I made it compilable and writing it business plan writing services ireland std:: So I would appreciate some allocator where I'm going wrong.
Writing a custom allocator Ask Question. I found an example and adpated custom When I made it compilable and tested it the std:: So I would appreciate some pointers c++ I'm going wrong. Using Visual Studio for now, so maybe not everything. You'd like to improve the performance of your application with regard to memory management, and you believe this can be accomplished by writing a custom allocator. But where do you start? Modern C++ brings many improvements to the standard allocator model, but with those improvements come several.
I'm trying to write a custom allocator, which allocator space for a fixed number of elements. However, Writing have writing problems with understanding the requirements. I custom an example and adpated it. Aug 28, · How to write custom allocator for vector of pointers? Unspoken. Hi guys. I am having this problem that I often use a vector of pointers to some objects. The problem is that I need to delete pointers in that vector manually which is prone to errors / memory leaks. Currently I am doing it like this.