I have a CSV file which contains 250 lines and each line contains 12 items separated by commas. I am going to store this in a 2D array of which the dimension is [250][12].
My question is : " Is it a bad programming practice to use such a huge array ?
I am going to pass this array to a method which takes a 2D array as the argument. It comes with openCV.
will there be a memory overflow ? "
Well, if you don't have to use it, it would be better. For example, read the file line by line and enter each line into the csv parser. That way each line is dealt with, and you rely on the (hopefully professional and optimized) memory management.
However, if it works it works. If you don't need this in a production environment, I don't see why you should have to change it, other than good practice.
First, you have to be clear about how you'll break a line of text into 12 fields typed as expected by openCV. You may want that to be the central area of the design.
No problem using a static array if the size 250x12 will never change and memory consumption is suitable for the hardware your program is supposed to run on. You face a trade-off between memory usage and complexity of code: if memory is a concern or if you have flexibility in mind then you should process line by line or even token by token, provided openCV implements those modes.
If you know the size of the array is going to be limited to 250*12 then that is not a huge array, assuming you are using a reasonable machine. Even with long double type elements your array is going to take 36 MB of space. If, however, your underlying elements are objects with sub-elements then you may want to re-think your approach e.g., processing the array row-by-row or element-by-element instead of reading it into the memory all at once.
As for passing the array to the function, you will not pass the array by value, you will pass a pointer to the array so it should not be a big overhead.
Related
There are a lot of questions online about allocating, copying, indexing, etc 2d and 3d arrays on CUDA. I'm getting a lot of conflicting answers so I'm attempting to compile past questions to see if I can ask the right ones.
First link: https://devtalk.nvidia.com/default/topic/392370/how-to-cudamalloc-two-dimensional-array-/
Problem: Allocating a 2d array of pointers
User solution: use mallocPitch
"Correct" inefficient solution: Use malloc and memcpy in a for loop for each row (Absurd overhead)
"More correct" solution: Squash it into a 1d array "professional opinion," one comment saying no one with an eye on performance uses 2d pointer structures on the gpu
Second link: https://devtalk.nvidia.com/default/topic/413905/passing-a-multidimensional-array-to-kernel-how-to-allocate-space-in-host-and-pass-to-device-/
Problem: Allocating space on host and passing it to device
Sub link: https://devtalk.nvidia.com/default/topic/398305/cuda-programming-and-performance/dynamically-allocate-array-of-structs/
Sub link solution: Coding pointer based structures on the GPU is a bad experience and highly inefficient, squash it into a 1d array.
Third link: Allocate 2D Array on Device Memory in CUDA
Problem: Allocating and transferring 2d arrays
User solution: use mallocPitch
Other solution: flatten it
Fourth link: How to use 2D Arrays in CUDA?
Problem: Allocate and traverse 2d arrays
Submitted solution: Does not show allocation
Other solution: squash it
There are a lot of other sources mostly saying the same thing but in multiple instances I see warnings about pointer structures on the GPU.
Many people claim the proper way to allocate an array of pointers is with a call to malloc and memcpy for each row yet the functions mallocPitch and memcpy2D exist. Are these functions somehow less efficient? Why wouldn't this be the default answer?
The other 'correct' answer for 2d arrays is to squash them into one array. Should I just get used to this as a fact of life? I'm very persnickety about my code and it feels inelegant to me.
Another solution I was considering was to max a matrix class that uses a 1d pointer array but I can't find a way to implement the double bracket operator.
Also according to this link: Copy an object to device?
and the sub link answer: cudaMemcpy segmentation fault
This gets a little iffy.
The classes I want to use CUDA with all have 2/3d arrays and wouldn't there be a lot of overhead in converting those to 1d arrays for CUDA?
I know I've asked a lot but in summary should I get used to squashed arrays as a fact of life or can I use the 2d allocate and copy functions without getting bad overhead like in the solution where alloc and cpy are called in a for loop?
Since your question compiles a list of other questions, I'll answer by compiling a list of other answers.
cudaMallocPitch/cudaMemcpy2D:
First, the cuda runtime API functions like cudaMallocPitch and cudaMemcpy2D do not actually involve either double-pointer allocations or 2D (doubly-subscripted) arrays. This is easy to confirm simply by looking at the documentation, and noting the types of parameters in the function prototypes. The src and dst parameters are single-pointer parameters. They could not be doubly-subscripted, or doubly dereferenced. For additional example usage, here is one of many questions on this. here is a fully worked example usage. Another example covering various concepts associated with cudaMallocPitch/cudaMemcpy2d usage is here. Instead the correct way to think about these is that they work with pitched allocations. Also, you cannot use cudaMemcpy2D to transfer data when the underlying allocation has been created using a set of malloc (or new, or similar) operations in a loop. That sort of host data allocation construction is particularly ill-suited to working with the data on the device.
general, dynamically allocated 2D case:
If you wish to learn how to use a dynamically allocated 2D array in a CUDA kernel (meaning you can use doubly-subscripted access, e.g. data[x][y]), then the cuda tag info page contains the "canonical" question for this, it is here. The answer given by talonmies there includes the proper mechanics, as well as appropriate caveats:
there is additional, non-trivial complexity
the access will generally be less efficient than 1D access, because data access requires dereferencing 2 pointers, instead of 1.
(note that allocating an array of objects, where the object(s) has an embedded pointer to a dynamic allocation, is essentially the same as the 2D array concept, and the example you linked in your question is a reasonable demonstration for that)
Also, here is a thrust method for building a general dynamically allocated 2D array.
flattening:
If you think you must use the general 2D method, then go ahead, it's not impossible (although sometimes people struggle with the process!) However, due to the added complexity and reduced efficiency, the canonical "advice" here is to "flatten" your storage method, and use "simulated" 2D access. Here is one of many examples of questions/answers discussing "flattening".
general, dynamically allocated 3D case:
As we extend this to 3 (or higher!) dimensions, the general case becomes overly complex to handle, IMO. The additional complexity should strongly motivate us to seek alternatives. The triply-subscripted general case involves 3 pointer accesses before the data is actually retrieved, so even less efficient. Here is a fully worked example (2nd code example).
special case: array width known at compile time:
Note that it should be considered a special case when the array dimension(s) (the width, in the case of a 2D array, or 2 of the 3 dimensions for a 3D array) is known at compile-time. In this case, with an appropriate auxiliary type definition, we can "instruct" the compiler how the indexing should be computed, and in this case we can use doubly-subscripted access with considerably less complexity than the general case, and there is no loss of efficiency due to pointer-chasing. Only one pointer need be dereferenced to retrieve the data (regardless of array dimensionality, if n-1 dimensions are known at compile time for a n-dimensional array). The first code example in the already-mentioned answer here (first code example) gives a fully worked example of that in the 3D case, and the answer here gives a 2D example of this special case.
doubly-subscripted host code, singly-subscripted device code:
Finally another methodology option allows us to easily mix 2D (doubly-subscripted) access in host code while using only 1D (singly-subscripted, perhaps with "simulated 2D" access) in device code. A worked example of that is here. By organizing the underlying allocation as a contiguous allocation, then building the pointer "tree", we can enable doubly-subscripted access on the host, and still easily pass the flat allocation to the device. Although the example does not show it, it would be possible to extend this method to create a doubly-subscripted access system on the device based off a flat allocation and a manually-created pointer "tree", however this would have approximately the same issues as the 2D general dynamically allocated method given above: it would involve double-pointer (double-dereference) access, so less efficient, and there is some complexity associated with building the pointer "tree", for use in device code (e.g. it would necessitate an additional cudaMemcpy operation, probably).
From the above methods, you'll need to choose one that fits your appetite and needs. There is not one single recommendation that fits every possible case.
I'm writing a fairly straightforward function that sends an array over to a file descriptor. However, in order to send the data, I need to append a one byte header.
Here is a simplified version of what I'm doing and it seems to work:
void SendData(uint8_t* buffer, size_t length) {
uint8_t buffer_to_send[length + 1];
buffer_to_send[0] = MY_SPECIAL_BYTE;
memcpy(buffer_to_send + 1, buffer, length);
// more code to send the buffer_to_send goes here...
}
Like I said, the code seems to work fine, however, I've recently gotten into the habit of using the Google C++ style guide since my current project has no set style guide for it (I'm actually the only software engineer on my project and I wanted to use something that's used in industry). I ran Google's cpplint.py and it caught the line where I am creating buffer_to_send and threw some comment about not using variable length arrays. Specifically, here's what Google's C++ style guide has to say about variable length arrays...
http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Variable-Length_Arrays_and_alloca__
Based on their comments, it appears I may have found the root cause of seemingly random crashes in my code (which occur very infrequently, but are nonetheless annoying). However, I'm a bit torn as to how to fix it.
Here are my proposed solutions:
Make buffer_to_send essentially a fixed length array of a constant length. The problem that I can think of here is that I have to make the buffer as big as the theoretically largest buffer I'd want to send. In the average case, the buffers are much smaller, and I'd be wasting about 0.5KB doing so each time the function is called. Note that the program must run on an embedded system, and while I'm not necessarily counting each byte, I'd like to use as little memory as possible.
Use new and delete or malloc/free to dynamically allocate the buffer. The issue here is that the function is called frequently and there would be some overhead in terms of constantly asking the OS for memory and then releasing it.
Use two successive calls to write() in order to pass the data to the file descriptor. That is, the first write would pass only the one byte, and the next would send the rest of the buffer. While seemingly straightforward, I would need to research the code a bit more (note that I got this code handed down from a previous engineer who has since left the company I work for) in order to guarantee that the two successive writes occur atomically. Also, if this requires locking, then it essentially becomes more complex and has more performance impact than case #2.
Note that I cannot make the buffer_to_send a member variable or scope it outside the function since there are (potentially) multiple calls to the function at any given time from various threads.
Please let me know your opinion and what my preferred approach should be. Thanks for your time.
You can fold the two successive calls to write() in your option 3 into a single call using writev().
http://pubs.opengroup.org/onlinepubs/009696799/functions/writev.html
I would choose option 1. If you know the maximum length of your data, then allocate that much space (plus one byte) on the stack using a fixed size array. This is no worse than the variable length array you have shown because you must always have enough space left on the stack otherwise you simply won't be able to handle your maximum length (at worst, your code would randomly crash on larger buffer sizes). At the time this function is called, nothing else will be using the further space on your stack so it will be safe to allocate a fixed size array.
I have a problem I am working on where I need to use some sort of 2 dimensional array. The array is fixed width (four columns), but I need to create extra rows on the fly.
To do this, I have been using vectors of vectors, and I have been using some nested loops that contain this:
array.push_back(vector<float>(4));
array[n][0] = a;
array[n][1] = b;
array[n][2] = c;
array[n][3] = d;
n++
to add the rows and their contents. The trouble is that I appear to be running out of memory with the number of elements I was trying to create, so I reduced the number that I was using. But then I started reading about deque, and thought it would allow me to use more memory because it doesn't have to be contiguous. I changed all mentions of "vector" to "deque", in this loop, as well as all declarations. But then it appeared that I ran out of memory again, this time with even with the reduced number of rows.
I looked at how much memory my code is using, and when I am using deque, the memory rises steadily to above 2GB, and the program closes soon after, even when using the smaller number of rows. I'm not sure exactly where in this loop it is when it runs out of memory.
When I use vectors, the memory usage (for the same number of rows) is still under 1GB, even when the loop exits. It then goes on to a similar loop where more rows are added, still only reaching about 1.4GB.
So my question is. Is this normal for deque to use more than twice the memory of vector, or am I making an erroneous assumption in thinking I can just replace the word "vector" with "deque" in the declarations/initializations and the above code?
Thanks in advance.
I'm using:
MS Visual C++ 2010 (32-bit)
Windows 7 (64-bit)
The real answer here has little to do with the core data structure. The answer is that MSVC's implementation of std::deque is especially awful and degenerates to an array of pointers to individual elements, rather than the array of arrays it should be. Frankly, only twice the memory use of vector is surprising. If you had a better implementation of deque you'd get better results.
It all depends on the internal implementation of deque (I won't speak about vector since it is relatively straightforward).
Fact is, deque has completely different guarantees than vector (the most important one being that it supports O(1) insertion at both ends while vector only supports O(1) insertion at the back). This in turn means the internal structures managed by deque have to be more complex than vector.
To allow that, a typical deque implementation will split its memory in several non-contiguous blocks. But each individual memory block has a fixed overhead to allow the memory management to work (eg. whatever the size of the block, the system may need another 16 or 32 bytes or whatever in addition, just for bookkeeping). Since, contrary to a vector, a deque requires many small, independent blocks, the overhead stacks up which can explain the difference you see. Also note that those individual memory blocks need to be managed (maybe in separate structures?), which probably means some (or a lot of) additional overhead too.
As for a way to solve your problem, you could try what #BasileStarynkevitch suggested in the comments, this will indeed reduce your memory usage but it will get you only so far because at some point you'll still run out of memory. And what if you try to run your program on a machine that only has 256MB RAM? Any other solution which goal is to reduce your memory footprint while still trying to keep all your data in memory will suffer from the same problems.
A proper solution when handling large datasets like yours would be to adapt your algorithms and data structures in order to be able to handle small partitions at a time of your whole dataset, and load/save those partitions as needed in order to make room for the other partitions. Unfortunately since it probably means disk access, it also means a big drop in performance but hey, you can't eat the cake and have it too.
Theory
There two common ways to efficiently implement a deque: either with a modified dynamic array or with a doubly linked list.
The modified dynamic array uses is basically a dynamic array that can grow from both ends, sometimes called array deques. These array deques have all the properties of a dynamic array, such as constant-time random access, good locality of reference, and inefficient insertion/removal in the middle, with the addition of amortized constant-time insertion/removal at both ends, instead of just one end.
There are several implementations of modified dynamic array:
Allocating deque contents from the center of the underlying array,
and resizing the underlying array when either end is reached. This
approach may require more frequent resizings and waste more space,
particularly when elements are only inserted at one end.
Storing deque contents in a circular buffer, and only resizing when
the buffer becomes full. This decreases the frequency of resizings.
Storing contents in multiple smaller arrays, allocating additional
arrays at the beginning or end as needed. Indexing is implemented by
keeping a dynamic array containing pointers to each of the smaller
arrays.
Conclusion
Different libraries may implement deques in different ways, but generally as a modified dynamic array. Most likely your standard library uses the approach #1 to implement std::deque, and since you append elements only from one end, you ultimately waste a lot of space. For that reason, it makes an illusion that std::deque takes up more space than usual std::vector.
Furthermore, if std::deque would be implemented as doubly-linked list, that would result in a waste of space too since each element would need to accommodate 2 pointers in addition to your custom data.
Implementation with approach #3 (modified dynamic array approach too) would again result in a waste of space to accommodate additional metadata such as pointers to all those small arrays.
In any case, std::deque is less efficient in terms of storage than plain old std::vector. Without knowing what do you want to achieve I cannot confidently suggest which data structure do you need. However, it seems like you don't even know what deques are for, therefore, what you really want in your situation is std::vector. Deques, in general, have different application.
Deque can have additional memory overhead over vector because it's made of a few blocks instead of contiguous one.
From en.cppreference.com/w/cpp/container/deque:
As opposed to std::vector, the elements of a deque are not stored contiguously: typical implementations use a sequence of individually allocated fixed-size arrays.
The primary issue is running out of memory.
So, do you need all the data in memory at once?
You may never be able to accomplish this.
Partial Processing
You may want to consider processing the data into "chunks" or smaller sub-matrices. For example, using the standard rectangular grid:
Read data of first quadrant.
Process data of first quandrant.
Store results (in a file) of first quandrant.
Repeat for remaining quandrants.
Searching
If you are searching for a particle or a set of datum, you can do that without reading in the entire data set into memory.
Allocate a block (array) of memory.
Read a portion of the data into this block of memory.
Search the block of data.
Repeat steps 2 and 3 until the data is found.
Streaming Data
If your application is receiving the raw data from an input source (other than a file), you will want to store the data for later processing.
This will require more than one buffer and is more efficient using at least two threads of execution.
The Reading Thread will be reading data into a buffer until the buffer is full. When the buffer is full, it will read data into another empty one.
The Writing Thread will initially wait until either the first read buffer is full or the read operation is finished. Next, the Writing Thread takes data out of the read buffer and writes to a file. The Write Thread then starts writing from the next read buffer.
This technique is called Double Buffering or Multiple Buffering.
Sparse Data
If there is a lot of zero or unused data in the matrix, you should try using Sparse Matrices. Essentially, this is a list of structures that hold the data's coordinates and the value. This also works when most of the data is a common value other than zero. This saves a lot of memory space; but costs a little bit more execution time.
Data Compression
You could also change your algorithms to use data compression. The idea here is to store the data location, value and the number or contiguous equal values (a.k.a. runs). So instead of storing 100 consecutive data points of the same value, you would store the starting position (of the run), the value, and 100 as the quantity. This saves a lot of space, but requires more processing time when accessing the data.
Memory Mapped File
There are libraries that can treat a file as memory. Essentially, they read in a "page" of the file into memory. When the requests go out of the "page", they read in another page. All this is performed "behind the scenes". All you need to do is treat the file like memory.
Summary
Arrays and deques are not your primary issue, quantity of data is. Your primary issue can be resolved by processing small pieces of data at a time, compressing the data storage, or treating the data in the file as memory. If you are trying to process streaming data, don't. Ideally, streaming data should be placed into a file and then processed later.
A historical purpose of a file is to contain data that doesn't fit into memory.
Currently I read arrays in C++ with ifstream, read and reinterpret_cast by making a loop on values. Is it possible to load for example an unsigned int array from a binary file in one time without making a loop ?
Thank you very much
Yes, simply pass the address of the first element of the array, and the size of the array in bytes:
// Allocate, for example, 47 ints
std::vector<int> numbers(47);
// Read in as many ints as 'numbers' has room for.
inFile.read(&numbers[0], numbers.size()*sizeof(numbers[0]));
Note: I almost never use raw arrays. If I need a sequence that looks like an array, I use std::vector. If you must use an array, the syntax is very similar.
The ability to read and write binary images is non-portable. You may not be able to re-read the data on another machine, or even on the same machine with a different compiler. But, you have that problem already, with the solution that you are using now.
I have a little problem here, i write c++ code to create an array but when i want to set array size to 100,000,000 or more i got an error.
this is my code:
int i=0;
double *a = new double[n*n];
this part is so important for my project.
When you think you need an array of 100,000,000 elements, what you actually need is a different data structure that you probably have never heard of before. Maybe a hash map, or maybe a sparse matrix.
If you tell us more about the actual problem you are trying to solve, we can provide better help.
In general, the only reason that would fail would be due to lack of memory/memory fragmentation/available address space. That is, trying to allocate 800MB of memory. Granted, I have no idea why your system's virtual memory can't handle that, but maybe you allocated a bunch of other stuff. It doesn't matter.
Your alternatives are to tricks like memory-mapped files, sparse arrays, and so forth instead of an explicit C-style array.
If you do not have sufficient memory, you may need to use a file to store your data and process it in smaller chunks.
Don't know if IMSL provides what you are looking for, however, if you want to work on smaller chunks you might devise an algorithm that can call IMSL functions with these small chunks and later merge the results. For example, you can do matrix multiplication by combining multiplication of sub-matrices.