On a homework assignment, one of the questions asked us to multiply any arbitrary integer by a constant using only the +, -, and << operators and a maximum of three operations. For example, the first constant was 17, which I solved as
(x << 4) + x
However, some of the constants given were negative (such as -7). Multiplying by 7 is a relatively trivial thing to do (I have it as (x << 3) - x), but I cannot figure out how to flip the sign using only the three allowed operators.
I have attempted to flip that bit by adding or subtracting 2147483648 to every result (with the idea that this would force the most significant bit to be used, thus flipping the sign), but in my test implementation in C#, this has proven unsuccessful.
Is there some positive number by which I can multiply a given int that will be functionally analogous to -7? Would adding 2147483648 work in a language other than C#? Am I overlooking something?
The original question from the book is below:
Suppose we are given the task of generating code to multiply integer variable x by various different constant factors K. To be efficient, we want to use only the operations +, -, and <<. For the following values of K, write C expressions to perform the multiplication using at most three operations per expression.
A. K = 17
B. K = -7
C. K = 60
D. K = -112
You don't need to change the sign. You wrote 7 * x as (equivalent to) 8*x - x. Now, what do you need to do with that to obtain -7 * x?
Is x - (x << 3) not valid?
Related
Take for example the number 91. That number in binary is 1011011. If you shift that number to the right by 5 bits, you would get 2 (10 in binary). According to a google search, bit shifting to the left or right by a certain amount of bits is the same as multiplying or dividing the number by 2 to the power of the number of bits to be shifted, respectively. so to get from 91 to 2 by bit shifting, the equation would look like this: 91 / 2^5, which is also 91 / 32. Now, of course if you did that in your calculator, there would be some decimal values, which aren't included when bit shifting. The resulting 2 is actually 2.84357. I'm sure you know that if you do a certain operation on a number and then you do the inverse, the result would be what you had in the first place. So does decimal precision have something to do with this?
There is a mathematical equivalent of shifting to the right... and the mathematical operation is UNRECOVERABLE.
You seem to think that shifting to the right is:
bit shifting to the left or right by a certain amount of bits is the same as multiplying or dividing the number by 2
This is what you will hear people casually say, but it is only half right. As it it is not the same but only similar.
The correct statement is:
shifting a base-2 number one digit to the right is THE SAME as dividing by two in the integer domain
If you have an integer calculator, if you did 91/32 you will get 2. You will not get ANY decimal point because we are operating in the integer domain.
For real numbers, the equivalent operation is:
FLOOR(91/32)
Which is also unrecoverable because it also results in 2.
The lesson here is be careful when listening to what people CASUALLY say. Casual speech is often imprecise and assumes the listener is familiar with the subject. You need to dig deeper what the statement is actually trying to say.
As for why it is unrecoverable? Division of integers give two results: the quotient (which is the main result) and the remainder. When we divide 91 by 32 we are doing this:
2
_____
32 ) 91
64
__
27
So we get the result of 2 and a remainder of 27. The reason you can't get 91 by multiplying 2*32 is because we threw away the remainder.
You can get the result back if you saved the remainder. However, calculating the remainder is not a matter of simple shifts. Here's an example of how to make it reversable in C:
int test () {
int a = 91;
int b = 32;
int result;
int remainder;
result = a / b; // result will be 2
remainder = a % b; // remainder will be 27
return (result * b) + remainder; // returns 91
}
You can only recover the result of an operation if it has a 1-1 mapping between the inputs and outputs, i.e. it has an inverse function. But not all mathematical functions have an inverse function
For example if f(x) = x >> n with >> is the shift operator then it'll be equivalent to
f(x) = āx/2nā
with ā ā being the floor function. Since there are many inputs that lead to the same output, the relationship isn't 1-1 and there can't be an inverse function for it. This function works the same for both signed and unsigned right shift:
91 >> 5 == floor(91.0/32.0) == 2
-91 >> 5 == floor(-91.0/32.0) == -3
Similarly for an unsigned left shift function g(x) = x << n then the equivalent is
g(x) = (x * 2n) mod 2N
with N being the size in bits of x, because integer math in hardware, C and many other languages always reduce modulo 2N due to the limit of register size and the use of two's complement. And it's clear that the modulo function also isn't invertible/recoverable. The signed left shift is almost the same with some small modifications
Is this a correct implementation of the Knuth multiplicative hash.
int hash(int v)
{
v *= 2654435761;
return v >> 32;
}
Does overflow in the multiplication affects the algorithm?
How to improve the performance of this method?
Knuth multiplicative hash is used to compute an hash value in {0, 1, 2, ..., 2^p - 1} from an integer k.
Suppose that p is in between 0 and 32, the algorithm goes like this:
Compute alpha as the closest integer to 2^32 (-1 + sqrt(5)) / 2. We get alpha = 2 654 435 769.
Compute k * alpha and reduce the result modulo 2^32:
k * alpha = n0 * 2^32 + n1 with 0 <= n1 < 2^32
Keep the highest p bits of n1:
n1 = m1 * 2^(32-p) + m2 with 0 <= m2 < 2^(32 - p)
So, a correct implementation of Knuth multiplicative algorithm in C++ is:
std::uint32_t knuth(int x, int p) {
assert(p >= 0 && p <= 32);
const std::uint32_t knuth = 2654435769;
const std::uint32_t y = x;
return (y * knuth) >> (32 - p);
}
Forgetting to shift the result by (32 - p) is a major mistake. As you would lost all the good properties of the hash. It would transform an even sequence into an even sequence which would be very bad as all the odd slots would stay unoccupied. That's like taking a good wine and mixing it with Coke. By the way, the web is full of people misquoting Knuth and using a multiplication by 2 654 435 761 without taking the higher bits. I just opened the Knuth and he never said such a thing. It looks like some guy who decided he was "smart" decided to take a prime number close to 2 654 435 769.
Bare in mind that most hash tables implementations don't allow this kind of signature in their interface, as they only allow
uint32_t hash(int x);
and reduce hash(x) modulo 2^p to compute the hash value for x. Those hash tables cannot accept the Knuth multiplicative hash. This might be a reason why so many people completely ruined the algorithm by forgetting to take the higher p bits.
So you can't use the Knuth multiplicative hash with std::unordered_map or std::unordered_set. But I think that those hash tables use a prime number as a size, so the Knuth multiplicative hash is not useful in this case. Using hash(x) = x would be a good fit for those tables.
Source: "Introduction to Algorithms, third edition", Cormen et al., 13.3.2 p:263
Source: "The Art of Computer Programming, Volume 3, Sorting and Searching", D.E. Knuth, 6.4 p:516
Ok, I looked it up in TAOCP volume 3 (2nd edition), section 6.4, page 516.
This implementation is not correct, though as I mentioned in the comments it may give the correct result anyway.
A correct way (I think - feel free to read the relevant chapter of TAOCP and verify this) is something like this: (important: yes, you must shift the result right to reduce it, not use bitwise AND. However, that is not the responsibility of this function - range reduction is not properly part of hashing itself)
uint32_t hash(uint32_t v)
{
return v * UINT32_C(2654435761);
// do not comment about the lack of right shift. I'm not ignoring it. read on.
}
Note the uint32_t's (as opposed to int's) - they make sure the multiplication overflows modulo 2^32, as it is supposed to do if you choose 32 as the word size. There is also no right shift by k here, because there is no reason to give responsibility for range-reduction to the basic hashing function and it is actually more useful to get the full result. The constant 2654435761 is from the question, the actual suggested constant is 2654435769, but that's a small difference that as far as I know does not affect the quality of the hash.
Other valid implementations shift the result right by some amount (not the full word size though, that doesn't make sense and C++ doesn't like it), depending on how many bits of hash you need. Or they may use an other constant (subject to certain conditions) or an other word size. Reducing the hash modulo something is not a valid implementation, but a common mistake, likely it is a de-facto standard way to do range-reduction on a hash. The bottom bits of a multiplicative hash are the worst-quality bits (they depend on less of the input), you only want to use them if you really need more bits, while reducing the hash modulo a power of two would return only the worst bits. Indeed that is equivalent to throwing away most of the input bits too. Reducing modulo a non-power-of-two is not so bad since it does mix in the higher bits, but it's not how the multiplicative hash was defined.
So to be clear, yes there is a right shift, but that is range reduction not hashing and can only be the responsibility of the hash table, since it depends on its internal size.
The type should be unsigned, otherwise the overflow is unspecified (thus possibly wrong, not just on non-2's-complement architectures but also on overly clever compilers) and the optional right shift would be a signed shift (wrong).
On the page I mention at the top, there is this formula:
Here we have A = 2654435761 (or 2654435769), w = 232 and M = 232. Calculating AK/w gives a fixed-point result with the format Q32.32, the mod 1 step takes only the 32 fraction bits. But that's just the same thing as doing a modular multiplication and then saying that the result is the fraction bits. Of course when multiplied by M, all the fraction bits become integer bits because of how M was chosen, and so it simplifies to just a plain old modular multiplication. When M is a lower power of two, that just right-shifts the result, as mentioned.
Might be late, but heres a Java Implementation of Knuth's Method :
For a hashtable of Size N :
public long hash(int key) {
long l = 2654435769L;
return (key * l >> 32) % N ;
}
If the input argument is a pointer then I use this
#include <inttypes.h>
uint32_t knuth_mul_hash(void* k) {
ptrdiff_t v = (ptrdiff_t)k * UINT32_C(2654435761);
v >>= ((sizeof(ptrdiff_t) - sizeof(uint32_t)) * 8); // Right-shift v by the size difference between a pointer and a 32-bit integer (0 for x86, 32 for x64)
return (uint32_t)(v & UINT32_MAX);
}
I usually use this as the default fallback hashing function in hashmap implementations, dictionaries, sets, etc...
I'm trying to port some Java code, which requires arithmetic and logical bit shifts, to ABAP.
As far as I know, ABAP only supports the bitwise NOT, AND, OR and XOR operations.
Does anyone know another way to implement these kind of shifts with ABAP? Is there perhaps a way to get the same result as the shifts, by using just the NOT, AND, OR and XOR operations?
Disclaimer: I am not specifically familiar with ABAP, hence this answer is given on a more general level.
Assuming that what you said is true (ABAP doesn't support shifts, which I somewhat doubt), you can use multiplications and divisions instead.
Logical shift left (LSHL)
Can be expressed in terms of multiplication:
x LSHL n = x * 2^n
For example given x=9, n=2:
9 LSHL 2 = 9 * 2^2 = 36
Logical shift right (LSHR)
Can be expressed with (truncating) division:
x LSHR n = x / 2^n
Given x=9, n=2:
9 LSHR 2 = 9 / 2^2 = 2.25 -> 2 (truncation)
Arithmetic shift left (here: "ASHL")
If you wish to perform arithmetic shifts (=preserve sign), we need to further refine the expressions to preserve the sign bit.
Assuming we know that we are dealing with a 32-bit signed integer, where the highest bit is used to represent the sign:
x ASHL n = ((x AND (2^31-1)) * 2^n) + (x AND 2^31)
Example: Shifting Integer.MAX_VALUE to left by one in Java
As an example of how this works, let us consider that we want to shift Java's Integer.MAX_VALUE to left by one. Logical shift left can be represented as *2. Consider the following program:
int maxval = (int)(Integer.MAX_VALUE);
System.out.println("max value : 0" + Integer.toBinaryString(maxval));
System.out.println("sign bit : " + Integer.toBinaryString(maxval+1));
System.out.println("max val<<1: " + Integer.toBinaryString(maxval<<1));
System.out.println("max val*2 : " + Integer.toBinaryString(maxval*2));
The program's output:
max value : 01111111111111111111111111111111 (2147483647)
sign bit : 10000000000000000000000000000000 (-2147483648)
max val<<1: 11111111111111111111111111111110 (-2)
max val*2 : 11111111111111111111111111111110 (-2)
The result is negative due that the highest bit in integer is used to represent sign. We get the exact number of -2, because of the way negative numbers are represents in Java (for details, see for instance http://www.javabeat.net/qna/30-negative-numbers-and-binary-representation-in/).
Edit: the updated code can now be found over here: github gist
This question already has answers here:
Closed 11 years ago.
Possible Duplicate:
Best way to detect integer overflow in C/C++
If I have an expression x + y (in C or C++) where x and y are both of type uint64_t which causes an integer overflow, how do I detect how much it overflowed by (the carry), place than in another variable, then compute the remainder?
The remainder will already be stored in the sum of x + y, assuming you are using unsigned integers. Unsigned integer overflow causes a wrap around ( signed integer overflow is undefined ). See standards reference from Pascal in the comments.
The overflow can only be 1 bit. If you add 2 64 bit numbers, there cannot be more than 1 carry bit, so you just have to detect the overflow condition.
For how to detect overflow, there was a previous question on that topic: best way to detect integer overflow.
For z = x + y, z stores the remainder. The overflow can only be 1 bit and it's easy to detect. If you were dealing with signed integers then there's an overflow if x and y have the same sign but z has the opposite. You cannot overflow if x and y have different signs. For unsigned integers you just check the most significant bit in the same manner.
The approach in C and C++ can be quite different, because in C++ you can have operator overloading work for you, and wrap the integer you want to protect in some kind of class (for which you would overload the necessary operators. In C, you would have to wrap the integer you want to protect in a structure (to carry the remainder as well as the result) and call some function to do the heavy lifting.
Other than that, the approach in the two languages is the same: depending on the operation you want to perform (adding, in your example) you have to figure out the worst that could happen and handle it.
In the case of adding, it's quite simple: if the sum of the two is going to be greater than some maximum value (which will be the case if the difference of that maximum value M and one of the operands is greater than the other operand) you can calculate the remainder - the part that's too big: if ((M - O1) > O2) R = O2 - (M - O1) (e.g. if M is 100, O1 is 80 and O2 is 30, 30 - (100 - 80) = 10, which is the remainder).
The case of subtraction is equally simple: if your first operand is smaller than the second, the remainder is the second minus the first (if (O1 < O2) { Rem = O2 - O1; Result = 0; } else { Rem = 0; Result = O1 - O2; }).
It's multiplication that's a bit more difficult: your safest bet is to do a binary multiplication of the values and check that your resulting value doesn't exceed the number of bits you have. Binary multiplication is a long multiplication, just like you would do if you were doing a decimal multiplication by hand on paper, so, for example, 12 * 5 is:
0110
0100
====
0110
0
0110
0
++++++
011110 = 40
if you'd have a four-bit integer, you'd have an overflow of one bit here (i.e. bit 4 is 1, bit 5 is 0. so only bit 4 counts as an overflow).
For division you only really need to care about division by 0, most of the time - the rest will be handled be your CPU.
HTH
rlc
I read somewhere once that the modulus operator is inefficient on small embedded devices like 8 bit micro-controllers that do not have integer division instruction. Perhaps someone can confirm this but I thought the difference is 5-10 time slower than with an integer division operation.
Is there another way to do this other than keeping a counter variable and manually overflowing to 0 at the mod point?
const int FIZZ = 6;
for(int x = 0; x < MAXCOUNT; x++)
{
if(!(x % FIZZ)) print("Fizz\n"); // slow on some systems
}
vs:
The way I am currently doing it:
const int FIZZ = 6;
int fizzcount = 1;
for(int x = 1; x < MAXCOUNT; x++)
{
if(fizzcount >= FIZZ)
{
print("Fizz\n");
fizzcount = 0;
}
}
Ah, the joys of bitwise arithmetic. A side effect of many division routines is the modulus - so in few cases should division actually be faster than modulus. I'm interested to see the source you got this information from. Processors with multipliers have interesting division routines using the multiplier, but you can get from division result to modulus with just another two steps (multiply and subtract) so it's still comparable. If the processor has a built in division routine you'll likely see it also provides the remainder.
Still, there is a small branch of number theory devoted to Modular Arithmetic which requires study if you really want to understand how to optimize a modulus operation. Modular arithmatic, for instance, is very handy for generating magic squares.
So, in that vein, here's a very low level look at the math of modulus for an example of x, which should show you how simple it can be compared to division:
Maybe a better way to think about the problem is in terms of number
bases and modulo arithmetic. For example, your goal is to compute DOW
mod 7 where DOW is the 16-bit representation of the day of the
week. You can write this as:
DOW = DOW_HI*256 + DOW_LO
DOW%7 = (DOW_HI*256 + DOW_LO) % 7
= ((DOW_HI*256)%7 + (DOW_LO % 7)) %7
= ((DOW_HI%7 * 256%7) + (DOW_LO%7)) %7
= ((DOW_HI%7 * 4) + (DOW_LO%7)) %7
Expressed in this manner, you can separately compute the modulo 7
result for the high and low bytes. Multiply the result for the high by
4 and add it to the low and then finally compute result modulo 7.
Computing the mod 7 result of an 8-bit number can be performed in a
similar fashion. You can write an 8-bit number in octal like so:
X = a*64 + b*8 + c
Where a, b, and c are 3-bit numbers.
X%7 = ((a%7)*(64%7) + (b%7)*(8%7) + c%7) % 7
= (a%7 + b%7 + c%7) % 7
= (a + b + c) % 7
since 64%7 = 8%7 = 1
Of course, a, b, and c are
c = X & 7
b = (X>>3) & 7
a = (X>>6) & 7 // (actually, a is only 2-bits).
The largest possible value for a+b+c is 7+7+3 = 17. So, you'll need
one more octal step. The complete (untested) C version could be
written like:
unsigned char Mod7Byte(unsigned char X)
{
X = (X&7) + ((X>>3)&7) + (X>>6);
X = (X&7) + (X>>3);
return X==7 ? 0 : X;
}
I spent a few moments writing a PIC version. The actual implementation
is slightly different than described above
Mod7Byte:
movwf temp1 ;
andlw 7 ;W=c
movwf temp2 ;temp2=c
rlncf temp1,F ;
swapf temp1,W ;W= a*8+b
andlw 0x1F
addwf temp2,W ;W= a*8+b+c
movwf temp2 ;temp2 is now a 6-bit number
andlw 0x38 ;get the high 3 bits == a'
xorwf temp2,F ;temp2 now has the 3 low bits == b'
rlncf WREG,F ;shift the high bits right 4
swapf WREG,F ;
addwf temp2,W ;W = a' + b'
; at this point, W is between 0 and 10
addlw -7
bc Mod7Byte_L2
Mod7Byte_L1:
addlw 7
Mod7Byte_L2:
return
Here's a liitle routine to test the algorithm
clrf x
clrf count
TestLoop:
movf x,W
RCALL Mod7Byte
cpfseq count
bra fail
incf count,W
xorlw 7
skpz
xorlw 7
movwf count
incfsz x,F
bra TestLoop
passed:
Finally, for the 16-bit result (which I have not tested), you could
write:
uint16 Mod7Word(uint16 X)
{
return Mod7Byte(Mod7Byte(X & 0xff) + Mod7Byte(X>>8)*4);
}
Scott
If you are calculating a number mod some power of two, you can use the bit-wise and operator. Just subtract one from the second number. For example:
x % 8 == x & 7
x % 256 == x & 255
A few caveats:
This only works if the second number is a power of two.
It's only equivalent if the modulus is always positive. The C and C++ standards don't specify the sign of the modulus when the first number is negative (until C++11, which does guarantee it will be negative, which is what most compilers were already doing). A bit-wise and gets rid of the sign bit, so it will always be positive (i.e. it's a true modulus, not a remainder). It sounds like that's what you want anyway though.
Your compiler probably already does this when it can, so in most cases it's not worth doing it manually.
There is an overhead most of the time in using modulo that are not powers of 2.
This is regardless of the processor as (AFAIK) even processors with modulus operators are a few cycles slower for divide as opposed to mask operations.
For most cases this is not an optimisation that is worth considering, and certainly not worth calculating your own shortcut operation (especially if it still involves divide or multiply).
However, one rule of thumb is to select array sizes etc. to be powers of 2.
so if calculating day of week, may as well use %7 regardless
if setting up a circular buffer of around 100 entries... why not make it 128. You can then write % 128 and most (all) compilers will make this & 0x7F
Unless you really need high performance on multiple embedded platforms, don't change how you code for performance reasons until you profile!
Code that's written awkwardly to optimize for performance is hard to debug and hard to maintain. Write a test case, and profile it on your target. Once you know the actual cost of modulus, then decide if the alternate solution is worth coding.
#Matthew is right. Try this:
int main() {
int i;
for(i = 0; i<=1024; i++) {
if (!(i & 0xFF)) printf("& i = %d\n", i);
if (!(i % 0x100)) printf("mod i = %d\n", i);
}
}
x%y == (x-(x/y)*y)
Hope this helps.
Do you have access to any programmable hardware on the embedded device? Like counters and such? If so, you might be able to write a hardware based mod unit, instead of using the simulated %. (I did that once in VHDL. Not sure if I still have the code though.)
Mind you, you did say that division was 5-10 times faster. Have you considered doing a division, multiplication, and subtraction to simulated the mod? (Edit: Misunderstood the original post. I did think it was odd that division was faster than mod, they are the same operation.)
In your specific case, though, you are checking for a mod of 6. 6 = 2*3. So you could MAYBE get some small gains if you first checked if the least significant bit was a 0. Something like:
if((!(x & 1)) && (x % 3))
{
print("Fizz\n");
}
If you do that, though, I'd recommend confirming that you get any gains, yay for profilers. And doing some commenting. I'd feel bad for the next guy who has to look at the code otherwise.
You should really check the embedded device you need. All the assembly language I have seen (x86, 68000) implement the modulus using a division.
Actually, the division assembly operation returns the result of the division and the remaining in two different registers.
In the embedded world, the "modulus" operations you need to do are often the ones that break down nicely into bit operations that you can do with &, | and sometimes >>.
#Jeff V: I see a problem with it! (Beyond that your original code was looking for a mod 6 and now you are essentially looking for a mod 8). You keep doing an extra +1! Hopefully your compiler optimizes that away, but why not just test start at 2 and go to MAXCOUNT inclusive? Finally, you are returning true every time that (x+1) is NOT divisible by 8. Is that what you want? (I assume it is, but just want to confirm.)
For modulo 6 you can change the Python code to C/C++:
def mod6(number):
while number > 7:
number = (number >> 3 << 1) + (number & 0x7)
if number > 5:
number -= 6
return number
Not that this is necessarily better, but you could have an inner loop which always goes up to FIZZ, and an outer loop which repeats it all some certain number of times. You've then perhaps got to special case the final few steps if MAXCOUNT is not evenly divisible by FIZZ.
That said, I'd suggest doing some research and performance profiling on your intended platforms to get a clear idea of the performance constraints you're under. There may be much more productive places to spend your optimisation effort.
The print statement will take orders of magnitude longer than even the slowest implementation of the modulus operator. So basically the comment "slow on some systems" should be "slow on all systems".
Also, the two code snippets provided don't do the same thing. In the second one, the line
if(fizzcount >= FIZZ)
is always false so "FIZZ\n" is never printed.