As far as I know, custom memory managers are used in several medium and large-scale projects. This recent answer on security.se discusses the fact that a custom memory allocator in OpenSSL was included for performance reasons and ultimately ended up making the Heartbleed exploit worse. This old thread here discusses memory allocators, and in particular one of the answer links to an academic paper that shows that while people write custom memory allocators for performance reasons because malloc is slow, a general-purpose state-of-the-art allocator easily beats them and causes fewer problems than developers reinventing the wheel in every project.
As someone who does not program professionally, I am curious about how we ended up in this state and why we seem to be stuck there --- assuming my view is correct, which is not necessarily true. I imagine there must be subtler issues such as thread safety. Apologies if I am presenting the situation wrongly.
Why is the system malloc not developed and optimized to match the performance of these "general-purpose state-of-the-art allocators"? It seems to me that it should be quite an important feature for OS and standard library writers to focus on. I have heard a lot of talking about the scheduler implementation in Linux kernel in the past, for instance, and naively I would expect to see more or less the same amount of interest for memory allocators. How come standard malloc is so bad that so many people feel the need roll out a custom allocator? If there are alternative implementations that work so much better, why haven't system programmers included them in Linux and Windows, either as default or as a linking-time option?
There are two problems:
No single allocation scheme fits all application needs.
The C library was poorly designed (or not designed). Some non-eunuchs operating systems have configurable memory managers that can allow the application to choose the allocation scheme. In eunuchs-land, the solution is to link in your own malloc/free implementation into your application.
There is no real standard malloc implementation (GNU LIBC's is the probably the closest to standard). The malloc implementations that come with the OS tend to work fine for more applications.
I always assumed a heap (data structure) is used to implement a heap (dynamic memory allocation), but I've been told I'm wrong.
How are heaps (for example, the one implemented by typical malloc routines, or by Windows's HeapCreate, etc.) implemented, typically? What data structures do they use?
What I'm not asking:
While searching online, I've seen tons of descriptions of how to implement heaps with severe restrictions.
To name a few, I've seen lots of descriptions of how to implement:
Heaps that never release memory back to the OS (!)
Heaps that only give reasonable performance on small, similarly-sized blocks
Heaps that only give reasonable performance for large, contiguous blocks
etc.
And it's funny, they all avoid the harder question:
How are "normal", general-purpose heaps (like the one behind malloc, HeapCreate) implemented?
What data structures (and perhaps algorithms) do they use?
Allocators tend to be quite complex and often differ significantly in how they're implemented.
You can't really describe them in terms of one common data structure or algorithm, but there are some common themes:
Memory is taken from the system in large chunks -- often megabytes at a time.
These chunks are then split up into various smaller chunks as you perform allocations. Not exactly the same size as you allocate, but usually in certain ranges (200-250 bytes, 251-500 bytes, etc.). Sometimes this is multi-tiered, where you'd have an additional layer of "medium chunks" which come before your actual requests.
Controlling which "large chunk" to break a piece off of is a very difficult and important thing to do -- this greatly affects memory fragmentation.
One or more free pools (aka "free list", "memory pool", "lookaside list") are maintained for each of these ranges. Sometimes even thread-local pools. This can greatly speed up a pattern of allocating/deallocating many objects of similar size.
Large allocations are treated a bit differently so as to not waste a lot of RAM and not be pooled quite so much if at all.
If you wanted to check out some source code, jemalloc is a modern high-performance allocator and should be representative in complexity of other common ones. TCMalloc is another common general-purpose allocator, and their website goes into all the gory implementation details. Intel's Thread Building Blocks has an allocator built specifically for high concurrency.
One interesting difference can be seen between Windows and *nix. In *nix, the allocator has very low-level control over the address space an app uses. In Windows, you basically have a course-grained, slow allocator VirtualAlloc to base your own allocator off of.
This results in *nix-compatible allocators typically directly giving you an malloc/free implementation where it's assumed you'll only use one allocator for everything (otherwise they'd trample each-other), while Windows-specific allocators provide additional functions, leaving malloc/free alone, and can be used in harmony (for instance, you can use HeapCreate to make private heaps which can work alongside others).
In practice, this trade in flexibility gives *nix allocators a small leg up performance-wise. It's very rare to see an app intentionally use multiple heaps on Windows -- mostly it's by accident due to different DLLs using different runtimes which each have their own malloc/free, and can cause a lot of headaches if you're not diligent in tracking which heap some memory came from.
Note: The following answer assumes you're using a typical, modern system with virtual memory. The C and C++ standards do not require virtual memory; therefore of course you can't rely on such assumptions on hardware without this feature (e.g. GPUs typically don't have this feature; nor do extremely small hardware like the PIC).
This depends on the platform you're using. Heaps can be very complicated beasts; they don't use only a single data structure; and there is no "standard" data structure. Even where the heap code is located is different depending on the platform. For instance, the heap code is typically provided by the C Runtime on Unix boxes; but is typically provided by the operating system on Windows.
Yes, this is common on Unix machines; due to the way *nix's underlying APIs and memory model operate. Basically, the standard API to return memory to the operating system on these systems only allows returning memory on the "frontier" between where user memory is allocated and the "hole" in between user memory and system facilities like the stack. (The API in question is brk or sbrk). Instead of returning memory to the operating system, many heaps only try to reuse memory no longer in use by the program proper, and don't try to return memory to the system. This is less common on Windows, because its equivalent to sbrk (VirtualAlloc) doesn't have this limitation. (But like sbrk, it is very expensive and has caveats like only allocating page-sized and page-aligned chunks. So heaps try to call either as rarely as possible)
This sounds like a "block allocator", which divides the memory into fixed size chunks, and then just return one of the free chunks. To my (albeit limited) understanding, Windows' RtlHeap maintains a number of data structures like this for different known block sizes. (E.g. it'll have one for blocks of size 16, for instance) RtlHeap calls these "lookaside lists".
I don't really know of a specific structure that handles this case well. Large blocks are problematic for most allocation systems because they cause fragmentation of the address space.
The best reference I've found discussing the common allocation strategies employed on major platforms is the book Secure Coding in C and C++, by Robert Seacord. All of chapter 4 is dedicated to heap data structures (and problems caused when users use said heap systems incorrectly).
I read the OpenCL overview, and it states it is suitable for code that runs of CPUs, GPGPUs, DSPs, etc. However, from looking through the command reference, it seems to be all math and image type operations. I didn't see anything for say strings.
This makes me wonder what would you run on a CPU via OpenCL?
Further, I know OpenCL can be used to perform sorting on GPGPUs. But would one ever use it (or, for that matter, a current GPGPU) to perform string processing such as pattern matching, metaphone extraction, dictionary lookup, or anything else that requires the processing of arrays of strings.
EDIT
I noticed that Intel's upcoming Ivy Bridge is touted as "OpenCL compliant" with reference to its graphics units. Does this infer that the CPU cores are not OpenCL compliant, or is there no such inference?
EDIT
In the interests of non-debate and constructiveness, I would appreciate if anyone could point me to official references that would answer my question.
You can think of OpenCL as a combination of a runtime (for device discovery, queueing) and a C-based programming language. This programming language has native vector types and built-in functions and operations for doing all sorts fun stuff to these vectors. This is nice in that you can write a vectorized kernel in OpenCL, and it it the responsibility of the implementation to map that to the actual vector ISA of your hardware.
From this 4/2011 article, which might vanish:
There are two major CPU architectures out there, x86 and ARM, both of
which should soon run OpenCL code.
If you write an OpenCL application that targets both of these architectures, you wouldn't have to worry about writing two versions, one SSE and one NEON. Just write OpenCL C and be done with it. Yes, I know. This assumes the vendor has done his job and written a solid implementation that fully utilizes the underlying ISA. But if he doesn't, complain!
In addition, some CL implementations offer auto-vectorization of scalar kernels, which are usually easier to write. A good auto-vectorizer would give you a solid performance increase for no effort. Since CL kernels are compiled "online," obtaining such a benefit wouldn't require shipping rebuilt code.
No links, but I would assume this is because algorithms that use strings may do a lot of dynamic memory allocation and branching, both of which GPGPUs are not well-suited for. GPGPUs also have a lot in common with vector processing, so doing units of work with different sized blocks of memory (which a string algorithm will generally work on, you usually don't have a homogeneous group of strings), yields poorer performance and is hard to program.
GPUs were designed to do the same work, with little to no branching, on a homogeneous group of data (such as per-vector or per-pixel operations). Algorithms that can mimic this type of behavior are great on GPUs.
This makes me wonder what would you run on a CPU via OpenCL?
I prefer to use ocl to offload work from the cpu to my graphics hardware. Sometimes there is a limitation with my video card, so I like having a backup kernel for cpu use. Such limitations can be memory size, memory bottleneck, low clock speed, or when the pci-e bus gets in the way.
I say I like using a separate kernel for cpu, because I think all kernels should be tweaked to run on their target hardware. I even like to have an openmp backup plan, as most algorithms I use get tested out in this manner ahead of time.
I suppose it is best practice to test out a gpu kernel on the cpu to make sure it runs as expected. If a user of your software has opencl installed, but only a cpu (or a low-end gpu) it's nice to be able to execute the same code on the different devices.
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.
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.
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.
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.
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
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.
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.
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.
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 ;)
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.
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.
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.
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.