I have a relatively simple question. In one of my CS classes, for an assignment, we have to make a simple side-scrolling game using C++ and the XLib libraries. In a forum we have for the class, a lot of students complained about memory leaks and issues with dynamic allocation. I am almost done the assignment, but I haven't had to use any dynamic allocation. I've just been using stack space, and have had no issues with this so far.
I am just wondering if there is any drawbacks to this? Seems like a lot of the other students are using dynamic allocation. If my little game works fine just using the stack, do I have any reason to worry?
Thanks guys.
There is nothing wrong per-se about using memory on the stack, though you need to be careful about allocating anything of decent size.
For instance:
// inside some function ...
int blah[1000];
// ...code using blah
might work fine, or you could run out of space and fail silently.
I would suggest using new / malloc() if you need a big chunk of memory, but it's fine to use small arrays on the stack.
If you're so worried about dynamic memory allocation and memory leaks, why not try use a Smart Pointer:
Here http://en.wikipedia.org/wiki/Smart_pointer#C.2B.2B_smart_pointers
Related
I have written a rather complex torch application and it works quite well, that is if it doesn't run out of memory. Now I have tried to see what sort of inputs or situations cause it too seemingly randomly run out of memory but so far I have had little to no success. So now I'm looking for a way to check which variables take how much (v)ram.
I can with a simple statement switch between running my code on caffe:cuda or caffe:cl which changes whatever or not my program runs in RAM or on the GPU, I imagine that such a switch will make validating my memory usage a lot easier.
I have already tried to use print(collectgarbage("count")*1024) to check how much memory is in usage at a given point in time however this does not clearly show me where the memory is being used, perhaps because the program is relatively complex (although there are a few variables which I suspect are hugging a lot of memory, neural networks, large matrices and such).
I already know that once I have identified who is hogging my memory I can assign a nill value to it and call the garbage collector too free it.
So in short is there a program or a tool that allows me to run a torch program and then list each variable and it's memory usage?
I don't know if you tried google :)
But here you are:
Torch7-profiling
Neural Model profiler script
"How to Profile a Lua Script using Pepperfish"
Easy Lua Profiling
tbo, I've never had memory issues with Torch7 so it might be your implementation which is just not optimal. It might be a loop without collectgarbage call somewhere where it should be, e.g. in a training loop or between the epochs.
can I use more than one stack in microprocessor?
and if I can,How can I progamming those?
Sure you can. Several CPU architectures have multiple stack pointers - even lowly 8-bit processors, such as the M6809. And even if the concept is not implemented in the CPU hardware, you can easily create multiple stacks in software. A stack pointer is basically simply an index register, so you could (for example) use the IX and IY registers of the Z80 to implement multiple stacks.
If your microprocesser has more than one hardware stack, then yes, you can. You would have to write assembler though, since no c/c++ implementation makes use of multiple stacks.
It would be easier to help if you could say exactly what architecture you're talking about.
As for the how of it. Generally there is a special register or memory location that is used to point to the stack. Using another stack is just as simple as setting this value. This is all processor and architecture dependent so it depends on the one you are using.
On some platforms, the stack used for return addresses is entirely separate from the one used for parameter passing. Indeed, on some platforms, C compilers don't permit recursion and don't use any stack for parameter passing. Frankly, I like such designs, since they minimize the likelihood of stack problems causing errant program behavior.
I'm looking at refactoring a lot of large (1000+ lines) methods into nice chunks that can then be unit tested as appropriate.
This started me thinking about the call stack, as many of my rafactored blocks have other refactored blocks within them, and my large methods may well have been called by other large methods.
I'd like to open this for discussion to see if refactoring can lead to call stack issues. I doubt it will in most cases, but wondered about refactored recursive methods and whether it would be possible to cause a stack overflow without creating an infinite loop?
Excluding recursion, I wouldn't worry about call stack issues until they appear (which they likely won't).
Regarding recursion: it must be carefully implemented and carefully tested no matter how it's done so this would be no different.
I guess it's technically possible. But not something that I would worry about unless it actually happens when I test my code.
When I was a kid, and computers had 64K of RAM, the call stack size mattered.
Nowadays, it's hardly worth discussing. Memory is huge, stack frames are small, a few extra function calls are hardly measurable.
As an example, Python has an artificially small call stack so it detects infinite recursion promptly. The default size is 1000 frames, but this is adjustable with a simple API call.
The only way to run afoul of the stack in Python is to tackle Project Euler problems without thinking. Even then, you typically run out of time before you run out of stack. (100 trillion loops would take far longer than a human lifespan.)
I think it's highly unlikely for you to get a stackoverflow without recursion when refactoring. The only way that I can see that this would happen is if you are allocating and/or passing a lot of data between methods on the stack itself.
Most of the literature on Virtual Memory point out that the as a Application developer,understanding Virtual Memory can help me in harnessing its powerful capabilities. I have been involved in developing applications on Linux for sometime but but didn't care about Virtual Memory intricacies while I code. Am I missing something? If so, please shed some light on how I can leverage the workings of Virtual Memory. Else let me know if am I not making sense with the question!
Well, the concept is pretty simple actually. I won't repeat it here, but you should pick up any book on OS design and it will be explained there. I recommend the "Operating System Concepts" from Silberscahtz and Galvin - it's what I had to use in the University and it's good.
A couple of things that I can think of what Virtual Memory knowledge might give you are:
Learning to allocate memory on page boundaries to avoid waste (applies only to virtual memory, not the usual heap/stack memory);
Lock some pages in RAM so they don't get swapped to HDD;
Guardian pages;
Reserving some address range and committing actual memory later;
Perhaps using the NX (non-executable) bit to increase security, but im not sure on this one.
PAE for accessing >4GB on a 32-bit system.
Still, all of these things would have uses only in quite specific scenarios. Indeed, 99% of applications need not concern themselves about this.
Added: That said, it's definately good to know all these things, so that you can identify such scenarios when they arise. Just beware - with power comes responsibility.
It's a bit of a vague question.
The way you can use virtual memory, is chiefly through the use of memory-mapped files. See the mmap() man page for more details.
Although, you are probably using it implicitly anyway, as any dynamic library is implemented as a mapped file, and many database libraries use them too.
The interface to use mapped files from higher level languages is often quite inconvenient, which makes them less useful.
The chief benefits of using mapped files are:
No system call overhead when accessing parts of the file (this actually might be a disadvantage, as a page fault probably has as much overhead anyway, if it happens)
No need to copy data from OS buffers to application buffers - this can improve performance
Ability to share memory between processes.
Some drawbacks are:
32-bit machines can run out of address space easily
Tricky to handle file extending correctly
No easy way to see how many / which pages are currently resident (there may be some ways however)
Not good for real-time applications, as a page fault may cause an IO request, which blocks the thread (the file can be locked in memory however, but only if there is enough).
May be 9 out of 10 cases you need not worry about virtual memory management. That's the job of the kernel. May be in some highly specialized applications do you need to tweak around them.
I know of one article that talks about computer memory management with an emphasis on Linux [ http://lwn.net/Articles/250967 ]. Hope this helps.
For most applications today, the programmer can remain unaware of the workings of computer memory without any harm. But sometimes -- for example the case when you want to improve the footprint of your program -- you do end up having to manipulate memory yourself. In such situations, knowing how memory is designed to work is essential.
In other words, although you can indeed survive without it, learning about virtual memory will only make you a better programmer.
And I would think the Wikipedia article can be a good start.
If you are concerned with performance -- understanding memory hierarchy is important.
For small data sets which are fully contained in physical memory you need to be concerned with caching (accessing memory from the cache is much faster).
When dealing with large data sets -- which may be paged out due to lack of physical memory you need to be careful to keep your access patterns localized.
For example if you declare a matrix in C (int a[rows][cols]), it is allocated by rows. Thus when scanning the matrix, you need to scan by rows rather than by columns. Otherwise you will be paging the same data in and out many times.
Another issue is the difference between dirty and clean data held in memory. Clean data is information loaded from file that was not modified by the program. The OS may page out clean data (perhaps depending on how it was loaded) without writing it to disk. Dirty pages must first be written to the swap file.
What is "tagged memory" and how does it help in reducing program size?
You may be referring to a tagged union, or more specifically a hardware implementation like the tagged architecture used in LISP machines. Basically a method for storing data with type information.
In a LISP machine, this was done in-memory by using a longer word length and using some of the extra bits to store type information. Handling and checking of tags was done implicitly in hardware.
For a type-safe C++ implementation, see boost:variant.
Not sure, but it is possible that you are referring to garbage collection, which is the process of automatically disposing of no longer used objects created when running a program.
"Tagged memory" can be a synonym for mark-and-sweep, which is the most basic way to implement garbage collection.
If this is all wrong, please edit your question to clarify.
The Windows DDK makes use of "pool tags" when allocating memory out of the kernel page pool. It costs 4 bytes of memory per allocation, but allows you to label (i.e. tag) portions of kernel memory which might help with debugging and detecting memory leaks.
BTW I don't see how anything called "tagged memory" could reduce program code size. It sounds like extra work, which translates to "more code" and "bigger program." Maybe it's meant to reduce the memory footprint somehow?
Here's a more technical description going into the implementation details as to how this is used for garbage collection. You may also want to check out the wikipedia article about Tagged Pointers.