Simple question:
For my assignment I am asked to count the words in a file and keep track of their frequency. I am to create a parallel int array for the frequency.
Is a parallel array a special data structure, or does it simply mean I am creating 2 arrays, where one is dependent on the other. For example, I create 2 dynamic arrays and update both inside the loop with respect to my i variable from the for loop.
A parallel array is basically what you posit in your question. It's two distinct arrays connected by the index.
For example, a parallel array counting frequencies of temperatures may be:
int tempVal [100];
size_t tempCount[100];
and the temperature value at index 42 has a frequency given by tempCount[42].
Purists will argue (and they do have a point) that it's better to provide a single array of a structure such as:
typedef struct {
int val;
size_t count;
} tFreq;
tFreq tempFreq[100];
and C++ has collections that will do this for you, such as std::pair. But, if your assignment specifically calls for parallel arrays, I suspect std::pair would not be considered thus.
There isn't a parallel array data structure as such.
You can create two arrays and address them in parallel.
There are some alternatives, such as creating an array of std::pair, or (probably the "right" one for the task at hand) an std::unordered_map (or possibly an std::map instead).
No structure is special, it's always composed of primitives.
Related
I give online coding contests, where the speed of the program is everything. Over the time I have come across 3 ways to use the concept of array in C++. The questions of the contests usually require us to create a dynamic array of the given size. So its just a one time creation of the dynamic array as per the input and we don't resize the array again.
std::vector
Vectors look the most fancy and everyone tends to love them. But few days back one of the question gave me the TIME_LIMIT_EXCEEDED error when doing it with vectors.When I implemented the same logic with normal arrays, the program was submitted successfully.On researching, I found out that using the push_back() function takes a long time as compared to a normal arr[i]=x;
std::array
I don't have much knowledge about its performance. But it looks like a nicer way to handle arrays.
default arrays in C++
I do the dynamic allocation using int *arr=new int[given_size]; and then use the array normally.Passing an array as argument is not as simple as vectors but its not a big deal.
Apart from this there are also times when I have to work with 2D arrays and I am always unsure about what could be the fastest way. vector<vector<int>> is regarded slow in some forums and so is using multidimensional pointers. So I like to use a 1D array with a typedef function to handle its index but it gets complicated when I have to pass a row to a function.
Most of the answers in forums are based on what the OP was trying to do and this gives different answers. What I want to know is which is the best and long term way to use to have maximum speed/efficiency.
push_back takes a long time compared to arr[i]=x;.
Sorry but you are showing your lack of experience with vectors here, because your examples do two different things.
You are comparing something like this code
vector<int> vec; // vector created with size zero
for (...)
vec.push_back(x); // vector size increases
with this code
int arr[N];
for (...)
arr[i] = x;
The difference is that in the first case the vector has size 0 and it's size increases as you add items to it (this takes extra time), but in the second case the array starts out at it's final size. With an array this is how it must be, but with vectors you have a choice. If you know what the final size of the vector is you should code it like this
vector<int> vec(N); // vector created at size N, note use () not []
for (...)
vec[i] = x;
That is the code you should be comparing with the array code for efficiency,
You might also want to research the resize and reserve methods of a vector. Vectors (if nothing else) are much more flexible than arrays.
C++ newbie here! I would like to simulate a population containing patches, containing individuals, containing chromosomes, containing genes.
What are the pros and cons of using a series of simple classes versus a highly dimensional matrix in C++? Typically, does the time to access a memory slot varies in between the two technics?
Highly dimensional Matrix
One could make "a vector of vectors of vectors of vectors" (or a C-style highly dimensional arrays of integers) and access any gene in memory with
for (int patch=0;patch<POP.size();patch++)
{
for (int ind=0;ind<POP[patch].size();patch++)
{
for (int chrom=0;chrom<POP[patch][ind].size();chrom++)
{
for (int gene=0;gene<POP[patch][ind][chrom].size();gene++)
{
POP[patch][ind][chrom][gene];
}
}
}
}
Series of Simple Classes
One could use a series of simple classes and access any gene in memory with
for (int patch=0;patch<POP->PATCHES.size();patch++)
{
for (int ind=0;ind<POP->PATCHES[patch]->INDIVIDUALS.size();patch++)
{
for (int chrom=0;chrom<POP->PATCHES[patch]->INDIVIDUALS[ind]->CHROMOSOMES.size();chrom++)
{
for (int gene=0;gene<POP->PATCHES[patch]->INDIVIDUALS[ind]->CHROMOSOMES[chrom]->GENES.size();gene++)
{
POP->PATCHES[patch]->INDIVIDUALS[ind]->CHROMOSOMES[chrom]->GENES[gene];
}
}
}
}
While a high-dimensional matrix would work, consider that you might want to add more information to an individual. It might not just have chromosomes, but also an age, siblings, parents, phenotypes, et cetera. It is then natural to have a class Individual, which can contain all that information along with its list of chromosomes. Using classes will group relevant information together.
While I in general agree with #g-sliepen's answer, there is an additional point you should know about:
C++ gives you the ability to make a distinction between interface and type. You can leave the type abstract for the users of your code (even if that is only you) and provide a finite set of operations on it.
Using this pattern allows you to change the implementation completely (e.g. back to vectors for parallel computation etc.) later without having to change the code using it (e.g. a concrete simulation).
I won't cover what's already been suggested as it is generally a good idea to store your individual entities as a class with all relevant fields associared with it, but I'll just address your first suggestion:
The issue with using something like a std::vector<std::vector<std::vector<std::vector<type>>>> (apart from the fact it's a pain to handle generically) is that whilst the overall std::vector enclosing the structure has contiguous storage (so long as you aren't storing bools in your std::vector that is) the inner vectors are not contiguous with each other or the other elements.
Due to this, if you are storing a large amount of data in your structure and need access and iteration to be as fast as possible, this method of storage is not ideal - it also complicates matters of iterating through the entire structure.
A good solution for storing a large multi-dimensional "matrix" (technically a rank 4 tensor in this case I suppose) when you require fast iteration and random access is to write a wrapper around a single std::vector in some row-major / column-major configuration such that all your data is stored as a contiguous block and you can iterate over it all via a single loop or call to std::for_each (for example). Then each index by which you access the structure would correspond to patch, ind, chrom and gene in order.
An example of a pre-built data structure which could handle this is boost::multi_array if you'd rather not code the wrapper yourself.
There are two major ways to do multidimensional arrays. Vector of vectors (aka jagged array) and really multidimensional array - n dimensional cube. Using the latter one means for example, that all chromozomes have the same amount of genes and every individual has the same amount of chromozomes. If You can accept those restrictions, You get some advantages like continuous memory storage.
struct Face
{
// Matrixd is 1D representation of 2D matrix
std::array < Matrixd<5,5>, 2 > M;
};
std::vector <Face> face;
I have a distributed for-loop among nodes. After all nodes finish working on their elements I would like to transfer corresponding elements among nodes. But AFAIK to use MPI_Allgatherv the data should be contiguous. First of all, I switched to 1D representation of 2D matrices (I was using [][] notation before). Now I want to make face.M to be contiguous. I am thinking to copy all elements of say, M[0] to an std::array an transfer that among nodes. Is this way efficient? To give an idea of number of data I work with, if I have 20k cells, at maximum I have 20k*3=60k faces. I might have a million of cells, too.
A true 2D array in C/C++, e.g. int foo[5][5] is already contiguous in memory; it's basically just syntactic sugar for int foo[25] where accesses like foo[3][2] implicitly look up foo[3*5 + 2] in the flat equivalent. Switching to a Matrixd defined in a single dimension won't change the actual memory layout.
std::array is (mostly) just a wrapper for C-style arrays as well; with no virtual members, and compile time defined size with no internal pointers (just the raw array), it's also going to be contiguous. I strongly suspect if you checked the assembly produced, you'd find that the array of Matrixds is already contiguous.
In short, I don't think you need to change anything; you're already contiguous, so MPI should be fine.
Let me preface this with the statement that most of my background has been with functional programming languages so I'm fairly novice with C++.
Anyhow, the problem I'm working on is that I'm parsing a csv file with multiple variable types. A sample line from the data looks as such:
"2011-04-14 16:00:00, X, 1314.52, P, 812.1, 812"
"2011-04-14 16:01:00, X, 1316.32, P, 813.2, 813.1"
"2011-04-14 16:02:00, X, 1315.23, C, 811.2, 811.1"
So what I've done is defined a struct which stores each line. Then each of these are stored in a std::vector< mystruct >. Now say I want to subset this vector by column 4 into two vectors where every element with P in it is in one and C in the other.
Now the example I gave is fairly simplified, but the actual problem involves subsetting multiple times.
My initial naive implementation was iterate through the entire vector, creating individual subsets defined by new vectors, then subsetting those newly created vectors. Maybe something a bit more memory efficient would be to create an index, which would then be shrunk down.
Now my question is, is there a more efficient, in terms of speed/memory usage) way to do this either by this std::vector< mystruct > framework or if there's some better data structure to handle this type of thing.
Thanks!
EDIT:
Basically the output I'd like is first two lines and last line separately. Another thing worth noting, is that typically the dataset is not ordered like the example, so the Cs and Ps are not grouped together.
I've used std::partition for this. It's not part of boost though.
If you want a data structure that allows you to move elements between different instances cheaply, the data structure you are looking for is std::list<> and it's splice() family of functions.
I understand you have not per se trouble doing this but you seem to be concerned about memory usage and performance.
Depending on the size of your struct and the number of entries in the csv file it may be advisabe to use a smart pointer if you don't need to modify the partitioned data so the mystruct objects are not copied:
typedef std::vector<boost::shared_ptr<mystruct> > table_t;
table_t cvs_data;
If you use std::partition (as another poster suggested) you need to define a predicate that takes the indirection of the shared_ptr into accont.
This is my little big question about containers, in particular, arrays.
I am writing a physics code that mainly manipulates a big (> 1 000 000) set of "particles" (with 6 double coordinates each). I am looking for the best way (in term of performance) to implement a class that will contain a container for these data and that will provide manipulation primitives for these data (e.g. instantiation, operator[], etc.).
There are a few restrictions on how this set is used:
its size is read from a configuration file and won't change during execution
it can be viewed as a big two dimensional array of N (e.g. 1 000 000) lines and 6 columns (each one storing the coordinate in one dimension)
the array is manipulated in a big loop, each "particle / line" is accessed and computation takes place with its coordinates, and the results are stored back for this particle, and so on for each particle, and so on for each iteration of the big loop.
no new elements are added or deleted during the execution
First conclusion, as the access on the elements is essentially done by accessing each element one by one with [], I think that I should use a normal dynamic array.
I have explored a few things, and I would like to have your opinion on the one that can give me the best performances.
As I understand there is no advantage to use a dynamically allocated array instead of a std::vector, so things like double** array2d = new ..., loop of new, etc are ruled out.
So is it a good idea to use std::vector<double> ?
If I use a std::vector, should I create a two dimensional array like std::vector<std::vector<double> > my_array that can be indexed like my_array[i][j], or is it a bad idea and it would be better to use std::vector<double> other_array and acces it with other_array[6*i+j].
Maybe this can gives better performance, especially as the number of columns is fixed and known from the beginning.
If you think that this is the best option, would it be possible to wrap this vector in a way that it can be accessed with a index operator defined as other_array[i,j] // same as other_array[6*i+j] without overhead (like function call at each access) ?
Another option, the one that I am using so far is to use Blitz, in particular blitz::Array:
typedef blitz::Array<double,TWO_DIMENSIONS> store_t;
store_t my_store;
Where my elements are accessed like that: my_store(line, column);.
I think there are not much advantage to use Blitz in my case because I am accessing each element one by one and that Blitz would be interesting if I was using operations directly on array (like matrix multiplication) which I am not.
Do you think that Blitz is OK, or is it useless in my case ?
These are the possibilities I have considered so far, but maybe the best one I still another one, so don't hesitate to suggest me other things.
Thanks a lot for your help on this problem !
Edit:
From the very interesting answers and comments bellow a good solution seems to be the following:
Use a structure particle (containing 6 doubles) or a static array of 6 doubles (this avoid the use of two dimensional dynamic arrays)
Use a vector or a deque of this particle structure or array. It is then good to traverse them with iterators, and that will allow to change from one to another later.
In addition I can also use a Blitz::TinyVector<double,6> instead of a structure.
So is it a good idea to use std::vector<double> ?
Usually, a std::vector should be the first choice of container. You could use either std::vector<>::reserve() or std::vector<>::resize() to avoid reallocations while populating the vector. Whether any other container is better can be found by measuring. And only by measuring. But first measure whether anything the container is involved in (populating, accessing elements) is worth optimizing at all.
If I use a std::vector, should I create a two dimensional array like std::vector<std::vector<double> > [...]?
No. IIUC, you are accessing your data per particle, not per row. If that's the case, why not use a std::vector<particle>, where particle is a struct holding six values? And even if I understood incorrectly, you should rather write a two-dimensional wrapper around a one-dimensional container. Then align your data either in rows or columns - what ever is faster with your access patterns.
Do you think that Blitz is OK, or is it useless in my case?
I have no practical knowledge about blitz++ and the areas it is used in. But isn't blitz++ all about expression templates to unroll loop operations and optimizing away temporaries when doing matrix manipulations? ICBWT.
First of all, you don't want to scatter the coordinates of one given particle all over the place, so I would begin by writing a simple struct:
struct Particle { /* coords */ };
Then we can make a simple one dimensional array of these Particles.
I would probably use a deque, because that's the default container, but you may wish to try a vector, it's just that 1.000.000 of particles means about a single chunk of a few MBs. It should hold but it might strain your system if this ever grows, while the deque will allocate several chunks.
WARNING:
As Alexandre C remarked, if you go the deque road, refrain from using operator[] and prefer to use iteration style. If you really need random access and it's performance sensitive, the vector should prove faster.
The first rule when choosing from containers is to use std::vector. Then, only after your code is complete and you can actually measure performance, you can try other containers. But stick to vector first. (And use reserve() from the start)
Then, you shouldn't use an std::vector<std::vector<double> >. You know the size of your data: it's 6 doubles. No need for it to be dynamic. It is constant and fixed. You can define a struct to hold you particle members (the six doubles), or you can simply typedef it: typedef double particle[6]. Then, use a vector of particles: std::vector<particle>.
Furthermore, as your program uses the particle data contained in the vector sequentially, you will take advantage of the modern CPU cache read-ahead feature at its best performance.
You could go several ways. But in your case, don't declare astd::vector<std::vector<double> >. You're allocating a vector (and you copy it around) for every 6 doubles. Thats way too costly.
If you think that this is the best option, would it be possible to wrap this vector in a way that it can be accessed with a index operator defined as other_array[i,j] // same as other_array[6*i+j] without overhead (like function call at each access) ?
(other_array[i,j] won't work too well, as i,j employs the comma operator to evaluate the value of "i", then discards that and evaluates and returns "j", so it's equivalent to other_array[i]).
You will need to use one of:
other_array[i][j]
other_array(i, j) // if other_array implements operator()(int, int),
// but std::vector<> et al don't.
other_array[i].identifier // identifier is a member variable
other_array[i].identifier() // member function getting value
other_array[i].identifier(double) // member function setting value
You may or may not prefer to put get_ and set_ or similar on the last two functions should you find them useful, but from your question I think you won't: functions are prefered in APIs between parts of large systems involving many developers, or when the data items may vary and you want the algorithms working on the data to be independent thereof.
So, a good test: if you find yourself writing code like other_array[i][3] where you've decided "3" is the double with the speed in it, and other_array[i][5] because "5" is the the acceleration, then stop doing that and give them proper identifiers so you can say other_array[i].speed and .acceleration. Then other developers can read and understand it, and you're much less likely to make accidental mistakes. On the other hand, if you are iterating over those 6 elements doing exactly the same things to each, then you probably do want Particle to hold a double[6], or to provide an operator[](int). There's no problem doing both:
struct Particle
{
double x[6];
double& speed() { return x[3]; }
double speed() const { return x[3]; }
double& acceleration() { return x[5]; }
...
};
BTW / the reason that vector<vector<double> > may be too costly is that each set of 6 doubles will be allocated on the heap, and for fast allocation and deallocation many heap implementations use fixed-size buckets, so your small request will be rounded up t the next size: that may be a significant overhead. The outside vector will also need to record a extra pointer to that memory. Further, heap allocation and deallocation is relatively slow - in you're case, you'd only be doing it at startup and shutdown, but there's no particular point in making your program slower for no reason. Even more importantly, the areas on the heap may just around in memory, so your operator[] may have cache-faults pulling in more distinct memory pages than necessary, slowing the entire program. Put another way, vectors store elements contiguously, but the pointed-to-vectors may not be contiguous.