when using C++ vector, time spent is 718 milliseconds,
while when I use Array, time is almost 0 milliseconds.
Why so much performance difference?
int _tmain(int argc, _TCHAR* argv[])
{
const int size = 10000;
clock_t start, end;
start = clock();
vector<int> v(size*size);
for(int i = 0; i < size; i++)
{
for(int j = 0; j < size; j++)
{
v[i*size+j] = 1;
}
}
end = clock();
cout<< (end - start)
<<" milliseconds."<<endl; // 718 milliseconds
int f = 0;
start = clock();
int arr[size*size];
for(int i = 0; i < size; i++)
{
for(int j = 0; j < size; j++)
{
arr[i*size+j] = 1;
}
}
end = clock();
cout<< ( end - start)
<<" milliseconds."<<endl; // 0 milliseconds
return 0;
}
Your array arr is allocated on the stack, i.e., the compiler has calculated the necessary space at compile time. At the beginning of the method, the compiler will insert an assembler statement like
sub esp, 10000*10000*sizeof(int)
which means the stack pointer (esp) is decreased by 10000 * 10000 * sizeof(int) bytes to make room for an array of 100002 integers. This operation is almost instant.
The vector is heap allocated and heap allocation is much more expensive. When the vector allocates the required memory, it has to ask the operating system for a contiguous chunk of memory and the operating system will have to perform significant work to find this chunk of memory.
As Andreas says in the comments, all your time is spent in this line:
vector<int> v(size*size);
Accessing the vector inside the loop is just as fast as for the array.
For an additional overview see e.g.
[What and where are the stack and heap?
[http://computer.howstuffworks.com/c28.htm][2]
[http://www.cprogramming.com/tutorial/virtual_memory_and_heaps.html][3]
Edit:
After all the comments about performance optimizations and compiler settings, I did some measurements this morning. I had to set size=3000 so I did my measurements with roughly a tenth of the original entries. All measurements performed on a 2.66 GHz Xeon:
With debug settings in Visual Studio 2008 (no optimization, runtime checks, and debug runtime) the vector test took 920 ms compared to 0 ms for the array test.
98,48 % of the total time was spent in vector::operator[], i.e., the time was indeed spent on the runtime checks.
With full optimization, the vector test needed 56 ms (with a tenth of the original number of entries) compared to 0 ms for the array.
The vector ctor required 61,72 % of the total application running time.
So I guess everybody is right depending on the compiler settings used. The OP's timing suggests an optimized build or an STL without runtime checks.
As always, the morale is: profile first, optimize second.
If you are compiling this with a Microsoft compiler, to make it a fair comparison you need to switch off iterator security checks and iterator debugging, by defining _SECURE_SCL=0 and _HAS_ITERATOR_DEBUGGING=0.
Secondly, the constructor you are using initialises each vector value with zero, and you are not memsetting the array to zero before filling it. So you are traversing the vector twice.
Try:
vector<int> v;
v.reserve(size*size);
To get a fair comparison I think something like the following should be suitable:
#include <sys/time.h>
#include <vector>
#include <iostream>
#include <algorithm>
#include <numeric>
int main()
{
static size_t const size = 7e6;
timeval start, end;
int sum;
gettimeofday(&start, 0);
{
std::vector<int> v(size, 1);
sum = std::accumulate(v.begin(), v.end(), 0);
}
gettimeofday(&end, 0);
std::cout << "= vector =" << std::endl
<< "(" << end.tv_sec - start.tv_sec
<< " s, " << end.tv_usec - start.tv_usec
<< " us)" << std::endl
<< "sum = " << sum << std::endl << std::endl;
gettimeofday(&start, 0);
int * const arr = new int[size];
std::fill(arr, arr + size, 1);
sum = std::accumulate(arr, arr + size, 0);
delete [] arr;
gettimeofday(&end, 0);
std::cout << "= Simple array =" << std::endl
<< "(" << end.tv_sec - start.tv_sec
<< " s, " << end.tv_usec - start.tv_usec
<< " us)" << std::endl
<< "sum = " << sum << std::endl << std::endl;
}
In both cases, dynamic allocation and deallocation is performed, as well as accesses to elements.
On my Linux box:
$ g++ -O2 foo.cpp
$ ./a.out
= vector =
(0 s, 21085 us)
sum = 7000000
= Simple array =
(0 s, 21148 us)
sum = 7000000
Both the std::vector<> and array cases have comparable performance. The point is that std::vector<> can be just as fast as a simple array if your code is structured appropriately.
On a related note switching off optimization makes a huge difference in this case:
$ g++ foo.cpp
$ ./a.out
= vector =
(0 s, 120357 us)
sum = 7000000
= Simple array =
(0 s, 60569 us)
sum = 7000000
Many of the optimization assertions made by folks like Neil and jalf are entirely correct.
HTH!
EDIT: Corrected code to force vector destruction to be included in time measurement.
Change assignment to eg. arr[i*size+j] = i*j, or some other non-constant expression. I think compiler optimizes away whole loop, as assigned values are never used, or replaces array with some precalculated values, so that loop isn't even executed and you get 0 milliseconds.
Having changed 1 to i*j, i get the same timings for both vector and array, unless pass -O1 flag to gcc, then in both cases I get 0 milliseconds.
So, first of all, double-check whether your loops are actually executed.
You are probably using VC++, in which case by default standard library components perform many checks at run-time (e.g whether index is in range). These checks can be turned off by defining some macros as 0 (I think _SECURE_SCL).
Another thing is that I can't even run your code as is: the automatic array is way too large for the stack. When I make it global, then with MingW 3.5 the times I get are 627 ms for the vector and 26875 ms (!!) for the array, which indicates there are really big problems with an array of this size.
As to this particular operation (filling with value 1), you could use the vector's constructor:
std::vector<int> v(size * size, 1);
and the fill algorithm for the array:
std::fill(arr, arr + size * size, 1);
Two things. One, operator[] is much slower for vector. Two, vector in most implementations will behave weird at times when you add in one element at a time. I don't mean just that it allocates more memory but it does some genuinely bizarre things at times.
The first one is the main issue. For a mere million bytes, even reallocating the memory a dozen times should not take long (it won't do it on every added element).
In my experiments, preallocating doesn't change its slowness much. When the contents are actual objects it basically grinds to a halt if you try to do something simple like sort it.
Conclusion, don't use stl or mfc vectors for anything large or computation heavy. They are implemented poorly/slowly and cause lots of memory fragmentation.
When you declare the array, it lives in the stack (or in static memory zone), which it's very fast, but can't increase its size.
When you declare the vector, it assign dynamic memory, which it's not so fast, but is more flexible in the memory allocation, so you can change the size and not dimension it to the maximum size.
When profiling code, make sure you are comparing similar things.
vector<int> v(size*size);
initializes each element in the vector,
int arr[size*size];
doesn't. Try
int arr[size * size];
memset( arr, 0, size * size );
and measure again...
Related
This question already has answers here:
Performance: memset
(2 answers)
Why might std::vector be faster than a raw dynamically allocated array?
(2 answers)
Why is iterating though `std::vector` faster than iterating though `std::array`?
(2 answers)
Idiomatic way of performance evaluation?
(1 answer)
Closed 3 years ago.
When I run the following program (with optimization on), the for loop with the std::vector takes about 0.04 seconds while the for loop with the array takes 0.0001 seconds.
#include <iostream>
#include <vector>
#include <chrono>
int main()
{
int len = 800000;
int* Data = new int[len];
int arr[3] = { 255, 0, 0 };
std::vector<int> vec = { 255, 0, 0 };
auto start = std::chrono::high_resolution_clock::now();
for (int i = 0; i < len; i++) {
Data[i] = vec[0];
}
auto finish = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> elapsed = finish - start;
std::cout << "The vector took " << elapsed.count() << "seconds\n";
start = std::chrono::high_resolution_clock::now();
for (int i = 0; i < len; i++) {
Data[i] = arr[0];
}
finish = std::chrono::high_resolution_clock::now();
elapsed = finish - start;
std::cout << "The array took " << elapsed.count() << "seconds \n";
char s;
std::cin >> s;
delete[] Data;
}
The code is a simplified version of a performance issue I was having while writing a raycaster. The len variable corresponds to how many times the loop in the original program needs to run (400 pixels * 400 pixels * 50 maximum render distance). For complicated reasons (perhaps that I don't fully understand how to use arrays) I have to use a vector rather than an array in the actual raycaster. However, as this program demonstrates, that would only give me 20 frames per second as opposed to the envied 10,000 frames per second that using an array would supposedly give me (obviously, this is just a simplified performance test). But regardless of how accurate those numbers are, I still want to boost my frame rate as much as possible. So, why is the vector performing so much slower than the array? Is there a way to speed it up? Thanks for your help. And if there's anything else I'm doing weirdly that might be affecting performance, please let me know. I didn't even know about optimization until researching an answer for this question, so if there are any more things like that which might boost the performance, please let me know (and I'd prefer if you explained where those settings are in the properties manager rather than command line since I don't yet know how to use the command line)
Let us observe how GCC optimizes this test program:
#include <vector>
int main()
{
int len = 800000;
int* Data = new int[len];
int arr[3] = { 255, 0, 0 };
std::vector<int> vec = { 255, 0, 0 };
for (int i = 0; i < len; i++) {
Data[i] = vec[0];
}
for (int i = 0; i < len; i++) {
Data[i] = arr[0];
}
delete[] Data;
}
The compiler rightly notices that the vector is constant, and eliminates it. Exactly same code is generated for both loops. Therefore it should be irrelevant whether the first loop uses array or vector.
.L2:
movups XMMWORD PTR [rcx], xmm0
add rcx, 16
cmp rsi, rcx
jne .L2
What makes difference in your test program is the order of loops. The comments point out that when a third loop is added to the beginning, both loops take the same time.
I would expect that with a modern compiler accessing a vector would be approximately as fast as accessing an array, when optimization is enabled and debug is disabled. If there is an observable difference in your actual program, the problem lies somewhere else.
It is about caches. I dont know how it works detailed but Data[] is getting known better by cpu while it is used. If you reverse the order of calculation you can see 'vector is faster'.
But actually, you are testing neither vector nor array. Let's assume that vec[0] resides at 0x01 memory location, arr[0] resides at 0xf1. Only difference is reading a word from different single memory adresses. So you are testing how fast can I assign a value to elements of dynamically allocated array.
Note: std::chrono::high_resolution_clock might not be sufficient to measure ticks. It is better to use steady_clock as cppreference says.
Problem Statement is to find prime number below 2 billion in timeframe < 20 sec.
I followed below approaches.
Divide the number n with list of number k ( k < sqrt(n)) - took 20 sec
Divide the number n with list of prime number below sqrt(n).In this scenario I stored prime numbers in std::list - took more than 180 sec
Can someone help me understand why did 2nd approach take longtime even though we reduced no of divisions by 50%(approx)? or Did I choose wrong Data Structure?
Approach 1:
#include <iostream>
#include<list>
#include <ctime>
using namespace std;
list<long long> primeno;
void ListPrimeNumber();
int main()
{
clock_t time_req = clock();
ListPrimeNumber();
time_req = clock() - time_req;
cout << "time taken " << static_cast<float>(time_req) / CLOCKS_PER_SEC << " seconds" << endl;
return 0;
}
void check_prime(int i);
void ListPrimeNumber()
{
primeno.push_back(2);
primeno.push_back(3);
primeno.push_back(5);
for (long long i = 6; i <= 20000000; i++)
{
check_prime(i);
}
}
void check_prime(int i)
{
try
{
int j = 0;
int limit = sqrt(i);
for (j = 2 ; j <= limit;j++)
{
if(i % j == 0)
{
break;
}
}
if( j > limit)
{
primeno.push_back(i);
}
}
catch (exception ex)
{
std::cout << "Message";
}
}
Approach 2 :
#include <iostream>
#include<list>
#include <ctime>
using namespace std;
list<long long> primeno;
int noofdiv = 0;
void ListPrimeNumber();
int main()
{
clock_t time_req = clock();
ListPrimeNumber();
time_req = clock() - time_req;
cout << "time taken " << static_cast<float>(time_req) / CLOCKS_PER_SEC << " seconds" << endl;
cout << "No of divisions : " << noofdiv;
return 0;
}
void check_prime(int i);
void ListPrimeNumber()
{
primeno.push_back(2);
primeno.push_back(3);
primeno.push_back(5);
for (long long i = 6; i <= 10000; i++)
{
check_prime(i);
}
}
void check_prime(int i)
{
try
{
int limit = sqrt(i);
for (int iter : primeno)
{
noofdiv++;
if (iter <= limit && (i%iter) == 0)
{
break;
}
else if (iter > limit)
{
primeno.push_back(i);
break;
}
}
}
catch (exception ex)
{
std::cout << "Message";
}
}
The reason your second example takes longer is you're iterating a std::list.
A std::list in C++ is a linked list, which means it doesn't use contiguous memory. This is bad because to iterate the list you must jump from node to node in a (to the CPU/prefetcher) unpredictable way. Also, You're most likely only "using" a few bytes of each cacheline. RAM is slow. Fetching a byte from RAM takes a lot longer than fetching it from L1. CPUs are fast these days, so your program is most of the time not doing anything and waiting for memory to arrive.
Use a std::vector instead. It stores all values one after the other and iterating is very cheap. Since you're iterating forward in memory without jumping, you're using the full cacheline and your prefetcher will be able to fetch further pages before you need them because your access of memory is predictable.
It has been proven by numerous people, including Bjarne Stroustrup, that std::vector is in a lot of cases faster than std::list, even in cases where the std::list has "theoretically" better complexity (random insert, delete, ...) just because caching helps a lot. So always use std::vector as your default. And if you think a linked list would be faster in your case, measure it and be surprised that - most of the time - std::vector dominates.
Edit: as others have noted, your method of finding primes isn't very efficient. I just played around a bit and implemented a Sieve of Eratosthenes using a bitset.
constexpr int max_prime = 1000000000;
std::bitset<max_prime> *bitset = new std::bitset<max_prime>{};
// Note: Bit SET means NO prime
bitset->set(0);
bitset->set(1);
for(int i = 4; i < max_prime ; i += 2)
bitset->set(i); // set all even numbers
int max = sqrt(max_prime);
for(int i = 3; i < max; i += 2) { // No point testing even numbers as they can't be prime
if(!bitset->test(i)) { // If i is prime
for(int j = i * 2; j < no_primes; j += i)
bitset->set(j); // set all multiples of i to non-prime
}
}
This takes between 4.2 and 4.5 seconds 30 seconds (not sure why it changed that much after slight modifications... must be an optimization I'm not hitting anymore) to find all primes below one Billion (1,000,000,000) on my machine. Your approach took way too long even for 100 million. I cancelled the 1 Billion search after about two minutes.
Comparison for 100 million:
time taken: 63.515 seconds
time taken bitset: 1.874 seconds
No of divisions : 1975961174
No of primes found: 5761455
No of primes found bitset: 5761455
I'm not a mathematician so I'm pretty sure there are still ways to optimize it further, I only optimize for even numbers.
The first thing to do is make sure you are compiling with optimisations enabled. The c++ standard library template classes tend to perform very poorly with unoptimised code as they generate lots of function calls. The optimiser inlines most of these function calls which makes them much cheaper.
std::list is a linked list. Its is mostly useful where you want to insert or remove elements randomly (i.e. not from the end).
For the case where you are only appending to the end of a list std::list has the following issues:
Iterating through the list is relatively expensive as the code has to follow node pointers and then retrieve the data
The list uses quite a lot more memory, each element needs a pointer to the previous and next nodes in addition to the actual data. On a 64-bit system this equates to 20 bytes per element rather than 4 for a list of int
As the elements in the list are not contiguous in memory the compiler can't perform as many SIMD optimisations and you will suffer more from CPU cache misses
A std::vector would solve all of the above as its memory is contiguous and iterating through it is basically just a case of incrementing an array index. You do need to make sure that you call reserve on your vector at the beginning with a sufficiently large value so that appending to the vector doesn't cause the whole array to be copied to a new larger array.
A bigger optimisation than the above would be to use the Sieve of Eratosthenes to calculate your primes. As generating this light require random deletions (depending on your exact implementation) std::list might perform better than std::vector though even in this case the overheads of std::list might not outweigh its costs.
A test at Ideone (the OP code with few superficial alterations) completely contradicts the claims made in this question:
/* check_prime__list:
time taken No of divisions No of primes
10M: 0.873 seconds 286144936 664579
20M: 2.169 seconds 721544444 1270607 */
2B: projected time: at least 16 minutes but likely much more (*)
/* check_prime__nums:
time taken No of divisions No of primes
10M: 4.650 seconds 1746210131 664579
20M: 12.585 seconds 4677014576 1270607 */
2B: projected time: at least 3 hours but likely much more (*)
I also changed the type of the number of divisions counter to long int because it was wrapping around the data type limit. So they could have been misinterpreting that.
But the run time wasn't being affected by that. A wall clock is a wall clock.
Most likely explanation seems to be a sloppy testing by the OP, with different values used in each test case, by mistake.
(*) The time projection was made by the empirical orders of growth analysis:
100**1.32 * 2.169 / 60 = 15.8
100**1.45 * 12.585 / 3600 = 2.8
Empirical orders of growth, as measured on the given range of sizes, were noticeably better for the list algorithm, n1.32 vs. the n1.45 for the testing by all numbers. This is entirely expected from theoretical complexity, since there are fewer primes than all numbers up to n, by a factor of log n, for a total complexity of O(n1.5/log n) vs. O(n1.5). It is also highly unlikely for any implementational discrepancy to beat an actual algorithmic advantage.
I have implemented a isPermutation function which given two string will return true if the two are permutation of each other, otherwise it will return false.
One uses c++ sort algorithm twice, while the other uses an array of ints to keep track of string count.
I ran the code several times and every time the sorting method is faster. Is my array implementation wrong?
Here is the output:
1
0
1
Time: 0.088 ms
1
0
1
Time: 0.014 ms
And the code:
#include <iostream> // cout
#include <string> // string
#include <cstring> // memset
#include <algorithm> // sort
#include <ctime> // clock_t
using namespace std;
#define MAX_CHAR 255
void PrintTimeDiff(clock_t start, clock_t end) {
std::cout << "Time: " << (end - start) / (double)(CLOCKS_PER_SEC / 1000) << " ms" << std::endl;
}
// using array to keep a count of used chars
bool isPermutation(string inputa, string inputb) {
int allChars[MAX_CHAR];
memset(allChars, 0, sizeof(int) * MAX_CHAR);
for(int i=0; i < inputa.size(); i++) {
allChars[(int)inputa[i]]++;
}
for (int i=0; i < inputb.size(); i++) {
allChars[(int)inputb[i]]--;
if(allChars[(int)inputb[i]] < 0) {
return false;
}
}
return true;
}
// using sorting anc comparing
bool isPermutation_sort(string inputa, string inputb) {
std::sort(inputa.begin(), inputa.end());
std::sort(inputb.begin(), inputb.end());
if(inputa == inputb) return true;
return false;
}
int main(int argc, char* argv[]) {
clock_t start = clock();
cout << isPermutation("god", "dog") << endl;
cout << isPermutation("thisisaratherlongerinput","thisisarathershorterinput") << endl;
cout << isPermutation("armen", "ramen") << endl;
PrintTimeDiff(start, clock());
start = clock();
cout << isPermutation_sort("god", "dog") << endl;
cout << isPermutation_sort("thisisaratherlongerinput","thisisarathershorterinput") << endl;
cout << isPermutation_sort("armen", "ramen") << endl;
PrintTimeDiff(start, clock());
return 0;
}
To benchmark this you have to eliminate all the noise you can.
The easiest way to do this is to wrap it in a loop that repeats the call to each 1000 times or so, then only spit out the value every 10 iterations. This way they each have a similar caching profile. Throw away values that are bogus due (eg blowouts due to context switches by the OS).
I got your method marginally faster by doing this. An excerpt.
method 1 array Time: 0.768 us
method 2 sort Time: 0.840333 us
method 1 array Time: 0.621333 us
method 2 sort Time: 0.774 us
method 1 array Time: 0.769 us
method 2 sort Time: 0.856333 us
method 1 array Time: 0.766 us
method 2 sort Time: 0.850333 us
method 1 array Time: 0.802667 us
method 2 sort Time: 0.89 us
method 1 array Time: 0.778 us
method 2 sort Time: 0.841333 us
I used rdtsc which works better for me on this system. 3000 cycles per microsecond is close enough for this, but please do make it more accurate if you care about precision of the readings.
#if defined(__x86_64__)
static uint64_t rdtsc()
{
uint64_t hi, lo;
__asm__ __volatile__ (
"xor %%eax, %%eax\n"
"cpuid\n"
"rdtsc\n"
: "=a"(lo), "=d"(hi)
:: "ebx", "ecx");
return (hi << 32)|lo;
}
#else
#error wrong architecture - implement me
#endif
void PrintTimeDiff(uint64_t start, uint64_t end) {
std::cout << "Time: " << (end - start)/double(3000) << " us" << std::endl;
}
you cannot check performance differences between implementations putting in the mix calls to std::cout. isPermutation and isPermutation_sort are some order of magnitude faster than a call to std::cout (and, anyway, prefer \n over std::endl).
for testing you have to activate compiler optimizations. Doing so the compiler will apply the loop-invariant code motion optimization and you'll probably get the same results for both implementations.
A more effective way of testing is:
int main()
{
const std::vector<std::string> bag
{
"god", "dog", "thisisaratherlongerinput", "thisisarathershorterinput",
"armen", "ramen"
};
static std::mt19937 engine;
std::uniform_int_distribution<std::size_t> rand(0, bag.size() - 1);
const unsigned stop = 1000000;
unsigned counter = 0;
std::clock_t start = std::clock();
for (unsigned i(0); i < stop; ++i)
counter += isPermutation(bag[rand(engine)], bag[rand(engine)]);
std::cout << counter << '\n';
PrintTimeDiff(start, clock());
counter = 0;
start = std::clock();
for (unsigned i(0); i < stop; ++i)
counter += isPermutation_sort(bag[rand(engine)], bag[rand(engine)]);
std::cout << counter << '\n';
PrintTimeDiff(start, clock());
return 0;
}
I have 2.4s for isPermutations_sort vs 2s for isPermutation (somewhat similar to Hal's results). Same with g++ and clang++.
Printing the value of counter has the double benefit of:
triggering the as-if rule (the compiler cannot remove the for loops);
allowing a first check of your implementations (the two values cannot be too distant).
There're some things you have to change in your implementation of isPermutation:
pass arguments as const references
bool isPermutation(const std::string &inputa, const std::string &inputb)
just this change brings the time down to 0.8s (of course you cannot do the same with isPermutation_sort).
you can use std::array and std::fill instead of memset (this is C++ :-)
avoid premature pessimization and prefer preincrement. Only use postincrement if you're going to use the original value
do not mix signed and unsigned value in the for loops (inputa.size() and i). i should be declared as std::size_t
even better, use the range based for loop.
So something like:
bool isPermutation(const std::string &inputa, const std::string &inputb)
{
std::array<int, MAX_CHAR> allChars;
allChars.fill(0);
for (auto c : inputa)
++allChars[(unsigned char)c];
for (auto c : inputb)
{
--allChars[(unsigned char)c];
if (allChars[(unsigned char)c] < 0)
return false;
}
return true;
}
Anyway both isPermutation and isPermutation_sort should have this preliminary check:
if (inputa.length() != inputb.length())
return false;
Now we are at 0.55s for isPermutation vs 1.1s for isPermutation_sort.
Last but not least consider std::is_permutation:
for (unsigned i(0); i < stop; ++i)
{
const std::string &s1(bag[rand(engine)]), &s2(bag[rand(engine)]);
counter += std::is_permutation(s1.begin(), s1.end(), s2.begin());
}
(0.6s)
EDIT
As observed in BeyelerStudios' comment Mersenne-Twister is too much in this case.
You can change the engine to a simpler one.:
static std::linear_congruential_engine<std::uint_fast32_t, 48271, 0, 2147483647> engine;
This further lowers the timings. Luckily the relative speeds remain the same.
Just to be sure I've also checked with a non random access scheme obtaining the same relative results.
Your idea amounts to using a Counting Sort on both strings, but with the comparison happening on the count array, rather than after writing out sorted strings.
It works well because a byte can only have one of 255 non-zero values. Zeroing 256B of memory, or even 4*256B, is pretty cheap, so it works well even for fairly short strings, where most of the count array isn't touched.
It should be fairly good for very long strings, at least in some cases. It's pretty heavily dependent on a good and a heavily pipelined L1 cache, because scattered increments to the count array produces scattered read-modify-writes. Repeated occurrences create a dependency chain of with a store-load round-trip in it. This is a big glass-jaw for this algorithm, on CPUs where many loads and stores can be in flight at once (with their latencies happening in parallel). Modern x86 CPUs should run it pretty well, since they can sustain a load + store every clock cycle.
The initial count of inputa compiles to a very tight loop:
.L15:
movsx rdx, BYTE PTR [rax]
add rax, 1
add DWORD PTR [rsp-120+rdx*4], 1
cmp rax, rcx
jne .L15
This brings us to the first major bug in your code: char can be signed or unsigned. In the x86-64 ABI, char is signed, so allChars[(int)inputa[i]]++; sign-extends it for use as an array index. (movsx instead of movzx). Your code will write outside the array bounds on non-ASCII characters that have their high bit set. So you should have written allChars[(unsigned char)inputa[i]]++;. Note that casting to (unsigned) doesn't give the result we want (see comments).
Note that clang makes much worse code (v3.7.1 and v3.8, both with -O3), with a function call to std::basic_string<...>::_M_leak_hard() inside the inner loop. (Leak as in leak a reference, I think.) #manlio's version doesn't have this problem, so I guess for (auto c : inputa) syntax helps clang figure out what's happening.
Also, using std::string when your callers have char[] forces them to construct a std::string. That's kind of silly, but it is helpful to be able to compare string lengths.
GNU libc's std::is_permutation uses a very different strategy:
First, it skips any common prefix that's identical without permutation in both strings.
Then, for each element in inputa:
count the occurrences of that element in inputb. Check that it matches the count in inputa.
There are a couple optimizations:
Only compare counts the first time an element is seen: find duplicates by searching from the beginning of inputa, and if the match position isn't the current position, we've already checked this element.
check that the match count in inputb is != 0 before counting matches in the rest of inputa.
This doesn't need any temporary storage, so it can work when the elements are large. (e.g. an array of int64_t, or an array of structs).
If there is a mismatch, this is likely to find it early, before doing as much work. There are probably a few cases of inputs where the counting version would take less time, but probably for most inputs the library algorithm is best.
std::is_permutation uses std::count, which should be implemented very well with SSE / AVX vectors. Unfortunately, it's auto-vectorized in a really stupid way by both gcc and clang. It unpacks bytes to 64bit integers before accumulating them in vector elements, to avoid overflow. So it spends most of its instructions shuffling data around, and is probably slower than a scalar implementation (which you'd get from compiling with -O2, or with -O3 -fno-tree-vectorize).
It could and should only do this every few iterations, so the inner loop of count can just be something like pcmpeqb / psubb, with a psadbw every 255 iterations. Or pcmpeqb / pmovmskb / popcnt / add, but that's slower.
Template specializations in the library could help a lot for std::count for 8, 16, and 32bit types whose equality can be checked with bitwise equality (integer ==).
I have a vector<bool> and I'd like to zero it out. I need the size to stay the same.
The normal approach is to iterate over all the elements and reset them. However, vector<bool> is a specially optimized container that, depending on implementation, may store only one bit per element. Is there a way to take advantage of this to clear the whole thing efficiently?
bitset, the fixed-length variant, has the set function. Does vector<bool> have something similar?
There seem to be a lot of guesses but very few facts in the answers that have been posted so far, so perhaps it would be worthwhile to do a little testing.
#include <vector>
#include <iostream>
#include <time.h>
int seed(std::vector<bool> &b) {
srand(1);
for (int i = 0; i < b.size(); i++)
b[i] = ((rand() & 1) != 0);
int count = 0;
for (int i = 0; i < b.size(); i++)
if (b[i])
++count;
return count;
}
int main() {
std::vector<bool> bools(1024 * 1024 * 32);
int count1= seed(bools);
clock_t start = clock();
bools.assign(bools.size(), false);
double using_assign = double(clock() - start) / CLOCKS_PER_SEC;
int count2 = seed(bools);
start = clock();
for (int i = 0; i < bools.size(); i++)
bools[i] = false;
double using_loop = double(clock() - start) / CLOCKS_PER_SEC;
int count3 = seed(bools);
start = clock();
size_t size = bools.size();
bools.clear();
bools.resize(size);
double using_clear = double(clock() - start) / CLOCKS_PER_SEC;
int count4 = seed(bools);
start = clock();
std::fill(bools.begin(), bools.end(), false);
double using_fill = double(clock() - start) / CLOCKS_PER_SEC;
std::cout << "Time using assign: " << using_assign << "\n";
std::cout << "Time using loop: " << using_loop << "\n";
std::cout << "Time using clear: " << using_clear << "\n";
std::cout << "Time using fill: " << using_fill << "\n";
std::cout << "Ignore: " << count1 << "\t" << count2 << "\t" << count3 << "\t" << count4 << "\n";
}
So this creates a vector, sets some randomly selected bits in it, counts them, and clears them (and repeats). The setting/counting/printing is done to ensure that even with aggressive optimization, the compiler can't/won't optimize out our code to clear the vector.
I found the results interesting, to say the least. First the result with VC++:
Time using assign: 0.141
Time using loop: 0.068
Time using clear: 0.141
Time using fill: 0.087
Ignore: 16777216 16777216 16777216 16777216
So, with VC++, the fastest method is what you'd probably initially think of as the most naive -- a loop that assigns to each individual item. With g++, the results are just a tad different though:
Time using assign: 0.002
Time using loop: 0.08
Time using clear: 0.002
Time using fill: 0.001
Ignore: 16777216 16777216 16777216 16777216
Here, the loop is (by far) the slowest method (and the others are basically tied -- the 1 ms difference in speed isn't really repeatable).
For what it's worth, in spite of this part of the test showing up as much faster with g++, the overall times were within 1% of each other (4.944 seconds for VC++, 4.915 seconds for g++).
Try
v.assign(v.size(), false);
Have a look at this link:
http://www.cplusplus.com/reference/vector/vector/assign/
Or the following
std::fill(v.begin(), v.end(), 0)
You are out of luck. std::vector<bool> is a specialization that apparently does not even guarantee contiguous memory or random access iterators (or even forward?!), at least based on my reading of cppreference -- decoding the standard would be the next step.
So write implementation specific code, pray and use some standard zeroing technique, or do not use the type. I vote 3.
The recieved wisdom is that it was a mistake, and may become deprecated. Use a different container if possible. And definitely do not mess around with the internal guts, or rely on its packing. Check if you have dynamic bitset in your std library mayhap, or roll your own wrapper around std::vector<unsigned char>.
I ran into this as a performance issue recently. I hadn't tried looking for answers on the web but did find that using assignment with the constructor was 10x faster using g++ O3 (Debian 4.7.2-5) 4.7.2. I found this question because I was looking to avoid the additional malloc. Looks like the assign is optimized as well as the constructor and about twice as good in my benchmark.
unsigned sz = v.size(); for (unsigned ii = 0; ii != sz; ++ii) v[ii] = false;
v = std::vector(sz, false); // 10x faster
v.assign(sz, false); > // 20x faster
So, I wouldn't say to shy away from using the specialization of vector<bool>; just be very cognizant of the bit vector representation.
Use the std::vector<bool>::assign method, which is provided for this purpose.
If an implementation is specific for bool, then assign, most likely, also implemented appropriately.
If you're able to switch from vector<bool> to a custom bit vector representation, then you can use a representation designed specifically for fast clear operations, and get some potentially quite significant speedups (although not without tradeoffs).
The trick is to use integers per bit vector entry and a single 'rolling threshold' value that determines which entries actually then evaluate to true.
You can then clear the bit vector by just increasing the single threshold value, without touching the rest of the data (until the threshold overflows).
A more complete write up about this, and some example code, can be found here.
It seems that one nice option hasn't been mentioned yet:
auto size = v.size();
v.resize(0);
v.resize(size);
The STL implementer will supposedly have picked the most efficient means of zeroising, so we don't even need to know which particular method that might be. And this works with real vectors as well (think templates), not just the std::vector<bool> monstrosity.
There can be a minuscule added advantage for reused buffers in loops (e.g. sieves, whatever), where you simply resize to whatever will be needed for the current round, instead of to the original size.
As an alternative to std::vector<bool>, check out boost::dynamic_bitset (https://www.boost.org/doc/libs/1_72_0/libs/dynamic_bitset/dynamic_bitset.html). You can zero one (ie, set each element to false) out by calling the reset() member function.
Like clearing, say, std::vector<int>, reset on a boost::dynamic_bitset can also compile down to a memset, whereas you probably won't get that with std::vector<bool>. For example, see https://godbolt.org/z/aqSGCi
I believe (from some research reading) that counting down in for-loops is actually more efficient and faster in runtime. My full software code is C++
I currently have this:
for (i=0; i<domain; ++i) {
my 'i' is unsigned resgister int,
also 'domain' is unsigned int
in the for-loop i is used for going through an array, e.g.
array[i] = do stuff
converting this to count down messes up the expected/correct output of my routine.
I can imagine the answer being quite trivial, but I can't get my head round it.
UPDATE: 'do stuff' does not depend on previous or later iteration. The calculations within the for-loop are independant for that iteration of i. (I hope that makes sense).
UPDATE: To achieve a runtime speedup with my for-loop, do I count down and if so remove the unsigned part when delcaring my int, or what other method?
Please help.
There is only one correct method of looping backwards using an unsigned counter:
for( i = n; i-- > 0; )
{
// Use i as normal here
}
There's a trick here, for the last loop iteration you will have i = 1 at the top of the loop, i-- > 0 passes because 1 > 0, then i = 0 in the loop body. On the next iteration i-- > 0 fails because i == 0, so it doesn't matter that the postfix decrement rolled over the counter.
Very non obvious I know.
I'm guessing your backward for loop looks like this:
for (i = domain - 1; i >= 0; --i) {
In that case, because i is unsigned, it will always be greater than or equal to zero. When you decrement an unsigned variable that is equal to zero, it will wrap around to a very large number. The solution is either to make i signed, or change the condition in the for loop like this:
for (i = domain - 1; i >= 0 && i < domain; --i) {
Or count from domain to 1 rather than from domain - 1 to 0:
for (i = domain; i >= 1; --i) {
array[i - 1] = ...; // notice you have to subtract 1 from i inside the loop now
}
This is not an answer to your problem, because you don't seem to have a problem.
This kind of optimization is completely irrelevant and should be left to the compiler (if done at all).
Have you profiled your program to check that your for-loop is a bottleneck? If not, then you do not need to spend time worrying about this. Even more so, having "i" as a "register" int, as you write, makes no real sense from a performance standpoint.
Even without knowing your problem domain, I can guarantee you that both the reverse-looping technique and the "register" int counter will have negligible impact on your program's performance. Remember, "Premature optimization is the root of all evil".
That said, better spent optimization time would be on thinking about the overall program structure, data structures and algorithms used, resource utilization, etc.
Checking to see if a number is zero can be quicker or more efficient than a comparison. But this is the sort of micro-optimization you really shouldn't worry about - a few clock cycles will be greatly dwarfed by just about any other perf issue.
On x86:
dec eax
jnz Foo
Instead of:
inc eax
cmp eax, 15
jl Foo
It has nothing to do with counting up or down. What can be faster is counting toward zero. Michael's answer shows why — x86 gives you a comparison with zero as an implicit side effect of many instructions, so after you adjust your counter, you just branch based on the result instead of doing an explicit comparison. (Maybe other architectures do that, too; I don't know.)
Borland's Pascal compilers are notorious for performing that optimization. The compiler transforms this code:
for i := x to y do
foo(i);
into an internal representation more akin to this:
tmp := Succ(y - x);
i := x;
while tmp > 0 do begin
foo(i);
Inc(i);
Dec(tmp);
end;
(I say notorious not because the optimization affects the outcome of the loop, but because the debugger displays the counter variable incorrectly. When the programmer inspects i, the debugger may display the value of tmp instead, causing no end of confusion and panic for programmers who think their loops are running backward.)
The idea is that even with the extra Inc or Dec instruction, it's still a net win, in terms of running time, over doing an explicit comparison. Whether you can actually notice that difference is up for debate.
But note that the conversion is something the compiler would do automatically, based on whether it deemed the transformation worthwhile. The compiler is usually better at optimizing code than you are, so don't spend too much effort competing with it.
Anyway, you asked about C++, not Pascal. C++ "for" loops aren't quite as easy to apply that optimization to as Pascal "for" loops are because the bounds of Pascal's loops are always fully calculated before the loop runs, whereas C++ loops sometimes depend on the stopping condition and the loop contents. C++ compilers need to do some amount of static analysis to determine whether any given loop could fit the requirements for the kind of transformation Pascal loops qualify for unconditionally. If the C++ compiler does the analysis, then it could do a similar transformation.
There's nothing stopping you from writing your loops that way on your own:
for (unsigned i = 0, tmp = domain; tmp > 0; ++i, --tmp)
array[i] = do stuff
Doing that might make your code run faster. Like I said before, though, you probably won't notice. The bigger cost you pay by manually arranging your loops like that is that your code no longer follows established idioms. Your loop is a perfectly ordinary "for" loop, but it no longer looks like one — it has two variables, they're counting in opposite directions, and one of them isn't even used in the loop body — so anyone reading your code (including you, a week, a month, or a year from now when you've forgotten the "optimization" you were hoping to achieve) will need to spend extra effort proving to himself or herself that the loop is indeed an ordinary loop in disguise.
(Did you notice that my code above used unsigned variables with no danger of wrapping around at zero? Using two separate variables allows that.)
Three things to take away from all this:
Let the optimizer do its job; on the whole it's better at it than you are.
Make ordinary code look ordinary so that the special code doesn't have to compete to get attention from people reviewing, debugging, or maintaining it.
Don't do anything fancy in the name of performance until testing and profiling show it to be necessary.
If you have a decent compiler, it will optimize "counting up" just as effectively as "counting down". Just try a few benchmarks and you'll see.
So you "read" that couting down is more efficient? I find this very difficult to believe unless you show me some profiler results and the code. I can buy it under some circumstances, but in the general case, no. Seems to me like this is a classic case of premature optimization.
Your comment about "register int i" is also very telling. Nowadays, the compiler always knows better than you how to allocate registers. Don't bother using using the register keyword unless you have profiled your code.
When you're looping through data structures of any sort, cache misses have a far bigger impact than the direction you're going. Concern yourself with the bigger picture of memory layout and algorithm structure instead of trivial micro-optimisations.
You may try the following, which compiler will optimize very efficiently:
#define for_range(_type, _param, _A1, _B1) \
for (_type _param = _A1, _finish = _B1,\
_step = static_cast<_type>(2*(((int)_finish)>(int)_param)-1),\
_stop = static_cast<_type>(((int)_finish)+(int)_step); _param != _stop; \
_param = static_cast<_type>(((int)_param)+(int)_step))
Now you can use it:
for_range (unsigned, i, 10,0)
{
cout << "backwards i: " << i << endl;
}
for_range (char, c, 'z','a')
{
cout << c << endl;
}
enum Count { zero, one, two, three };
for_range (Count, c, three, zero)
{
cout << "backwards: " << c << endl;
}
You may iterate in any direction:
for_range (Count, c, zero, three)
{
cout << "forward: " << c << endl;
}
The loop
for_range (unsigned,i,b,a)
{
// body of the loop
}
will produce the following code:
mov esi,b
L1:
; body of the loop
dec esi
cmp esi,a-1
jne L1
Hard to say with information given but... reverse your array, and count down?
Jeremy Ruten rightly pointed out that using an unsigned loop counter is dangerous. It's also unnecessary, as far as I can tell.
Others have also pointed out the dangers of premature optimization. They're absolutely right.
With that said, here is a style I used when programming embedded systems many years ago, when every byte and every cycle did count for something. These forms were useful for me on the particular CPUs and compilers that I was using, but your mileage may vary.
// Start out pointing to the last elem in array
pointer_to_array_elem_type p = array + (domain - 1);
for (int i = domain - 1; --i >= 0 ; ) {
*p-- = (... whatever ...)
}
This form takes advantage of the condition flag that is set on some processors after arithmetical operations -- on some architectures, the decrement and testing for the branch condition can be combined into a single instruction. Note that using predecrement (--i) is the key here -- using postdecrement (i--) would not have worked as well.
Alternatively,
// Start out pointing *beyond* the last elem in array
pointer_to_array_elem_type p = array + domain;
for (pointer_to_array_type p = array + domain; p - domain > 0 ; ) {
*(--p) = (... whatever ...)
}
This second form takes advantage of pointer (address) arithmetic. I rarely see the form (pointer - int) these days (for good reason), but the language guarantees that when you subtract an int from a pointer, the pointer is decremented by (int * sizeof (*pointer)).
I'll emphasize again that whether these forms are a win for you depends on the CPU and compiler that you're using. They served me well on Motorola 6809 and 68000 architectures.
In some later arm cores, decrement and compare takes only a single instruction. This makes decrementing loops more efficient than incrementing ones.
I don't know why there isn't an increment-compare instruction also.
I'm surprised that this post was voted -1 when it's a true issue.
Everyone here is focusing on performance. There is actually a logical reason to iterate towards zero that can result in cleaner code.
Iterating over the last element first is convenient when you delete invalid elements by swapping with the end of the array. For bad elements not adjacent to the end we can swap into the end position, decrease the end bound of the array, and keep iterating. If you were to iterate toward the end then swapping with the end could result in swapping bad for bad. By iterating end to 0 we know that the element at the end of the array has already been proven valid for this iteration.
For further explanation...
If:
You delete bad elements by swapping with one end of the array and changing the array bounds to exclude the bad elements.
Then obviously:
You would swap with a good element i.e. one that has already been tested in this iteration.
So this implies:
If we iterate away from the variable bound then elements between the variable bound and the current iteration pointer have been proven good. Whether the iteration pointer gets ++ or -- doesn't matter. What matters is that we're iterating away from the variable bound so we know that the elements adjacent to it are good.
So finally:
Iterating towards 0 allows us to use only one variable to represent the array bounds. Whether this matters is a personal decision between you and your compiler.
What matters much more than whether you're increasing or decreasing your counter is whether or not you're going up memory or down memory. Most caches are optimized for going up memory, not down memory. Since memory access time is the bottleneck that most programs today face, this means that changing your program so that you go up memory can result in a performance boost even if this requires comparing your counter to a non-zero value. In some of my programs, I saw a significant improvement in performance by changing my code to go up memory instead of down it.
Skeptical? Here's the output that I got:
sum up = 705046256
sum down = 705046256
Ave. Up Memory = 4839 mus
Ave. Down Memory = 5552 mus
sum up = inf
sum down = inf
Ave. Up Memory = 18638 mus
Ave. Down Memory = 19053 mus
from running this program:
#include <chrono>
#include <iostream>
#include <random>
#include <vector>
template<class Iterator, typename T>
void FillWithRandomNumbers(Iterator start, Iterator one_past_end, T a, T b) {
std::random_device rnd_device;
std::mt19937 generator(rnd_device());
std::uniform_int_distribution<T> dist(a, b);
for (auto it = start; it != one_past_end; it++)
*it = dist(generator);
return ;
}
template<class Iterator>
void FillWithRandomNumbers(Iterator start, Iterator one_past_end, double a, double b) {
std::random_device rnd_device;
std::mt19937_64 generator(rnd_device());
std::uniform_real_distribution<double> dist(a, b);
for (auto it = start; it != one_past_end; it++)
*it = dist(generator);
return ;
}
template<class RAI, class T>
inline void sum_abs_up(RAI first, RAI one_past_last, T &total) {
T sum = 0;
auto it = first;
do {
sum += *it;
it++;
} while (it != one_past_last);
total += sum;
}
template<class RAI, class T>
inline void sum_abs_down(RAI first, RAI one_past_last, T &total) {
T sum = 0;
auto it = one_past_last;
do {
it--;
sum += *it;
} while (it != first);
total += sum;
}
template<class T> std::chrono::nanoseconds TimeDown(
std::vector<T> &vec, const std::vector<T> &vec_original,
std::size_t num_repititions, T &running_sum) {
std::chrono::nanoseconds total{0};
for (std::size_t i = 0; i < num_repititions; i++) {
auto start_time = std::chrono::high_resolution_clock::now();
sum_abs_down(vec.begin(), vec.end(), running_sum);
total += std::chrono::high_resolution_clock::now() - start_time;
vec = vec_original;
}
return total;
}
template<class T> std::chrono::nanoseconds TimeUp(
std::vector<T> &vec, const std::vector<T> &vec_original,
std::size_t num_repititions, T &running_sum) {
std::chrono::nanoseconds total{0};
for (std::size_t i = 0; i < num_repititions; i++) {
auto start_time = std::chrono::high_resolution_clock::now();
sum_abs_up(vec.begin(), vec.end(), running_sum);
total += std::chrono::high_resolution_clock::now() - start_time;
vec = vec_original;
}
return total;
}
int main() {
std::size_t num_repititions = 1 << 10;
{
typedef int ValueType;
auto lower = std::numeric_limits<ValueType>::min();
auto upper = std::numeric_limits<ValueType>::max();
std::vector<ValueType> vec(1 << 24);
FillWithRandomNumbers(vec.begin(), vec.end(), lower, upper);
const auto vec_original = vec;
ValueType sum_up = 0, sum_down = 0;
auto time_up = TimeUp(vec, vec_original, num_repititions, sum_up).count();
auto time_down = TimeDown(vec, vec_original, num_repititions, sum_down).count();
std::cout << "sum up = " << sum_up << '\n';
std::cout << "sum down = " << sum_down << '\n';
std::cout << "Ave. Up Memory = " << time_up/(num_repititions * 1000) << " mus\n";
std::cout << "Ave. Down Memory = "<< time_down/(num_repititions * 1000) << " mus"
<< std::endl;
}
{
typedef double ValueType;
auto lower = std::numeric_limits<ValueType>::min();
auto upper = std::numeric_limits<ValueType>::max();
std::vector<ValueType> vec(1 << 24);
FillWithRandomNumbers(vec.begin(), vec.end(), lower, upper);
const auto vec_original = vec;
ValueType sum_up = 0, sum_down = 0;
auto time_up = TimeUp(vec, vec_original, num_repititions, sum_up).count();
auto time_down = TimeDown(vec, vec_original, num_repititions, sum_down).count();
std::cout << "sum up = " << sum_up << '\n';
std::cout << "sum down = " << sum_down << '\n';
std::cout << "Ave. Up Memory = " << time_up/(num_repititions * 1000) << " mus\n";
std::cout << "Ave. Down Memory = "<< time_down/(num_repititions * 1000) << " mus"
<< std::endl;
}
return 0;
}
Both sum_abs_up and sum_abs_down do the same thing and are timed they same way with the only difference being that sum_abs_up goes up memory while sum_abs_down goes down memory. I even pass vec by reference so that both functions access the same memory locations. Nevertheless, sum_abs_up is consistently faster than sum_abs_down. Give it a run yourself (I compiled it with g++ -O3).
FYI vec_original is there for experimentation, to make it easy for me to change sum_abs_up and sum_abs_down in a way that makes them alter vec while not allowing these changes to affect future timings.
It's important to note how tight the loop that I'm timing is. If a loop's body is large then it likely won't matter whether its iterator goes up or down memory since the time it takes to execute the loop's body will likely completely dominate. Also, it's important to mention that with some rare loops, going down memory is sometimes faster than going up it. But even with such loops it's rarely ever the case that going up was always slower than going down (unlike loops that go up memory, which are very often always faster than the equivalent down-memory loops; a small handful of times they were even 40+% faster).
The point is, as a rule of thumb, if you have the option, if the loop's body is small, and if there's little difference between having your loop go up memory instead of down it, then you should go up memory.