Memory Allocation/Deallocation Bottleneck?

问题:

How much of a bottleneck is memory allocation/deallocation in typical real-world programs? Answers from any type of program where performance typically matters are welcome. Are decent implementations of malloc/free/garbage collection fast enough that it's only a bottleneck in a few corner cases, or would most performance-critical software benefit significantly from trying to keep the amount of memory allocations down or having a faster malloc/free/garbage collection implementation?

Note: I'm not talking about real-time stuff here. By performance-critical, I mean stuff where throughput matters, but latency doesn't necessarily.

Edit: Although I mention malloc, this question is not intended to be C/C++ specific.

回答1:

It's significant, especially as fragmentation grows and the allocator has to hunt harder across larger heaps for the contiguous regions you request. Most performance-sensitive applications typically write their own fixed-size block allocators (eg, they ask the OS for memory 16MB at a time and then parcel it out in fixed blocks of 4kb, 16kb, etc) to avoid this issue.

In games I've seen calls to malloc()/free() consume as much as 15% of the CPU (in poorly written products), or with carefully written and optimized block allocators, as little as 5%. Given that a game has to have a consistent throughput of sixty hertz, having it stall for 500ms while a garbage collector runs occasionally isn't practical.

回答2:

Nearly every high performance application now has to use threads to exploit parallel computation. This is where the real memory allocation speed killer comes in when writing C/C++ applications.

In a C or C++ application, malloc/new must take a lock on the global heap for every operation. Even without contention locks are far from free and should be avoided as much as possible.

Java and C# are better at this because threading was designed in from the start and the memory allocators work from per-thread pools. This can be done in C/C++ as well, but it isn't automatic.

回答3:

First off, since you said malloc, I assume you're talking about C or C++.

Memory allocation and deallocation tend to be a significant bottleneck for real-world programs. A lot goes on "under the hood" when you allocate or deallocate memory, and all of it is system-specific; memory may actually be moved or defragmented, pages may be reorganized--there's no platform-independent way way to know what the impact will be. Some systems (like a lot of game consoles) also don't do memory defragmentation, so on those systems, you'll start to get out-of-memory errors as memory becomes fragmented.

A typical workaround is to allocate as much memory up front as possible, and hang on to it until your program exits. You can either use that memory to store big monolithic sets of data, or use a memory pool implementation to dole it out in chunks. Many C/C++ standard library implementations do a certain amount of memory pooling themselves for just this reason.

No two ways about it, though--if you have a time-sensitive C/C++ program, doing a lot of memory allocation/deallocation will kill performance.

回答4:

In general the cost of memory allocation is probably dwarfed by lock contention, algorithmic complexity, or other performance issues in most applications. In general, I'd say this is probably not in the top-10 of performance issues I'd worry about.

Now, grabbing very large chunks of memory might be an issue. And grabbing but not properly getting rid of memory is something I'd worry about.

In Java and JVM-based languages, new'ing objects is now very, very, very fast.

Here's one decent article by a guy who knows his stuff with some references at the bottom to more related links: http://www.ibm.com/developerworks/java/library/j-jtp09275.html

回答5:

In Java (and potentially other languages with a decent GC implementation) allocating an object is very cheap. In the SUN JVM it only needs 10 CPU Cycles. A malloc in C/c++ is much more expensive, just because it has to do more work.

Still even allocation objects in Java is very cheap, doing so for a lot of users of a web application in parallel can still lead to performance problems, because more Garbage Collector runs will be triggered. Therefore there are those indirect costs of an allocation in Java caused by the deallocation done by the GC. These costs are difficult to quantify because they depend very much on your setup (how much memory do you have) and your application.

回答6:

A Java VM will claim and release memory from the operating system pretty much indepdently of what the application code is doing. This allows it to grab and release memory in large chunks, which is hugely more efficient than doing it in tiny individual operations, as you get with manual memory management.

This article was written in 2005, and JVM-style memory management was already streets ahead. The situation has only improved since then.

Which language boasts faster raw allocation performance, the Java language, or C/C++? The answer may surprise you -- allocation in modern JVMs is far faster than the best performing malloc implementations. The common code path for new Object() in HotSpot 1.4.2 and later is approximately 10 machine instructions (data provided by Sun; see Resources), whereas the best performing malloc implementations in C require on average between 60 and 100 instructions per call (Detlefs, et. al.; see Resources). And allocation performance is not a trivial component of overall performance -- benchmarks show that many real-world C and C++ programs, such as Perl and Ghostscript, spend 20 to 30 percent of their total execution time in malloc and free -- far more than the allocation and garbage collection overhead of a healthy Java application.

回答7:

Allocating and releasing memory in terms of performance are relatively costly operations. The calls in modern operating systems have to go all the way down to the kernel so that the operating system is able to deal with virtual memory, paging/mapping, execution protection etc.

On the other side, almost all modern programming languages hide these operations behind "allocators" which work with pre-allocated buffers.

This concept is also used by most applications which have a focus on throughput.

回答8:

I know I answered earlier, however, that was ananswer to the other answer's, not to your question.

To speak to you directly, if I understand correctly, your performance use case criteria is throughput.

This to me, means's that you should be looking almost exclusivly at NUMA aware allocators.

None of the earlier references; IBM JVM paper, Microquill C, SUN JVM. Cover this point so I am highly suspect of their application today, where, at least on the AMD ABI, NUMA is the pre-eminent memory-cpu governer.

Hands down; real world, fake world, whatever world... NUMA aware memory request/use technologies are faster. Unfortunately, I'm running Windows currently, and I have not found the "numastat" which is available in linux.

A friend of mine has written about this in depth in his implmentation for the FreeBSD kernel.

Dispite me being able to show at-hoc, the typically VERY large amount of local node memory requests on top of the remote node (underscoring the obvious performance throughput advantage), you can surly benchmark yourself, and that would likely be what you need todo as your performance charicterisitc is going to be highly specific.

I do know that in a lot of ways, at least earlier 5.x VMWARE faired rather poorly, at that time at least, for not taking advantage of NUMA, frequently demanding pages from the remote node. However, VM's are a very unique beast when it comes to memory compartmentailization or containerization.

One of the references I cited is to Microsoft's API implmentation for the AMD ABI, which has NUMA allocation specialized interfaces for user land application developers to exploit ;)

Here's a fairly recent analysis, visual and all, from some browser add-on developers who compare 4 different heap implmentations. Naturally the one they developed turns out on top (odd how the people who do the testing often exhibit the highest score's).

They do cover in some ways quantifiably, at least for their use case, what the exact trade off is between space/time, generally they had identified the LFH (oh ya and by the way LFH is simply a mode apparently of the standard heap) or similarly designed approach essentially consumes signifcantly more memory off the bat however over time, may wind up using less memory... the grafix are neat too...

I would think however that selecting a HEAP implmentation based on your typical workload after you well understand it ;) is a good idea, but to well understand your needs, first make sure your basic operations are correct before you optimize these odds and ends ;)

回答9:

This is where c/c++'s memory allocation system works the best. The default allocation strategy is OK for most cases but it can be changed to suit whatever is needed. In GC systems there's not a lot you can do to change allocation strategies. Of course, there is a price to pay, and that's the need to track allocations and free them correctly. C++ takes this further and the allocation strategy can be specified per class using the new operator:

class AClass
{
public:
  void *operator new (size_t size); // this will be called whenever there's a new AClass
   void *operator new [] (size_t size); // this will be called whenever there's a new AClass []
  void operator delete (void *memory); // if you define new, you really need to define delete as well
  void operator delete [] (void *memory);define delete as well
};

Many of the STL templates allow you to define custom allocators as well.

As with all things to do with optimisation, you must first determine, through run time analysis, if memory allocation really is the bottleneck before writing your own allocators.

回答10:

According to MicroQuill SmartHeap Technical Specification, "a typical application [...] spends 40% of its total execution time on managing memory". You can take this figure as an upper bound, i personally feel that a typical application spends more like 10-15% of execution time allocating/deallocating memory. It rarely is a bottleneck in single-threaded application.

In multithreaded C/C++ applications standard allocators become an issue due to lock contention. This is where you start to look for more scalable solutions. But keep in mind Amdahl's Law.

回答11:

Others have covered C/C++ so I'll just add a little information on .NET.

In .NET heap allocation is generally really fast, as it it just a matter of just grabbing the memory in the generation zero part of the heap. Obviously this cannot go on forever, which is where garbage collection comes in. Garbage collection may affect the performance of your application significantly since user threads must be suspended during compaction of memory. The fewer full collects, the better.

There are various things you can do to affect the workload of the garbage collector in .NET. Generally if you have a lot of memory reference the garbage collector will have to do more work. E.g. by implementing a graph using an adjacency matrix instead of references between nodes the garbage collector will have to analyze fewer references.

Whether that is actually significant in your application or not depends on several factors and you should profile the application with actual data before turning to such optimizations.

回答12:

Pretty much all of you are off base if you are talking about the Microsoft heap. Syncronization is effortlessly handled as is fragmentation.

The current perferrred heap is the LFH, (LOW FRAGMENTATION HEAP), it is default in vista+ OS's and can be configured on XP, via gflag, with out much trouble

It is easy to avoid any locking/blocking/contention/bus-bandwitth issues and the lot with the

HEAP_NO_SERIALIZE

option during HeapAlloc or HeapCreate. This will allow you to create/use a heap without entering into an interlocked wait.

I would reccomend creating several heaps, with HeapCreate, and defining a macro, perhaps, mallocx(enum my_heaps_set, size_t);

would be fine, of course, you need realloc, free also to be setup as appropiate. If you want to get fancy, make free/realloc auto-detect which heap handle on it's own by evaluating the address of the pointer, or even adding some logic to allow malloc to identify which heap to use based on it's thread id, and building a heierarchy of per-thread heaps and shared global heap's/pools.

The Heap* api's are called internally by malloc/new.

Here's a nice article on some dynamic memory management issues, with some even nicer references. To instrument and analyze heap activity.