In order to understand marching cube algorithm, I followed this page:
http://paulbourke.net/geometry/polygonise/
I have some questions:
what do bitwise & and | mean?, and how they will work with edge table to find the correct tringles?
if (grid.val[0] < isolevel) cubeindex |= 1;
if (edgeTable[cubeindex] & 1)
vertlist[0] =
VertexInterp(isolevel,grid.p[0],grid.p[1],grid.val[0],grid.val[1]);
Not going to read that text thats way to long. But here you can find how bit operators work https://en.wikipedia.org/wiki/Bitwise_operations_in_C. `
cubeindex |= 1 --> cubeindex = cubeindex | 1.
for example cubeindex = 26 (binary 11010) and 1 (binary 00001)
11010 | 00001 = 11011
Here you ad one 26->27.
For the followingedgeTable[cubeindex] & 1:
For example cubeindex = 17 (binary 10001) \
10001 & 00001 = 00001
This becomes 1. Used in the if statement, this just checks if the number edgeTable[cubeindex] contains the bit 00001 and returns true or false accordingly.
hope this helps :)
Cheers
Bitwise & (and) and | (or) operate on all the bits within an integer value. It operates on each bit independently, and is often used with you have a set of boolean truth values (aka flags) that indicate the state of various objects. It can also be used (as in your example) or test a specific flag while not modifying the others.
The bits in an integer represent powers of two values. If that bit is set to true, then that power of two is included in its value. If that bit is false, then it is no included. The least significant bit b0 represents 2^0, bit 1 represents 2^1, and so on.
For example, the value 5 = 101 in binary because 5 = 2^2 + 2^0 = 4 + 1 = 5
Bitwise OR works by setting the bits in the result to 1 if either of the operands contains a one in that position. Bitwise AND workds by setting each bit to 1 if and only if both operands have a 1 in that position.
For example:
Bitwise OR: 5 | 9 = 0101 | 1001 = 1101
Bitwise AND: 5 & 9 = 0101 & 1001 = 0001
Using these operators, if the set of bit values in cubeindex represent a set of states(true or false values), then you can use the same bitwise operands to test and if a specific state is true, while ignoring the other bits.
In your example:
cubeindex |= 1
sets the least significant bit in cubeindex to true (1) regardless of what it was before. This is because the bitwise OR of any bit value to 0, is the same bit value, while the bitwise OR of any bit value of 1, is always 1 and so is the same as setting that bit to 1 regardless of its previous stae. It is equivalent to:
cubeindex = cubeindex | 000000000000000001 = cubeindex with b0 set to 1
The logical AND is being used to test if the least significant bit of edgeTable[cubeindex] equals 1. The condition is only true if the b0 of this value is 1. Why? Because the other bits do not matter since they are being bitwise AND with zero, which is always zero.
edgeTable[cubeindex] & 1 = value of bit 0
Related
This question already has answers here:
Boolean operators vs Bitwise operators
(9 answers)
Closed 4 months ago.
What the different between logical operators and, or and bitwise analogs &, | in usage? Is there any difference in efficiency in various solutions?
Logical operators operate on logical values, while bitwise operators operate on integer bits. Stop thinking about performance, and use them for they're meant for.
if x and y: # logical operation
...
z = z & 0xFF # bitwise operation
Bitwise = Bit by bit checking
# Example
Bitwise AND: 1011 & 0101 = 0001
Bitwise OR: 1011 | 0101 = 1111
Logical = Logical checking or in other words, you can say True/False checking
# Example
# both are non-zero so the result is True
Logical AND: 1011 && 0101 = 1 (True)
# one number is zero so the result is False
Logical AND: 1011 && 0000 = 0 (False)
# one number is non-zero so the result is non-zero which is True
Logical OR: 1011 || 0000 = 1 (True)
# both numbers are zero so the result is zero which is False
Logical OR: 0000 || 0000 = 0 (False)
Logical operators are used for booleans, since true equals 1 and false equals 0. If you use (binary) numbers other than 1 and 0, then any number that's not zero becomes a one. Ex: int x = 5; (101 in binary) int y = 0; (0 in binary) In this case, printing x && y would print 0, because 101 was changed to 1, and 0 was kept at zero: this is the same as printing true && false, which returns false (0). On the other hand, bitwise operators perform an operation on every single bit of the two operands (hence the term "bitwise"). Ex: int x = 5; int y = 8; printing x | y (bitwise OR) would calculate this: 000101 (5)| 1000 (8) ----------- = 1101 (13) Meaning it would print 13.
I am working on an Udacity quiz for their parallel programming course. I am pretty stuck on how I should start on the assignment because I am not sure if I understand it correctly.
For the assignment (in code) we are given two arrays and array on values and an array of positions. We are supposed to sort the array of values with a parallelized radix sort, along with setting the positions correctly too.
I completely understand radix sort and how it works. What I don't understand is how they want us to implemented it. Here is the template given to start the assignment
//Udacity HW 4
//Radix Sorting
#include "reference_calc.cpp"
#include "utils.h"
/* Red Eye Removal
===============
For this assignment we are implementing red eye removal. This is
accomplished by first creating a score for every pixel that tells us how
likely it is to be a red eye pixel. We have already done this for you - you
are receiving the scores and need to sort them in ascending order so that we
know which pixels to alter to remove the red eye.
Note: ascending order == smallest to largest
Each score is associated with a position, when you sort the scores, you must
also move the positions accordingly.
Implementing Parallel Radix Sort with CUDA
==========================================
The basic idea is to construct a histogram on each pass of how many of each
"digit" there are. Then we scan this histogram so that we know where to put
the output of each digit. For example, the first 1 must come after all the
0s so we have to know how many 0s there are to be able to start moving 1s
into the correct position.
1) Histogram of the number of occurrences of each digit
2) Exclusive Prefix Sum of Histogram
3) Determine relative offset of each digit
For example [0 0 1 1 0 0 1]
-> [0 1 0 1 2 3 2]
4) Combine the results of steps 2 & 3 to determine the final
output location for each element and move it there
LSB Radix sort is an out-of-place sort and you will need to ping-pong values
between the input and output buffers we have provided. Make sure the final
sorted results end up in the output buffer! Hint: You may need to do a copy
at the end.
*/
void your_sort(unsigned int* const d_inputVals,
unsigned int* const d_inputPos,
unsigned int* const d_outputVals,
unsigned int* const d_outputPos,
const size_t numElems)
{
}
I specifically don't understand how those 4 steps end up sorting the array.
So for the first step, I am supposed to create a histogram of the "digits" (why is that in quotes..?). So given a input value n I need to make a count of the 0's and 1's into a histogram. So, should step 1 create an array of histograms, one for each input value?
And well, for the rest of the steps it breaks down pretty quickly. Could someone show me how these steps are supposed to implement a radix sort?
The basic idea behind a radix sort is that we will consider each element to be sorted digit by digit, from least significant to most significant. For each digit, we will move the elements so that those digits are in increasing order.
Let's take a really simple example. Let's sort four quantities, each of which have 4 binary digits. Let's choose 1, 4, 7, and 14. We'll mix them up and also visualize the binary representation:
Element # 1 2 3 4
Value: 7 14 4 1
Binary: 0111 1110 0100 0001
First we will consider bit 0:
Element # 1 2 3 4
Value: 7 14 4 1
Binary: 0111 1110 0100 0001
bit 0: 1 0 0 1
Now the radix sort algorithm says we must move the elements in such a way that (considering only bit 0) all the zeroes are on the left, and all the ones are on the right. Let's do this while preserving the order of the elements with a zero bit and preserving the order of the elements with a one bit. We could do that like this:
Element # 2 3 1 4
Value: 14 4 7 1
Binary: 1110 0100 0111 0001
bit 0: 0 0 1 1
The first step of our radix sort is complete. The next step is to consider the next (binary) digit:
Element # 3 2 1 4
Value: 4 14 7 1
Binary: 0100 1110 0111 0001
bit 1: 0 1 1 0
Once again, we must move elements so that the digit in question (bit 1) is arranged in ascending order:
Element # 3 4 2 1
Value: 4 1 14 7
Binary: 0100 0001 1110 0111
bit 1: 0 0 1 1
Now we must move to the next higher digit:
Element # 3 4 2 1
Value: 4 1 14 7
Binary: 0100 0001 1110 0111
bit 2: 1 0 1 1
And move them again:
Element # 4 3 2 1
Value: 1 4 14 7
Binary: 0001 0100 1110 0111
bit 2: 0 1 1 1
Now we move to the last (highest order) digit:
Element # 4 3 2 1
Value: 1 4 14 7
Binary: 0001 0100 1110 0111
bit 3: 0 0 1 0
And make our final move:
Element # 4 3 1 2
Value: 1 4 7 14
Binary: 0001 0100 0111 1110
bit 3: 0 0 0 1
And the values are now sorted. This hopefully seems clear, but in the description so far we've glossed over the details of things like "how do we know which elements to move?" and "how do we know where to put them?" So let's repeat our example, but we'll use the specific methods and sequence suggested in the prompt, in order to answer these questions. Starting over with bit 0:
Element # 1 2 3 4
Value: 7 14 4 1
Binary: 0111 1110 0100 0001
bit 0: 1 0 0 1
First let's build a histogram of the number of zero bits in bit 0 position, and the number of 1 bits in bit 0 position:
bit 0: 1 0 0 1
zero bits one bits
--------- --------
1)histogram: 2 2
Now let's do an exclusive prefix-sum on these histogram values:
zero bits one bits
--------- --------
1)histogram: 2 2
2)prefix sum: 0 2
An exclusive prefix-sum is just the sum of all preceding values. There are no preceding values in the first position, and in the second position the preceding value is 2 (the number of elements with a 0 bit in bit 0 position). Now, as an independent operation, let's determine the relative offset of each 0 bit amongst all the zero bits, and each one bit amongst all the one bits:
bit 0: 1 0 0 1
3)offset: 0 0 1 1
This can actually be done programmatically using exclusive prefix-sums again, considering the 0-group and 1-group separately, and treating each position as if it has a value of 1:
0 bit 0: 1 1
3)ex. psum: 0 1
1 bit 0: 1 1
3)ex. psum: 0 1
Now, step 4 of the given algorithm says:
4) Combine the results of steps 2 & 3 to determine the final output location for each element and move it there
What this means is, for each element, we will select the histogram-bin prefix sum value corresponding to its bit value (0 or 1) and add to that, the offset associated with its position, to determine the location to move that element to:
Element # 1 2 3 4
Value: 7 14 4 1
Binary: 0111 1110 0100 0001
bit 0: 1 0 0 1
hist psum: 2 0 0 2
offset: 0 0 1 1
new index: 2 0 1 3
Moving each element to its "new index" position, we have:
Element # 2 3 1 4
Value: 14 4 7 1
Binary: 0111 1110 0111 0001
Which is exactly the result we expect for the completion of our first digit-move, based on the previous walk-through. This has completed step 1, i.e. the first (least-significant) digit; we still have the remaining digits to process, creating a new histogram and new prefix sums at each step.
Notes:
Radix-sort, even in a computer, does not have to be done based strictly on binary digits. It's possible to construct a similar algorithm with digits of different sizes, perhaps consisting of 2,3, or 4 bits.
One of the optimizations we can perform on a radix sort is to only sort based on the number of digits that are actually meaningful. For example, if we are storing quantities in 32-bit values, but we know that the largest quantity present is 1023 (2^10-1), we need not sort on all 32 bits. We can stop, expecting a proper sort, after proceeding through the first 10 bits.
What does any of this have to do with GPUs? In so far as the above description goes, not much. The practical application is to consider using parallel algorithms for things like the histogram, the prefix-sums, and the data movement. This decomposition of radix-sort allows one to locate and use parallel algorithms already developed for these more basic operations, in order to construct a fast parallel sort.
What follows is a worked example. This may help with your understanding of radix sort. I don't think it will help with your assignment, because this example performs a 32-bit radix sort at the warp level, for a single warp, ie. for 32 quantities. But a possible advantage from an understanding point of view is that things like histogramming and prefix sums can be done at the warp level in just a few instructions, taking advantage of various CUDA intrinsics. For your assignment, you won't be able to use these techniques, and you will need to come up with full-featured parallel prefix sums, histograms, etc. that can operate on an arbitrary dataset size.
#include <stdio.h>
#include <stdlib.h>
#define WSIZE 32
#define LOOPS 100000
#define UPPER_BIT 31
#define LOWER_BIT 0
__device__ unsigned int ddata[WSIZE];
// naive warp-level bitwise radix sort
__global__ void mykernel(){
__shared__ volatile unsigned int sdata[WSIZE*2];
// load from global into shared variable
sdata[threadIdx.x] = ddata[threadIdx.x];
unsigned int bitmask = 1<<LOWER_BIT;
unsigned int offset = 0;
unsigned int thrmask = 0xFFFFFFFFU << threadIdx.x;
unsigned int mypos;
// for each LSB to MSB
for (int i = LOWER_BIT; i <= UPPER_BIT; i++){
unsigned int mydata = sdata[((WSIZE-1)-threadIdx.x)+offset];
unsigned int mybit = mydata&bitmask;
// get population of ones and zeroes (cc 2.0 ballot)
unsigned int ones = __ballot(mybit); // cc 2.0
unsigned int zeroes = ~ones;
offset ^= WSIZE; // switch ping-pong buffers
// do zeroes, then ones
if (!mybit) // threads with a zero bit
// get my position in ping-pong buffer
mypos = __popc(zeroes&thrmask);
else // threads with a one bit
// get my position in ping-pong buffer
mypos = __popc(zeroes)+__popc(ones&thrmask);
// move to buffer (or use shfl for cc 3.0)
sdata[mypos-1+offset] = mydata;
// repeat for next bit
bitmask <<= 1;
}
// save results to global
ddata[threadIdx.x] = sdata[threadIdx.x+offset];
}
int main(){
unsigned int hdata[WSIZE];
for (int lcount = 0; lcount < LOOPS; lcount++){
unsigned int range = 1U<<UPPER_BIT;
for (int i = 0; i < WSIZE; i++) hdata[i] = rand()%range;
cudaMemcpyToSymbol(ddata, hdata, WSIZE*sizeof(unsigned int));
mykernel<<<1, WSIZE>>>();
cudaMemcpyFromSymbol(hdata, ddata, WSIZE*sizeof(unsigned int));
for (int i = 0; i < WSIZE-1; i++) if (hdata[i] > hdata[i+1]) {printf("sort error at loop %d, hdata[%d] = %d, hdata[%d] = %d\n", lcount,i, hdata[i],i+1, hdata[i+1]); return 1;}
// printf("sorted data:\n");
//for (int i = 0; i < WSIZE; i++) printf("%u\n", hdata[i]);
}
printf("Success!\n");
return 0;
}
The methodology that #Robert Crovella gives is absolutely correct and very helpful. It is mildly different than the process that they explain in the Udacity videos. I'll record one iteration of their method, watchable here, in this answer, jumping off from Robert Crovella's example:
Element # 1 2 3 4
Value: 7 14 4 1
Binary: 0111 1110 0100 0001
LSB: 1 0 0 1
Predicate: 0 __1__ __1__ 0
Pred. Scan: 0 __0__ __1__ 2
Number of ones in predicate: 2
!Predicate:__1__ 0 0 __1__
!Pred. Scan: 0 1 1 1
Offset for !Pred. Scan = Number of ones in predicate = 2
!Pred. Scan + Offset:
__2__ 3 3 __3__
Final indexes to move values after 1 iteration (on LSB):
2 0 1 3
Values after 1 iteration (on LSB):
14 4 7 1
I placed emphasis (__ __) on the values that indicate or contain the index to move the value to.
Terms (from Udacity video):
LSB = least significant bit
Predicate (for LSB): (x & 1) == 0
for the next significant bit: (x & 2) == 0
for the one after that: (x & 4) == 0
and so on, with more left shifting (<<)
Pred. Scan = Predicate Scan = Predicate exclusive prefix sum
!Pred. = bits of predicate flipped (0->1 and 1->0)
Number of ones in predicate
note that this is not necessarily the last entry in the scan, you can instead get this value (sum/reduction of the predicate) as an intermediate of the Blelloch scan
A summary of the above is:
Get the predicate of your list (bit in common, starting from the LSB)
Scan the predicate, and record the sum of the predicate in the process
Blelloch Scan on the GPU
note that your predicate will be of arbitrary size, so read the section on Blelloch Scan for arrays of arbitrary instead of 2^n size
Flip bits of the predicate, and scan that
Move the values in your array with the following rule:
For the ith element in the array:
if the ith predicate is TRUE, move the ith value to the index in the ith element of the predicate scan
else, move the ith value to the index in the ith element of the !Predicate scan plus the sum of the Predicate
Move to the next significant bit (NSB)
For reference, you can consult my solution for this HW assignment in CUDA.
How do you find the largest power of 2 a number is divisible by using logic function
for example 144 is divisible by 16 which is 2^4.
How would one do this.
I know 144 in binary is 1001 0000 and I have to use a bitwise function.
But what would I use (and or andn orn ?) or perhaps something else and what can I use as my mask?
I know you have to look at the right most number to tell if it is divisible by 2.
Any help is appreciated
I would go with n & -n or with n & (~n + 1), in case you are worried about running across one's complement arithmetic, given the latter works with both arithmetics.
E.g.,
> 144 & (~144 + 1)
< 16
Now a short explanation.
The bitwise NOT (i.e., ~ operator) of a number n gives -(n + 1). It inverts all the bits of n. The number 2 is represented by 00000010 while its negation is 11111101 which equals to -3 (i.e., , see the two's complement representation of signed numbers).
Do not to confuse it with logical negation.
E.g., ~144 = -(144 + 1) = -145.
The bitwise AND (i.e., & operator) compares two bits of the inputs and generates a result of 1 if both are 1, otherwise it returns 0.
Now the main topic.
This is an old tricks that gives the highest power of 2 that n is divisible by. This means that it returns a number with a single one bit, specifically the bottom bit that was set in n.
For example the binary representation of 144 is 010010000. Its bottom 1 bit is the bit in fourth position (counting backward from right and starting at position 0). Thus the higher power of 2 that divides 144 is 16 (i.e., 00010000).
144 & (~144 + 1) = 144 & -144 = 16
16 & ( ~16 + 1) = 16 & - 16 = 16
10 & ( ~10 + 1) = 10 & - 10 = 2
12 & ( ~12 + 1) = 12 & - 12 = 4
11 & ( ~11 + 1) = 11 & - 11 = 1
3 & ( ~ 3 + 1) = 3 & - 3 = 1
Note that if n is not divisible by any power of 2 it returns 1.
Why it works?
The negative of n is produced by inverting its bits via ~, then adding 1 (again, see two's complement definition). This sum causes every 1 bit (starting from the bottom) to overflow until a 0 bit is encountered (let us call it the bit x). Here the overflow process stops, leaving remaining bits (those beyond the current x bit) unchanged. Thus performing & between n and its inverse will result in a binary string containing only the x bit.
An example follows.
010010000 | +144 ~
----------|-------
101101111 | -145 +
1 |
----------|-------
101110000 | -144
101110000 | -144 &
010010000 | +144
----------|-------
000010000 | 16
This is task from algorithms book.
The thing is that I completely don't know where to start!
Trace the following non-recursive algorithm to generate the binary reflexive
Gray code of order 4. Start with the n-bit string of all 0’s.
For i = 1, 2, ... 2^n-1, generate the i-th bit string by flipping bit b in the
previous bit string, where b is the position of the least significant 1 in the
binary representation of i.
So I know the Gray code for 1 bit should be 0 1, for 2 00 01 11 10 etc.
Many questions
1) Do I know that for n = 1 I can start of with 0 1?
2) How should I understand "start with the n-bit string of all 0's"?
3) "Previous bit string"? Which string is the "previous"? Previous means from lower n-bit? (for instance for n=2, previous is the one from n=1)?
4) How do I even convert 1-bit strings to 2-bit strings if the only operation there is to flip?
This confuses me a lot. The only "human" method I understand so far is: take sets from lower n-bit, duplicate them, invert the 2nd set, add 0's to every element in 1st set, add 1's do every elements in 2nd set. Done (example: 0 1 -> 0 1 | 0 1 -> 0 1 | 1 0 -> 00 01 | 11 10 -> 11 01 11 10 done.
Thanks for any help
The answer to all four your questions is that this algorithm does not start with lower values of n. All strings it generates have the same length, and the i-th (for i = 1, ..., 2n-1) string is generated from the (i-1)-th one.
Here is the fist few steps for n = 4:
Start with G0 = 0000
To generate G1, flip 0-th bit in G0, as 0 is the position of the least significant 1 in the binary representation of 1 = 0001b. G1 = 0001.
To generate G2, flip 1-st bit in G1, as 1 is the position of the least significant 1 in the binary representation of 2 = 0010b. G2 = 0011.
To generate G3, flip 0-th bit in G2, as 0 is the position of the least significant 1 in the binary representation of 3 = 0011b. G3 = 0010.
To generate G4, flip 2-nd bit in G3, as 2 is the position of the least significant 1 in the binary representation of 4 = 0100b. G4 = 0110.
To generate G5, flip 0-th bit in G4, as 0 is the position of the least significant 1 in the binary representation of 5 = 0101b. G5 = 0111.
Using the classic code snippet:
if (x & (x-1)) == 0
If the answer is 1, then it is false and not a power of 2. However, working on 5 (not a power of 2) and 4 results in:
0001 1111
0001 1111
0000 1111
That's 4 1s.
Working on 8 and 7:
1111 1111
0111 1111
0111 1111
The 0 is first, but we have 4.
In this link (http://www.exploringbinary.com/ten-ways-to-check-if-an-integer-is-a-power-of-two-in-c/) for both cases, the answer starts with 0 and there is a variable number of 0s/1s. How does this answer whether the number is a power of 2?
You need refresh yourself on how binary works. 5 is not represented as 0001 1111 (5 bits on), it's represented as 0000 0101 (2^2 + 2^0), and 4 is likewise not 0000 1111 (4 bits on) but rather 0000 0100 (2^2). The numbers you wrote are actually in unary.
Wikipedia, as usual, has a pretty thorough overview.
Any power of two number can be represent in binary with a single 1 and multiple 0s.
eg.
10000(16)
1000(8)
100(4)
If you subtract 1 from any power of two number, you will get all 1s to the right of where the original one was.
10000(16) - 1 = 01111(15)
ANDing these two numbers will give you 0 every time.
In the case of a non-power of two number, subtracting one will leave at least one "1" unchanged somewhere in the number like:
10010(18) - 1 = 10001(17)
ANDing these two will result in
10000(16) != 0
Keep in mind that if x is a power of 2, there is exactly 1 bit set. Subtract 1, and you know two things: the resulting value is not a power of two, and the bit that was set is no longer set. So, when you do a bitwise and &, every bit that was set in x is not unset, and all the bits in (x-1) that are set must be matched against bits not set in x. So the and of each bit is always 0.
In other words, for any bit pattern, you are guaranteed that (x&(x-1)) is zero.
((n & (n-1)) == 0)
It checks whether the value of “n” is a power of 2.
Example:
if n = 8, the bit representation is 1000
n & (n-1) = (1000) & ( 0111) = (0000)
So it return zero only if its value is in power of 2.
The only exception to this is ‘0’.
0 & (0-1) = 0 but ‘0’ is not the power of two.
Why does this make sense?
Imagine what happens when you subtract 1 from a string of bits. You read from left to right,
turning each 0 to a 1 until you hit a 1, at which point that bit is flipped:
1000100100 -> (subtract 1) -> 1000100011
Thus, every bit, up through the first 1, is flipped. If there’s exactly one 1 in the number, then every bit (other than the leading zeros) will be flipped. Thus, n & (n-1) == 0 if there’s exactly one 1. If there’s exactly one 1, then it must be a power of two.