Either by profiling or measuring throughput. It's impossible to say anything for certain. How are we doing? Please help us improve Stack Overflow. Take our short survey. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. How bad it is to keep calling malloc and free?
Ask Question. Asked 10 years, 1 month ago. Active 3 years, 5 months ago. Viewed 16k times. Improve this question. A line of code is worth a thousand comments. I added the code to the main post — cap10ibrahim. Add a comment. Active Oldest Votes. Improve this answer. Are you using a fragmented heap and including mallocs that need to bring in new memory from the OS and frees that release it? This will almost always be the common case. The only time you should worry about time to get new memory from the OS is in realtime programming where you care about the worst-case latency of any operation rather than the overall runtime of your program.
Interesting stuff. Show 4 more comments. If he is allocating and freeing a fixed-size block over and over, he is almost certainly not fragmenting anything. That does not mean there is no overhead But fragmentation is probably not what causes it in this case. The storage space pointed to by the return value is guaranteed to be suitably aligned for storage of any type of object that has an alignment requirement less than or equal to that of the fundamental alignment.
If size is 0, malloc allocates a zero-length item in the heap and returns a valid pointer to that item. Always check the return from malloc , even if the amount of memory requested is small.
The malloc function allocates a memory block of at least size bytes. The block may be larger than size bytes because of the space that's required for alignment and maintenance information.
The following functions and their wide-character counterparts also call malloc. By default, malloc does not call the new handler routine on failure to allocate memory. You can override this default behavior so that, when malloc fails to allocate memory, malloc calls the new handler routine in the same way that the new operator does when it fails for the same reason. OBJ see Link Options. As we already said, a custom allocator dedicated to a data structure will allow you to decrease memory fragmentation and increase data locality.
But it also comes with additional benefits. A custom allocator can be adjusted to a specific environment for maximum performance. Here are a few examples:. Knowing your domain will help you pick the right strategy for your custom allocator. Imagine a scenario where a producer thread allocates an object and sends it to the consumer thread. After processing the object, the consumer thread destroys the object and releases the memory back to the operating system.
This kind of scenario puts a large pressure on the system allocator and increases memory fragmentation. One of the ways you could fix this problem is to allocate all the objects from a dedicated memory pool using a custom allocator.
You could overwrite operator new and operator delete methods to use the new allocator. However, you will need to introduce some kind of synchronization, since the memory for the objects is created and destroyed in two separate threads.
One solution to the synchronization problem is to cache memory chunks both on the allocate end and deallocate end.
This decreases the need for synchronization. Take a look at the following source code:. This solution works only with two threads, one thread that exclusively allocates and the other thread exclusively deallocates objects. Small values make the allocator useless, as most of the time it is working with the system allocator instead of the cached chunk lists.
A large value makes the program consume more memory. On line 4 we allocate memory for the object, but only on line 5 we call the constructor. The piece of the memory on which the object is created is given in parenthesis, after the keyword new. On line 8 we explicitly call the constructor. The object is destroyed, but the memory is not released back. We release the memory back to the pool on line 9. Alternatively, you can override operator new and operator delete , like this:.
This will make every object created using new and destroyed using delete allocated using memory from the memory pool. Here is the source code of a naive implementation:. What we could do, is that we could preallocate two integers as a part of the class and thus completely avoid calls to the system allocator. The code now looks like this:. The downside of this approach is the increase in class size. On 64 bit system, the original class was 24 bytes in size, the new class is 32 bytes in size.
Luckily, we can have both small size and small buffer optimizations in the same package with a trick. We can use C unions to overlay the data for the preallocated case and for the heap-allocated case. Here is the source code:.
This approach is used in several places. The techniques mentioned up to this point are domain-specific. In this section we talk about another way to speed up your program: by using a better system allocator. On Linux, there are several open-source allocators that try to solve the problem of efficient allocation and deallocation, but no allocator, as far as I know, can solve all the problems completely.
When you are picking a system allocator for your system, there are four properties that each allocator compromises on:. Its allocator is called GNU allocator and it is based on ptmalloc. Apart from it, there are several other open-source allocators commonly used on Linux : tcmalloc by Google , jemalloc by Facebook , mimalloc by Microsoft , hoard allocator , ptmalloc and dlmalloc. GNU allocator is not among the most efficient allocators. However it does have one advantage, the worst-case runtime and memory usage will not be too bad.
But the worst case happens rarely and it is definitely worth investigating if we can do better. Other allocators claim to be better in speed, memory usage or cache locality.
Still, when you are picking the right one for you, you should consider:. Well, this time there will not be any experiments.
The reason is simple: real-world programs differ too much from one another. An allocator that performs well under one load might have different behavior under another load. The author has compared several allocators on a test load that tries to simulate a real-world load. We will not repeat the results of his analysis here, but his conclusion matches ours: allocators are similar to one another and testing your particular application is more important than relying on synthetic benchmarks.
You can use this trick to quickly check if the allocator fits your needs:. All the allocators can be fine-tuned to run better on a particular system , but the default configuration should be enough for most use cases. Fine-tuning can be done at compile-time or run-time, through environment variables, configuration files or compilation options. Normally the allocators provide implementations for malloc and free and will replace the functions with the same name provided by Standard C library.
This means that every dynamic allocation your program makes goes through the new allocator. However, it is possible to keep default malloc and free implementations together with custom implementations provided by your chosen allocator.
Allocators can provide prefixed versions of malloc and free for this purpose. For example, jemalloc allows you to provide a custom prefix for malloc and free. We presented several techniques on how to make your program allocate memory faster.
Off-the-shelf allocators have the benefit of being very easy to set up and you can see the improvements within minutes. However, you will need to introduce new libraries to your program which can sometimes pose a problem. Other techniques presented are also very powerful when used correctly. Just decreasing the number of allocations by avoiding pointers removes a lot of stress from the system allocator as seen with vectors of pointers.
Custom allocators will help you get a better allocation speed and help you decrease memory fragmentation. Other techniques also have their place in making your program run faster, depending on the problem at hand.
Each of these techniques solves the question of allocation a bit differently and at the end of the day, it is your program and your requirements that will help you pick the best one.
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