In this file I will write descriptions of the exafmm ‘learning’ codes and my understanding of them. I have been tasked with understanding the code and porting it to Ruby, my favorite language. We shall start from the first tutorial, i.e. 0_tree. You can find the full Ruby code here.
- exafmm2d.h and step1.cxx
This program simply populates some bodies with random numbers, creates a hypothetical X and Y axes and figures out the quadrant of each of the bodies.
Each of the nodes of the tree have a maximum of 4 immediate children. We first initialize 100
struct Body objects, and then set the X and Y co-ordinates of each of them to a random number between 0 and 1.
In order to actually build the tree we follow the following steps:
- First get the bounds between which the random numbers lie. That is, we figure out the min and max random number that is present in the bodies.
- We then get a ‘center’ and a ‘radius’. This is useful for creating ‘quadrants’ and partitioning points into different quandrants in later steps. The center is calculated by adding the min and max numbers (which we treat as the diameter) and dividing by 2. This step is necessary since there is no ‘square’ space that can be partitioned into multiple spaces like there was in the lecture series. The way of calculating the radius
r0is a little peculiar. It does not use the distance formula, its main purpose is….
- And then simply count the bodies in each quadrant and display them.
The body is represented as the Ruby class
There is an interesting way of knowing the quadrant in this code. It goes like this:
Above code basically plays with 0 and 1 and returns a number between 0 and 3 as the correct quadrant number.
This code basically takes the bodies created in the previous step, counts the number of bodies in each quadrant and sorts them by quadrant.
The new steps introduced in this program can be summarized as follows:
- Count the bodies in each quadrant and store the count in an array. The
sizearray in case of the Ruby implementation.
- In the next step we successively add the number of elements in each quadrant so that it gives us the offset value at which elements from a new quadrant will start in the
bodiesArray (of course, after it is sorted).
- We then sort the bodies according to the quadrant that they belong to. Something peculiar that I notice about this part is that
counter[quadrant]also gets incremented after each iteration for sorting. Why is this the case even though the counters have been set to the correct offsets previously?
This program introduces a new method called
buildTree, inside of which we will actually build the tree. It removes some of the sorting logic from
main and puts it inside
buildTree function performs the following functions:
- Most of the functions relating to sorting etc are same. Only difference is that there is in-place sorting of the
bodiesarray and the
bufferarray does not store elements anymore.
- A new function introduced is that we re-calculate the center and the radius based on sorted co-ordinates. This is done because we want new center and radii for the children.
buildTreefunction is called recursively such that the quadrants are divided until a point is reached where the inner most quadrant in the hierarchy does not contain more than 4 elements.
There is an interesting piece of code in the part for calculating new center and radius:
In the above code, there is some bit shifting and interleaving taking place whose prime purpose is to split the quadrant number into X and Y dimension and then using this to calculate the center of the child cell.
Another piece of code is this:
counter = Array.new 4, start 1.upto(3) do |i| counter[i] = size[i-1] + counter[i-1] end # sort bodies and store them in buffer buffer = bodies.dup start.upto(finish-1) do |n| quadrant = quadrant_of x0, buffer[n] bodies[counter[quadrant]] = buffer[n] counter[quadrant] += 1 end
In the above code, the
counter variable is first used to store offsets of the elements in different quadrants. In the next loop it is in fact a counter for that stores in the index of the body that is currently under consideration.
In this step we use the code written in the previous steps and actually build the tree. The tree is built recursively by splitting into quadrants and then assigning them to cells based on the quadrant. The ‘tree’ is actually stored in an array.
The cells are stored in a C++ vector called
Cell struct, I wonder why the body is stored as a pointer and not a variable.
Implementation in the Ruby code, like saving the size of an Array during a recursive call is slightly different since Ruby does not support pointers, but the data structures and overall code is more or less a direct port.
These codes are for traversal of the tree that was created in the previous step. The full code can be found in 1_traversal.rb file.
This step implements the P2M and M2M passes of the FMM.
One major difference between the C++ and Ruby implementation is that since Ruby does not have pointers, I
have used the array indices of the elements instead. For this purpose there are two attributes in the
Cell class called
first_child_index that is responsible for holding the index in the
about the location of the first child of this cell, and the second
first_body_index which is responsible for holding the index of the body in the
This step does this by introducing a method called
upwardPass which iterates through nodes and thier children and computes the P2M and M2M kernels.
This step implements the rest of the kernels i.e. M2L, L2L, L2P and P2P. It also introduces two new methods
downward_pass that calculates the local forces from other local forces and L2P interactions and
horizontal_pass that calculates the inter-particle interactions and m2l.
No special code as such over here, its just the regular FMM stuff.
This code is quite different from the previous two. While the previous programs were mostly retricted to a single file, this program substantially increases complexity and spreads the implementation across several files. We start using 3 dimensional co-ordinates too.
In this code, we start to make a move towards spherical co-ordinate system to represent the particles in 3D. A few notable algorithms taken from some research papers have been implemented in this code.
Lets describe each file and see what implementation lies inside
kernel.h header file implemenets all the FMM kernels. It also implements two special functions called
evalLocal that evaluate the multipoles and local expansion for spherical co-ordinates using the actual algorithm that is actually used in exafmm. An implementation of this algorithm can be found on page 16 of the paper “Treecode and fast multipole method for N-body simulation with CUDA” by Yokota sensei. A preliminary implementation of this algorithm can be found in “A Fast Adaptive Multipole Algorithm in Three Dimensions” by Cheng.
The Ruby implementation of this file is in
I will now describe this algorithm here best I can:
This is a vector that defines the spherical harmonics of degree n and order m. A primitive version for computing this exists in the paper by Cheng and a newer, faster version in the paper by Yokota.
Spherical harmonics allow us to define series of a function in 3D rather in 1D that is usually the case for things like the expansion of sin(x). They are representations of functions on the surface of a sphere instead of on a circle, which is usually the case with other 2D expansion functions. They are like the Fourier series of the sphere. This article explains the notations used nicely.
The order (n) and degree (m) correspond to the order and degree of the Legendre polynomial that is used for obtaining the spherical harmonic. n is an integer and m goes from 0..n.
For causes of optimization, the values stored inside
ynm are not the ones that correspond to the spherical harmonic, but are values that yield optimized results when the actual computation happens.
Historical origins of kernel.h
This file is a new and improved version of the laplace.h file from the exafmm-alpha repo. Due to the enhacements made, the code in this file performs calculations that are significantly more accurate than those in laplace.h.
laplace.h consists of a C++ class inside which all the functions reside, along with a constructor that computes pre-determined values for subsequent computation of the kernels. For example, in the constructor of the
Kernel class, there is a line like so:
Anm[nm] = oddOrEven(n)/std::sqrt(fnmm*fnpm);
This line is computing the value of as is given by Cheng’s paper (equation 14). This value is used in M2L and L2L kernels later. However, this value is never directly computed in the new and optimized
kernel.h file. Instead, it modifies the computation of the
Ynm vector such that it no longer becomes necessary to involve the
Anm term in any kernel computation.
This function converts cartesian co-ordinates in (X,Y,Z) to spherical co-ordinates involving
radius is simply the square root of the norm of the co-ordinates (norm is defined as the sum of squares of the co-ordinates in
evalMultipole simple implementation
This algorithm calculates the multipole of a cell. It uses spherical harmonics so that net force of the forces inside a sphere and can be estimated on the surface of the sphere, which can then be treated as a single body for estimating forces.
The optimizations that are presented in the
kernel.h version of this file are quite complex to understand since they look quite different from the original equation.
For code that is still sane and easier to read, head over to the laplace.h file in exafmm-alpha. The explanations that follow for now are from this file. We will see how the same functions in
kernel.h have been modified to make computation faster and less dependent on large number divisions which reduce the accuracy of the system.
evalMultipole function basically tries to populate the
Ynm array with data that is computed with the following equation:
It starts with evaluating terms that need not be computed for every iteration of
n, and computes those terms in the outer loop itself. The terms in the outer loop correspond to the condition
m=n. The first of these is the exponential term .
After this is a curious case of computation of some indexes called
nmn. These are computed as follows:
The corresponding index calculation for the inner loop is like this:
This indexes the
Ynm array. This is done because we are visualizing the Ynm array as a pyramid whose base spans from
m and who height is
n. A rough visualization of this pyramid would be like so:
-m ---------- m n 10 11 12 13 14 | 6 7 8 9 | 3 4 5 | 1 2 V 0
The above formulas will give the indexes for each half of the pyramid. Since the values of one half of the pyramid are conjugates of the other half, we can only iterate from
m<P and use this indexing method for gaining the index of the other half of the pyramid.
Now let us talk about the evaluation of the Associated Legendre Polynomial , where m is the order of the differential equation and n is the degree. The Associated Legendre Polynomial is the solution to the Associated Legendre Equation. The Legendre polynomial can be expressed in terms of the Rodrigues form for computation without dependence on the simple Legendre Polynomial . However, due to the factorials and rather large divisions that need to be performed to compute the Associated Legendre polynomial in this form, computing this equation for large values of m and n quickly becomes unstable. Therefore, we use a recurrence relation of the Polynomial in order to compute different values.
The recurrence relation looks like so:
This is expressed in the code with the following line:
p = (x * (2 * n + 1) * p1 - (n + m) * p2) / (n - m + 1)
It can be seen that
p is equivalent to ,
p1 is equivalent to and
p2 is equivalent to . This convention is followed everywhere in the code.
Observe that the above equation requires the value of P for n-1 and n+1 to be computed so that the value for P at n can be computed. Therefore, we first set m=m+1 and then compute which can be expressed like this:
The above equation is expressed by the following line in the code:
p = x * (2 * m + 1) * p1
If you read the code closely, you will see that just at the beginning of the
evalMultipole function, we initialize
p1 = 1 the first time the looping is done. This is because when
p1 at the first instance is identified with
m = 0, and we substitute
m=0 in this equation:
We will get .
When you look at the code initially, there might be some confusion regarding the significance of having to
rhon. This is written because each term of
Ynm depends on a particular power of
rho raised to
n. So just before the inner loop, you can see the line
rhon = rhom, which basically reduces the number of times that
rho needs to be multiplied since the outer loop’s value of
rho is already set to what it should be for that particular iteration.
Finally, see that there is a line right after the inner loop which reads like this:
pn = -pn * fact * y
This line is for calculating the value of
p1 or after the first iteration of the loop. Since the second factorial term in the equation basically just deals with odd numbers, the calculation of this term can be simplified by simply incrementing by
fact += 2. The
y term in the above equation is in fact
sin(alpha) (defined at the top of this function). This is because, if you see the original equation, you will see that the third term is , and x is in fact
cos(alpha). Therefore, using the trigonometric equation, we can say simply substitute the entire term with
evalMultipole optimized implementation
Now that a background of the basic implementation of
evalMultipole has been established, we can move over to understanding the code that is placed inside the kernel.h file of the
exafmm/learning branch. This code is more optimized and can compute results with much higher accuracy than the code that is present in the
exafmm-alpha repo that we previously saw. The main insipiration for this code come’s from the Treecode paper posted above.
In this code, most of the stuff relating to indexing and calculation of the powers of
rho is pretty much the same. However, there are some important changes with regards to the computation of the values that go inside the
Ynm array. This change is also reflected in the subsequent kernels.
For instance, this new function derives an important term from Epton’s paper (equation
The Ruby implementation is here.
A major difference exists between computation of M2M in the
This file defines a new custom type for storing 1D vectors called
vec as a C++ class. It also defines various functions that can be used on vectors like
exp and other simple arithmetic.
The Ruby implementation of this file is in
exafmm2d.h and step1.cxx
Shows a very simple preliminary implementation of the actuall exafmm code. Mostly useful for understanding purpose only.