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Efficiency of & 1 over % 2 [duplicate]

What is the fastest way to find if a number is even or odd?

It is pretty well known that
static inline int is_odd_A(int x) { return x & 1; }
is more efficient than
static inline int is_odd_B(int x) { return x % 2; }
But with the optimizer on, will is_odd_B be no different from is_odd_A? No — with gcc-4.2 -O2, we get, (in ARM assembly):
_is_odd_A:
and r0, r0, #1
bx lr
_is_odd_B:
mov r3, r0, lsr #31
add r0, r0, r3
and r0, r0, #1
rsb r0, r3, r0
bx lr
We see that is_odd_B takes 3 more instructions than is_odd_A, the main reason is because
((-1) % 2) == -1
((-1) & 1) == 1
However, all the following versions will generate the same code as is_odd_A:
#include <stdbool.h>
static inline bool is_odd_D(int x) { return x % 2; } // note the bool
static inline int is_odd_E(int x) { return x % 2 != 0; } // note the !=
What does this mean? The optimizer is usually sophisticated enough that, for these simple stuff, the clearest code is enough to guarantee best efficiency.

Usual way to do it:
int number = ...;
if(number % 2) { odd }
else { even }
Alternative:
int number = ...;
if(number & 1) { odd }
else { even }
Tested on GCC 3.3.1 and 4.3.2, both have about the same speed (without compiler optimization) as both result in the and instruction (compiled on x86) - I know that using the div instruction for modulo would be much slower, thus I didn't test it at all.

if (x & 1) is true then it's odd, otherwise it's even.

bool is_odd = number & 1;

int i=5;
if ( i%2 == 0 )
{
// Even
} else {
// Odd
}

int is_odd(int n)
{
if (n == 0)
return 0;
else if (n == 1)
return 1;
else
return !is_odd(n - 1);
}
Oh wait, you said fastest way, not funniest. My bad ;)
Above function only works for positive numbers of course.

Check to see if the last bit is 1.
int is_odd(int num) {
return num & 1;
}

If it's an integer, probably by just checking the least significant bit. Zero would be counted as even though.

The portable way is to use the modulus operator %:
if (x % 2 == 0) // number is even
If you know that you're only ever going to run on two's complement architectures, you can use a bitwise and:
if (x & 0x01 == 0) // number is even
Using the modulus operator can result in slower code relative to the bitwise and; however, I'd stick with it unless all of the following are true:
You are failing to meet a hard performance requirement;
You are executing x % 2 a lot (say in a tight loop that's being executed thousands of times);
Profiling indicates that usage of the mod operator is the bottleneck;
Profiling also indicates that using the bitwise-and relieves the bottleneck and allows you to meet the performance requirement.

Your question is not completely specified. Regardless, the answer is dependent on your compiler and the architecture of your machine. For example, are you on a machine using one's complement or two's complement signed number representations?
I write my code to be correct first, clear second, concise third and fast last. Therefore, I would code this routine as follows:
/* returns 0 if odd, 1 if even */
/* can use bool in C99 */
int IsEven(int n) {
return n % 2 == 0;
}
This method is correct, it more clearly expresses the intent than testing the LSB, it's concise and, believe it or not, it is blazing fast. If and only if profiling told me that this method were a bottleneck in my application would I consider deviating from it.

Check the least significant bit:
if (number & 0x01) {
// It's odd
} else {
// It's even
}

Can't you just look at the last digit and check if its even or odd if the input is in base 10?
{1, 3, 5, 7, 9} is odd
{0, 2, 4, 6, 8} is even
Additional info: The OP states that a number is a given, so I went with that when constructing this answer. This also requires the number to be in base 10. This answer is mathematically correct by definition of even/odd in base 10. Depending on the use case, you have a mathematically consistent result just by checking the last digit.
Note: If your input is already an int, just check the low bit of that. This answer is only useful for numbers represented as a sequence of digits. You could convert int->string to do this, but that would be much slower than n % 2 == 0.
Checking the last digit does work for a string of digits in any even base, not just 10. For bases lower than 10, like base 8 (octal), 9 and 8 aren't possible digits, but the low digit being odd or even still determines whether the whole number is.
For bases higher than 10, there will be extra possibilities, but you don't want to search a list anyway, just check if the digit as an integer is odd or even using the normal i % 2 == 0 or !=0 check.
For ASCII hex using 'a' .. 'f' to represent digits values 10 through 15, the low bit of ASCII code does not represent odd or even, because 'a' == 0x61 (odd) but represents 10 aka 0xa (even). So you'd have to convert the hex digit to an integer, or do some bit-hack on the ASCII code to flip the low bit according to some other bit or condition.

What does the << mean?

Thanks for taking a look at this question.
I saw the following piece of code inside a traditional for block, but was not sure what its significance was inside its context.
index <<= 1;
For further context, here is the full block of code.
ulong index = 1;
int distance = 0;
for (int i = 0; i < 64; i++)
{
if ((hash1 & index) != (hash2 & index))
{
distance++;
}
index <<= 1;
}
Is it simply making sure that index is still 1 and if it isn't, return it's value to 1?
Secondly, what is this called so I can read up on it some more.
Finally, Thank you for your time and consideration for this matter.

The code in question is spinning through a pair of 64-bit hashes (probably as ulongs, like the index), and checking how many bits differ between them. I'm going to use 4-bit values for example purposes, but the principle is the same.
if ((hash1 & index) != (hash2 & index))
The & operator is doing a bitwise-AND operation. When the hash is ANDed with the index value, you get either 0 or the index value back, depending on whether that specific bit was 0 or 1. (1010 & 0010 == 0010 and 1010 & 0100 == 0000).
If both ANDs produce a 0, or both produce the index value, then the two bits of the hash match. Otherwise, they don't, and we distance++; to indicate that they are off by one more bit than we knew of before.
index <<= 1;
This line merely bumps the index digit to the next bit. It does this by taking the old index (which starts as 1, equal to 0001), and left shifting by one place (<< 1), then setting that back into the index variable (<<= instead of <<). So after the first loop, index will be 0010, then 0100, and so on.
This has the effect of multiplying by 2, but that's not its intended use here.
So overall, you'd get a distance of 2 by running 0011 and 1111 through this algorithm, because two bits are different.

The code
index <<= 1;
Is a left shift by one bit. It has the same effect in this case as multiplying by two. But see comments for cautions.

Optimisation of threshold computation

I'm trying to optimise the following C# code, which sets bytes to 0x00 or 0xFF based on a threshold.
for (int i = 0; i < veryLargeNumber; i++)
{
data[i] = (byte)(data[i] < threshold ? 0 : 255);
}
Visual Studio's performance profiler shows that the above code is rather expensive, taking nearly 8 seconds to compute - 98% of my total processing expense. I'm processing just under a thousand items, so that adds up to over two hours.
I think the issue is to do with the ternary conditional operator, since it causes a branch. I'd imagine a pure-math operation of some sort could be significantly faster, since it's CPU-cache friendly.
Is there a way to optimise this? It's possible for me to fix the threshold value, if that helps. I'd consider anything above a ~7% performance increase a win, since that's a whole 10 minutes shaved off the total processing time.

If you are using .NET 4.0 Framework, you could make use of Parallel Library in following link,
http://msdn.microsoft.com/en-us/library/dd460717
In Your case, you must have to verify the threshold, anyway it would take time. So make use of thread or lambda expressions

Just to suggest, use bitwise operators for this purpose because they are faster, together with parallel approach.
0x00 = 0000 0000
0xFF = 1111 1111
Try with OR operator(i.e. 0 | 1 = 1 where | stands for OR operator
EDIT:
This is how you could compare which number is bigger:
let a,b be numbers:
int temp= a ^ b;
temp|= temp>> 1;
temp|= temp>> 2;
temp|= temp>> 4;
temp|= temp>> 8;
temp|= temp>> 16;
temp&= ~(temp>> 1) | 0x80000000;
temp&= (a ^ 0x80000000) & (b ^ 0x7fffffff);

If you want a bit-wise solution -
int intSize = sizeof(int) * 8 - 1;
byte t = (byte)(threshold - 1);
for (....)
{
data[i] = (byte)(255 + 1 ^ ((t - data[i]) >> intSize));
}
Note: Wont work for corner case of 0. Sorry bout that
Also, try using an int array instead of byte and see if it is faster

Generating uniform random integers with a certain maximum

I want to generate uniform integers that satisfy 0 <= result <= maxValue.
I already have a generator that returns uniform values in the full range of the built in unsigned integer types. Let's call the methods for this byte Byte(), ushort UInt16(), uint UInt32() and ulong UInt64(). Assume that the result of these methods is perfectly uniform.
The signature of the methods I want are uint UniformUInt(uint maxValue) and ulong UniformUInt(ulong maxValue).
What I'm looking for:
Correctness
I'd prefer the return values to be distributed in the given interval.
But a very small bias is acceptable if it increases performance significantly. By that I mean a bias of an order that allows distinguisher with probability 2/3 given 2^64 values.
It must work correctly for any maxValue.
Performance
The method should be fast.
Efficiency
The method does consume little raw randomness, since depending on the underlying generator, generating the raw bytes might be costly. Wasting a few bits is fine, but consuming say 128 bits to generate a single number is probably excessive.
It's also possible to cache some left over randomness from the previous call in some member variables.
Be careful with int overflows, and wrapping behavior.
I already have a solution(I'll post it as an answer), but it's a bit ugly for my tastes. So I'd like to get ideas for better solutions.
Suggestions on how to unit test with large maxValues would be nice too, since I can't generate a histogram with 2^64 buckets and 2^74 random values. Another complication is that with certain bugs, only some maxValue distributions are biased a lot, and others only very slightly.

How about something like this as a general-purpose solution? The algorithm is based on that used by Java's nextInt method, rejecting any values that would cause a non-uniform distribution. So long as the output of your UInt32 method is perfectly uniform then this should be too.
uint UniformUInt(uint inclusiveMaxValue)
{
unchecked
{
uint exclusiveMaxValue = inclusiveMaxValue + 1;
// if exclusiveMaxValue is a power of two then we can just use a mask
// also handles the edge case where inclusiveMaxValue is uint.MaxValue
if ((exclusiveMaxValue & (~exclusiveMaxValue + 1)) == exclusiveMaxValue)
return UInt32() & inclusiveMaxValue;
uint bits, val;
do
{
bits = UInt32();
val = bits % exclusiveMaxValue;
// if (bits - val + inclusiveMaxValue) overflows then val has been
// taken from an incomplete chunk at the end of the range of bits
// in that case we reject it and loop again
} while (bits - val + inclusiveMaxValue < inclusiveMaxValue);
return val;
}
}
The rejection process could, theoretically, keep looping forever; in practice the performance should be pretty good. It's difficult to suggest any generally applicable optimisations without knowing (a) the expected usage patterns, and (b) the performance characteristics of your underlying RNG.
For example, if most callers will be specifying a max value <= 255 then it might not make sense to ask for four bytes of randomness every time. On the other hand, the performance benefit of requesting fewer bytes might be outweighed by the additional cost of always checking how many you actually need. (And, of course, once you do have specific information then you can keep optimising and testing until your results are good enough.)

I am not sure, that his is an answer. It definitly needs more space than a comment, so I have to write it here, but I am willing to delete if others think this is stupid.
From the OQ I get, that
Entropy bits are very expensive
Everything else should be considered expensive, but less so than entropy.
My idea is to use binary digits to half, quater ... the maxValue space, until it is reduced to a number. Somthing like
I'l use maxValue=333 (decimal) as an example and assume a function getBit(), that randomly returns 0 or 1
offset:=0
space:=maxValue
while (space>0)
//Right-shift the value, keeping the rightmost bit this should be
//efficient on x86 and x64, if coded in real code, not pseudocode
remains:=space & 1
part:=floor(space/2)
space:=part
//In the 333 example, part is now 166, but 2*166=332 If we were to simply chose one
//half of the space, we would be heavily biased towards the upper half, so in case
//we have a remains, we consume a bit of entropy to decide which half is bigger
if (remains)
if(getBit())
part++;
//Now we decide which half to chose, consuming a bit of entropy
if (getBit())
offset+=part;
//Exit condition: The remeinind number space=0 is guaranteed to be met
//In the 333 example, offset will be 0, 166 or 167, remaining space will be 166
}
randomResult:=offset
getBit() can either come from your entropy source, if it is bit-based, or by consuming n bits of entropy at once on first call (obviously with n being the optimum for your entropy source), and shifting this until empty.

My current solution. A bit ugly for my tastes. It also has two divisions per generated number, which might negatively impact performance (I haven't profiled this part yet).
uint UniformUInt(uint maxResult)
{
uint rand;
uint count = maxResult + 1;
if (maxResult < 0x100)
{
uint usefulCount = (0x100 / count) * count;
do
{
rand = Byte();
} while (rand >= usefulCount);
return rand % count;
}
else if (maxResult < 0x10000)
{
uint usefulCount = (0x10000 / count) * count;
do
{
rand = UInt16();
} while (rand >= usefulCount);
return rand % count;
}
else if (maxResult != uint.MaxValue)
{
uint usefulCount = (uint.MaxValue / count) * count;//reduces upper bound by 1, to avoid long division
do
{
rand = UInt32();
} while (rand >= usefulCount);
return rand % count;
}
else
{
return UInt32();
}
}
ulong UniformUInt(ulong maxResult)
{
if (maxResult < 0x100000000)
return InternalUniformUInt((uint)maxResult);
else if (maxResult < ulong.MaxValue)
{
ulong rand;
ulong count = maxResult + 1;
ulong usefulCount = (ulong.MaxValue / count) * count;//reduces upper bound by 1, since ulong can't represent any more
do
{
rand = UInt64();
} while (rand >= usefulCount);
return rand % count;
}
else
return UInt64();
}

Project Euler #16 - C# 2.0

I've been wrestling with Project Euler Problem #16 in C# 2.0. The crux of the question is that you have to calculate and then iterate through each digit in a number that is 604 digits long (or there-abouts). You then add up these digits to produce the answer.
This presents a problem: C# 2.0 doesn't have a built-in datatype that can handle this sort of calculation precision. I could use a 3rd party library, but that would defeat the purpose of attempting to solve it programmatically without external libraries. I can solve it in Perl; but I'm trying to solve it in C# 2.0 (I'll attempt to use C# 3.0 in my next run-through of the Project Euler questions).
Question
What suggestions (not answers!) do you have for solving project Euler #16 in C# 2.0? What methods would work?
NB: If you decide to post an answer, please prefix your attempt with a blockquote that has ###Spoiler written before it.

A number of a series of digits. A 32 bit unsigned int is 32 binary digits. The string "12345" is a series of 5 digits. Digits can be stored in many ways: as bits, characters, array elements and so on. The largest "native" datatype in C# with complete precision is probably the decimal type (128 bits, 28-29 digits). Just choose your own method of storing digits that allows you to store much bigger numbers.
As for the rest, this will give you a clue:
21 = 2
22 = 21 + 21
23 = 22 + 22
Example:
The sum of digits of 2^100000 is 135178
Ran in 4875 ms
The sum of digits of 2^10000 is 13561
Ran in 51 ms
The sum of digits of 2^1000 is 1366
Ran in 2 ms
SPOILER ALERT: Algorithm and solution in C# follows.
Basically, as alluded to a number is nothing more than an array of digits. This can be represented easily in two ways:
As a string;
As an array of characters or digits.
As others have mentioned, storing the digits in reverse order is actually advisable. It makes the calculations much easier. I tried both of the above methods. I found strings and the character arithmetic irritating (it's easier in C/C++; the syntax is just plain annoying in C#).
The first thing to note is that you can do this with one array. You don't need to allocate more storage at each iteration. As mentioned you can find a power of 2 by doubling the previous power of 2. So you can find 21000 by doubling 1 one thousand times. The doubling can be done in place with the general algorithm:
carry = 0
foreach digit in array
sum = digit + digit + carry
if sum > 10 then
carry = 1
sum -= 10
else
carry = 0
end if
digit = sum
end foreach
This algorithm is basically the same for using a string or an array. At the end you just add up the digits. A naive implementation might add the results into a new array or string with each iteration. Bad idea. Really slows it down. As mentioned, it can be done in place.
But how large should the array be? Well that's easy too. Mathematically you can convert 2^a to 10^f(a) where f(a) is a simple logarithmic conversion and the number of digits you need is the next higher integer from that power of 10. For simplicity, you can just use:
digits required = ceil(power of 2 / 3)
which is a close approximation and sufficient.
Where you can really optimise this is by using larger digits. A 32 bit signed int can store a number between +/- 2 billion (approximately. Well 9 digits equals a billion so you can use a 32 bit int (signed or unsigned) as basically a base one billion "digit". You can work out how many ints you need, create that array and that's all the storage you need to run the entire algorithm (being 130ish bytes) with everything being done in place.
Solution follows (in fairly rough C#):
static void problem16a()
{
const int limit = 1000;
int ints = limit / 29;
int[] number = new int[ints + 1];
number[0] = 2;
for (int i = 2; i <= limit; i++)
{
doubleNumber(number);
}
String text = NumberToString(number);
Console.WriteLine(text);
Console.WriteLine("The sum of digits of 2^" + limit + " is " + sumDigits(text));
}
static void doubleNumber(int[] n)
{
int carry = 0;
for (int i = 0; i < n.Length; i++)
{
n[i] <<= 1;
n[i] += carry;
if (n[i] >= 1000000000)
{
carry = 1;
n[i] -= 1000000000;
}
else
{
carry = 0;
}
}
}
static String NumberToString(int[] n)
{
int i = n.Length;
while (i > 0 && n[--i] == 0)
;
String ret = "" + n[i--];
while (i >= 0)
{
ret += String.Format("{0:000000000}", n[i--]);
}
return ret;
}

I solved this one using C# also, much to my dismay when I discovered that Python can do this in one simple operation.
Your goal is to create an adding machine using arrays of int values.
Spoiler follows
I ended up using an array of int
values to simulate an adding machine,
but I represented the number backwards
- which you can do because the problem only asks for the sum of the digits,
this means order is irrelevant.
What you're essentially doing is
doubling the value 1000 times, so you
can double the value 1 stored in the
1st element of the array, and then
continue looping until your value is
over 10. This is where you will have
to keep track of a carry value. The
first power of 2 that is over 10 is
16, so the elements in the array after
the 5th iteration are 6 and 1.
Now when you loop through the array
starting at the 1st value (6), it
becomes 12 (so you keep the last
digit, and set a carry bit on the next
index of the array) - which when
that value is doubled you get 2 ... plus the 1 for the carry bit which
equals 3. Now you have 2 and 3 in your
array which represents 32.
Continues this process 1000 times and
you'll have an array with roughly 600
elements that you can easily add up.

I have solved this one before, and now I re-solved it using C# 3.0. :)
I just wrote a Multiply extension method that takes an IEnumerable<int> and a multiplier and returns an IEnumerable<int>. (Each int represents a digit, and the first one it the least significant digit.) Then I just created a list with the item { 1 } and multiplied it by 2 a 1000 times. Adding the items in the list is simple with the Sum extension method.
19 lines of code, which runs in 13 ms. on my laptop. :)

Pretend you are very young, with square paper. To me, that is like a list of numbers. Then to double it you double each number, then handle any "carries", by subtracting the 10s and adding 1 to the next index. So if the answer is 1366... something like (completely unoptimized, rot13):
hfvat Flfgrz;
hfvat Flfgrz.Pbyyrpgvbaf.Trarevp;
pynff Cebtenz {
fgngvp ibvq Pneel(Yvfg<vag> yvfg, vag vaqrk) {
juvyr (yvfg[vaqrk] > 9) {
yvfg[vaqrk] -= 10;
vs (vaqrk == yvfg.Pbhag - 1) yvfg.Nqq(1);
ryfr yvfg[vaqrk + 1]++;
}
}
fgngvp ibvq Znva() {
ine qvtvgf = arj Yvfg<vag> { 1 }; // 2^0
sbe (vag cbjre = 1; cbjre <= 1000; cbjre++) {
sbe (vag qvtvg = 0; qvtvg < qvtvgf.Pbhag; qvtvg++) {
qvtvgf[qvtvg] *= 2;
}
sbe (vag qvtvg = 0; qvtvg < qvtvgf.Pbhag; qvtvg++) {
Pneel(qvtvgf, qvtvg);
}
}
qvtvgf.Erirefr();
sbernpu (vag v va qvtvgf) {
Pbafbyr.Jevgr(v);
}
Pbafbyr.JevgrYvar();
vag fhz = 0;
sbernpu (vag v va qvtvgf) fhz += v;
Pbafbyr.Jevgr("fhz: ");
Pbafbyr.JevgrYvar(fhz);
}
}

If you wish to do the primary calculation in C#, you will need some sort of big integer implementation (Much like gmp for C/C++). Programming is about using the right tool for the right job. If you cannot find a good big integer library for C#, it's not against the rules to calculate the number in a language like Python which already has the ability to calculate large numbers. You could then put this number into your C# program via your method of choice, and iterate over each character in the number (you will have to store it as a string). For each character, convert it to an integer and add it to your total until you reach the end of the number. If you would like the big integer, I calculated it with python below. The answer is further down.
Partial Spoiler
10715086071862673209484250490600018105614048117055336074437503883703510511249361
22493198378815695858127594672917553146825187145285692314043598457757469857480393
45677748242309854210746050623711418779541821530464749835819412673987675591655439
46077062914571196477686542167660429831652624386837205668069376
Spoiler Below!
>>> val = str(2**1000)
>>> total = 0
>>> for i in range(0,len(val)): total += int(val[i])
>>> print total
1366

If you've got ruby, you can easily calculate "2**1000" and get it as a string. Should be an easy cut/paste into a string in C#.
Spoiler
In Ruby: (2**1000).to_s.split(//).inject(0){|x,y| x+y.to_i}

spoiler
If you want to see a solution check
out my other answer. This is in Java but it's very easy to port to C#
Here's a clue:
Represent each number with a list. That way you can do basic sums like:
[1,2,3,4,5,6]
+ [4,5]
_____________
[1,2,3,5,0,1]

One alternative to representing the digits as a sequence of integers is to represent the number base 2^32 as a list of 32 bit integers, which is what many big integer libraries do. You then have to convert the number to base 10 for output. This doesn't gain you very much for this particular problem - you can write 2^1000 straight away, then have to divide by 10 many times instead of multiplying 2 by itself 1000 times ( or, as 1000 is 0b1111101000. calculating the product of 2^8,32,64,128,256,512 using repeated squaring 2^8 = (((2^2)^2)^2))) which requires more space and a multiplication method, but is far fewer operations ) - is closer to normal big integer use, so you may find it more useful in later problems ( if you try to calculate the last ten digits of 28433×2^(7830457)+1 using the digit-per int method and repeated addition, it may take some time (though in that case you could use modulo arthimetic, rather than adding strings of millions of digits) ).

Working solution that I have posted it here as well: http://www.mycoding.net/2012/01/solution-to-project-euler-problem-16/
The code:
import java.math.BigInteger;
public class Euler16 {
public static void main(String[] args) {
int power = 1;
BigInteger expo = new BigInteger("2");
BigInteger num = new BigInteger("2");
while(power < 1000){
expo = expo.multiply(num);
power++;
}
System.out.println(expo); //Printing the value of 2^1000
int sum = 0;
char[] expoarr = expo.toString().toCharArray();
int max_count = expoarr.length;
int count = 0;
while(count<max_count){ //While loop to calculate the sum of digits
sum = sum + (expoarr[count]-48);
count++;
}
System.out.println(sum);
}
}

Euler problem #16 has been discussed many times here, but I could not find an answer that gives a good overview of possible solution approaches, the lay of the land as it were. Here's my attempt at rectifying that.
This overview is intended for people who have already found a solution and want to get a more complete picture. It is basically language-agnostic even though the sample code is C#. There are some usages of features that are not available in C# 2.0 but they are not essential - their purpose is only to get boring stuff out of the way with a minimum of fuss.
Apart from using a ready-made BigInteger library (which doesn't count), straightforward solutions for Euler #16 fall into two fundamental categories: performing calculations natively - i.e. in a base that is a power of two - and converting to decimal in order to get at the digits, or performing the computations directly in a decimal base so that the digits are available without any conversion.
For the latter there are two reasonably simple options:
repeated doubling
powering by repeated squaring
Native Computation + Radix Conversion
This approach is the simplest and its performance exceeds that of naive solutions using .Net's builtin BigInteger type.
The actual computation is trivially achieved: just perform the moral equivalent of 1 << 1000, by storing 1000 binary zeroes and appending a single lone binary 1.
The conversion is also quite simple and can be done by coding the pencil-and-paper division method, with a suitably large choice of 'digit' for efficiency. Variables for intermediate results need to be able to hold two 'digits'; dividing the number of decimal digits that fit in a long by 2 gives 9 decimal digits for the maximum meta-digit (or 'limb', as it is usually called in bignum lore).
class E16_RadixConversion
{
const int BITS_PER_WORD = sizeof(uint) * 8;
const uint RADIX = 1000000000; // == 10^9
public static int digit_sum_for_power_of_2 (int exponent)
{
var dec = new List<int>();
var bin = new uint[(exponent + BITS_PER_WORD) / BITS_PER_WORD];
int top = bin.Length - 1;
bin[top] = 1u << (exponent % BITS_PER_WORD);
while (top >= 0)
{
ulong rest = 0;
for (int i = top; i >= 0; --i)
{
ulong temp = (rest << BITS_PER_WORD) | bin[i];
ulong quot = temp / RADIX; // x64 uses MUL (sometimes), x86 calls a helper function
rest = temp - quot * RADIX;
bin[i] = (uint)quot;
}
dec.Add((int)rest);
if (bin[top] == 0)
--top;
}
return E16_Common.digit_sum(dec);
}
}
I wrote (rest << BITS_PER_WORD) | big[i] instead of using operator + because that is precisely what is needed here; no 64-bit addition with carry propagation needs to take place. This means that the two operands could be written directly to their separate registers in a register pair, or to fields in an equivalent struct like LARGE_INTEGER.
On 32-bit systems the 64-bit division cannot be inlined as a few CPU instructions, because the compiler cannot know that the algorithm guarantees quotient and remainder to fit into 32-bit registers. Hence the compiler calls a helper function that can handle all eventualities.
These systems may profit from using a smaller limb, i.e. RADIX = 10000 and uint instead of ulong for holding intermediate (double-limb) results. An alternative for languages like C/C++ would be to call a suitable compiler intrinsic that wraps the raw 32-bit by 32-bit to 64-bit multiply (assuming that division by the constant radix is to be implemented by multiplication with the inverse). Conversely, on 64-bit systems the limb size can be increased to 19 digits if the compiler offers a suitable 64-by-64-to-128 bit multiply primitive or allows inline assembler.
Decimal Doubling
Repeated doubling seems to be everyone's favourite, so let's do that next. Variables for intermediate results need to hold one 'digit' plus one carry bit, which gives 18 digits per limb for long. Going to ulong cannot improve things (there's 0.04 bit missing to 19 digits plus carry), and so we might as well stick with long.
On a binary computer, decimal limbs do not coincide with computer word boundaries. That makes it necessary to perform a modulo operation on the limbs during each step of the calculation. Here, this modulo op can be reduced to a subtraction of the modulus in the event of carry, which is faster than performing a division. The branching in the inner loop can be eliminated by bit twiddling but that would be needlessly obscure for a demonstration of the basic algorithm.
class E16_DecimalDoubling
{
const int DIGITS_PER_LIMB = 18; // == floor(log10(2) * (63 - 1)), b/o carry
const long LIMB_MODULUS = 1000000000000000000L; // == 10^18
public static int digit_sum_for_power_of_2 (int power_of_2)
{
Trace.Assert(power_of_2 > 0);
int total_digits = (int)Math.Ceiling(Math.Log10(2) * power_of_2);
int total_limbs = (total_digits + DIGITS_PER_LIMB - 1) / DIGITS_PER_LIMB;
var a = new long[total_limbs];
int limbs = 1;
a[0] = 2;
for (int i = 1; i < power_of_2; ++i)
{
int carry = 0;
for (int j = 0; j < limbs; ++j)
{
long new_limb = (a[j] << 1) | carry;
carry = 0;
if (new_limb >= LIMB_MODULUS)
{
new_limb -= LIMB_MODULUS;
carry = 1;
}
a[j] = new_limb;
}
if (carry != 0)
{
a[limbs++] = carry;
}
}
return E16_Common.digit_sum(a);
}
}
This is just as simple as radix conversion, but except for very small exponents it does not perform anywhere near as well (despite its huge meta-digits of 18 decimal places). The reason is that the code must perform (exponent - 1) doublings, and the work done in each pass corresponds to about half the total number of digits (limbs).
Repeated Squaring
The idea behind powering by repeated squaring is to replace a large number of doublings with a small number of multiplications.
1000 = 2^3 + 2^5 + 2^6 + 2^7 + 2^8 + 2^9
x^1000 = x^(2^3 + 2^5 + 2^6 + 2^7 + 2^8 + 2^9)
x^1000 = x^2^3 * x^2^5 * x^2^6 * x^2^7 * x^2*8 * x^2^9
x^2^3 can be obtained by squaring x three times, x^2^5 by squaring five times, and so on. On a binary computer the decomposition of the exponent into powers of two is readily available because it is the bit pattern representing that number. However, even non-binary computers should be able to test whether a number is odd or even, or to divide a number by two.
The multiplication can be done by coding the pencil-and-paper method; here I'm using a helper function that computes one row of a product and adds it into the result at a suitably shifted position, so that the rows of partial products do not need to be stored for a separate addition step later. Intermediate values during computation can be up to two 'digits' in size, so that the limbs can be only half as wide as for repeated doubling (where only one extra bit had to fit in addition to a 'digit').
Note: the radix of the computations is not a power of 2, and so the squarings of 2 cannot be computed by simple shifting here. On the positive side, the code can be used for computing powers of bases other than 2.
class E16_DecimalSquaring
{
const int DIGITS_PER_LIMB = 9; // language limit 18, half needed for holding the carry
const int LIMB_MODULUS = 1000000000;
public static int digit_sum_for_power_of_2 (int e)
{
Trace.Assert(e > 0);
int total_digits = (int)Math.Ceiling(Math.Log10(2) * e);
int total_limbs = (total_digits + DIGITS_PER_LIMB - 1) / DIGITS_PER_LIMB;
var squared_power = new List<int>(total_limbs) { 2 };
var result = new List<int>(total_limbs);
result.Add((e & 1) == 0 ? 1 : 2);
while ((e >>= 1) != 0)
{
squared_power = multiply(squared_power, squared_power);
if ((e & 1) == 1)
result = multiply(result, squared_power);
}
return E16_Common.digit_sum(result);
}
static List<int> multiply (List<int> lhs, List<int> rhs)
{
var result = new List<int>(lhs.Count + rhs.Count);
resize_to_capacity(result);
for (int i = 0; i < rhs.Count; ++i)
addmul_1(result, i, lhs, rhs[i]);
trim_leading_zero_limbs(result);
return result;
}
static void addmul_1 (List<int> result, int offset, List<int> multiplicand, int multiplier)
{
// it is assumed that the caller has sized `result` appropriately before calling this primitive
Trace.Assert(result.Count >= offset + multiplicand.Count + 1);
long carry = 0;
foreach (long limb in multiplicand)
{
long temp = result[offset] + limb * multiplier + carry;
carry = temp / LIMB_MODULUS;
result[offset++] = (int)(temp - carry * LIMB_MODULUS);
}
while (carry != 0)
{
long final_temp = result[offset] + carry;
carry = final_temp / LIMB_MODULUS;
result[offset++] = (int)(final_temp - carry * LIMB_MODULUS);
}
}
static void resize_to_capacity (List<int> operand)
{
operand.AddRange(Enumerable.Repeat(0, operand.Capacity - operand.Count));
}
static void trim_leading_zero_limbs (List<int> operand)
{
int i = operand.Count;
while (i > 1 && operand[i - 1] == 0)
--i;
operand.RemoveRange(i, operand.Count - i);
}
}
The efficiency of this approach is roughly on par with radix conversion but there are specific improvements that apply here. Efficiency of the squaring can be doubled by writing a special squaring routine that utilises the fact that ai*bj == aj*bi if a == b, which cuts the number of multiplications in half.
Also, there are methods for computing addition chains that involve fewer operations overall than using the exponent bits for determining the squaring/multiplication schedule.
Helper Code and Benchmarks
The helper code for summing decimal digits in the meta-digits (decimal limbs) produced by the sample code is trivial, but I'm posting it here anyway for your convenience:
internal class E16_Common
{
internal static int digit_sum (int limb)
{
int sum = 0;
for ( ; limb > 0; limb /= 10)
sum += limb % 10;
return sum;
}
internal static int digit_sum (long limb)
{
const int M1E9 = 1000000000;
return digit_sum((int)(limb / M1E9)) + digit_sum((int)(limb % M1E9));
}
internal static int digit_sum (IEnumerable<int> limbs)
{
return limbs.Aggregate(0, (sum, limb) => sum + digit_sum(limb));
}
internal static int digit_sum (IEnumerable<long> limbs)
{
return limbs.Select((limb) => digit_sum(limb)).Sum();
}
}
This can be made more efficient in various ways but overall it is not critical.
All three solutions take O(n^2) time where n is the exponent. In other words, they will take a hundred times as long when the exponent grows by a factor of ten. Radix conversion and repeated squaring can both be improved to roughly O(n log n) by employing divide-and-conquer strategies; I doubt whether the doubling scheme can be improved in a similar fastion but then it was never competitive to begin with.
All three solutions presented here can be used to print the actual results, by stringifying the meta-digits with suitable padding and concatenating them. I've coded the functions as returning the digit sum instead of the arrays/lists with decimal limbs only in order to keep the sample code simple and to ensure that all functions have the same signature, for benchmarking.
In these benchmarks, the .Net BigInteger type was wrapped like this:
static int digit_sum_via_BigInteger (int power_of_2)
{
return System.Numerics.BigInteger.Pow(2, power_of_2)
.ToString()
.ToCharArray()
.Select((c) => (int)c - '0')
.Sum();
}
Finally, the benchmarks for the C# code:
# testing decimal doubling ...
1000: 1366 in 0,052 ms
10000: 13561 in 3,485 ms
100000: 135178 in 339,530 ms
1000000: 1351546 in 33.505,348 ms
# testing decimal squaring ...
1000: 1366 in 0,023 ms
10000: 13561 in 0,299 ms
100000: 135178 in 24,610 ms
1000000: 1351546 in 2.612,480 ms
# testing radix conversion ...
1000: 1366 in 0,018 ms
10000: 13561 in 0,619 ms
100000: 135178 in 60,618 ms
1000000: 1351546 in 5.944,242 ms
# testing BigInteger + LINQ ...
1000: 1366 in 0,021 ms
10000: 13561 in 0,737 ms
100000: 135178 in 69,331 ms
1000000: 1351546 in 6.723,880 ms
As you can see, the radix conversion is almost as slow as the solution using the builtin BigInteger class. The reason is that the runtime is of the newer type that does performs certain standard optimisations only for signed integer types but not for unsigned ones (here: implementing division by a constant as multiplication with the inverse).
I haven't found an easy means of inspecting the native code for existing .Net assemblies, so I decided on a different path of investigation: I coded a variant of E16_RadixConversion for comparison where ulong and uint were replaced by long and int respectively, and BITS_PER_WORD decreased by 1 accordingly. Here are the timings:
# testing radix conv Int63 ...
1000: 1366 in 0,004 ms
10000: 13561 in 0,202 ms
100000: 135178 in 18,414 ms
1000000: 1351546 in 1.834,305 ms
More than three times as fast as the version that uses unsigned types! Clear evidence of numbskullery in the compiler...
In order to showcase the effect of different limb sizes I templated the solutions in C++ on the unsigned integer types used as limbs. The timings are prefixed with the byte size of a limb and the number of decimal digits in a limb, separated by a colon. There is no timing for the often-seen case of manipulating digit characters in strings, but it is safe to say that such code will take at least twice as long as the code that uses double digits in byte-sized limbs.
# E16_DecimalDoubling
[1:02] e = 1000 -> 1366 0.308 ms
[2:04] e = 1000 -> 1366 0.152 ms
[4:09] e = 1000 -> 1366 0.070 ms
[8:18] e = 1000 -> 1366 0.071 ms
[1:02] e = 10000 -> 13561 30.533 ms
[2:04] e = 10000 -> 13561 13.791 ms
[4:09] e = 10000 -> 13561 6.436 ms
[8:18] e = 10000 -> 13561 2.996 ms
[1:02] e = 100000 -> 135178 2719.600 ms
[2:04] e = 100000 -> 135178 1340.050 ms
[4:09] e = 100000 -> 135178 588.878 ms
[8:18] e = 100000 -> 135178 290.721 ms
[8:18] e = 1000000 -> 1351546 28823.330 ms
For the exponent of 10^6 there is only the timing with 64-bit limbs, since I didn't have the patience to wait many minutes for full results. The picture is similar for radix conversion, except that there is no row for 64-bit limbs because my compiler does not have a native 128-bit integral type.
# E16_RadixConversion
[1:02] e = 1000 -> 1366 0.080 ms
[2:04] e = 1000 -> 1366 0.026 ms
[4:09] e = 1000 -> 1366 0.048 ms
[1:02] e = 10000 -> 13561 4.537 ms
[2:04] e = 10000 -> 13561 0.746 ms
[4:09] e = 10000 -> 13561 0.243 ms
[1:02] e = 100000 -> 135178 445.092 ms
[2:04] e = 100000 -> 135178 68.600 ms
[4:09] e = 100000 -> 135178 19.344 ms
[4:09] e = 1000000 -> 1351546 1925.564 ms
The interesting thing is that simply compiling the code as C++ doesn't make it any faster - i.e., the optimiser couldn't find any low-hanging fruit that the C# jitter missed, apart from not toeing the line with regard to penalising unsigned integers. That's the reason why I like prototyping in C# - performance in the same ballpark as (unoptimised) C++ and none of the hassle.
Here's the meat of the C++ version (sans reams of boring stuff like helper templates and so on) so that you can see that I didn't cheat to make C# look better:
template<typename W>
struct E16_RadixConversion
{
typedef W limb_t;
typedef typename detail::E16_traits<W>::long_t long_t;
static unsigned const BITS_PER_WORD = sizeof(limb_t) * CHAR_BIT;
static unsigned const RADIX_DIGITS = std::numeric_limits<limb_t>::digits10;
static limb_t const RADIX = detail::pow10_t<limb_t, RADIX_DIGITS>::RESULT;
static unsigned digit_sum_for_power_of_2 (unsigned e)
{
std::vector<limb_t> digits;
compute_digits_for_power_of_2(e, digits);
return digit_sum(digits);
}
static void compute_digits_for_power_of_2 (unsigned e, std::vector<limb_t> &result)
{
assert(e > 0);
unsigned total_digits = unsigned(std::ceil(std::log10(2) * e));
unsigned total_limbs = (total_digits + RADIX_DIGITS - 1) / RADIX_DIGITS;
result.resize(0);
result.reserve(total_limbs);
std::vector<limb_t> bin((e + BITS_PER_WORD) / BITS_PER_WORD);
bin.back() = limb_t(limb_t(1) << (e % BITS_PER_WORD));
while (!bin.empty())
{
long_t rest = 0;
for (std::size_t i = bin.size(); i-- > 0; )
{
long_t temp = (rest << BITS_PER_WORD) | bin[i];
long_t quot = temp / RADIX;
rest = temp - quot * RADIX;
bin[i] = limb_t(quot);
}
result.push_back(limb_t(rest));
if (bin.back() == 0)
bin.pop_back();
}
}
};
Conclusion
These benchmarks also show that this Euler task - like many others - seems designed to be solved on a ZX81 or an Apple ][, not on our modern toys that are a million times as powerful. There's no challenge involved here unless the limits are increased drastically (an exponent of 10^5 or 10^6 would be much more adequate).
A good overview of the practical state of the art can be got from GMP's overview of algorithms. Another excellent overview of the algorithms is chapter 1 of "Modern Computer Arithmetic" by Richard Brent and Paul Zimmermann. It contains exactly what one needs to know for coding challenges and competitions, but unfortunately the depth is not equal to that of Donald Knuth's treatment in "The Art of Computer Programming".
The radix conversion solution adds a useful technique to one's code challenge toolchest, since the given code can be trivially extended for converting any old big integer instead of only the bit pattern 1 << exponent. The repeated squaring solutiono can be similarly useful since changing the sample code to power something other than 2 is again trivial.
The approach of performing computations directly in powers of 10 can be useful for challenges where decimal results are required, because performance is in the same ballpark as native computation but there is no need for a separate conversion step (which can require similar amounts of time as the actual computation).