Large Integer Multiplication on

Massively Parallel Processors

 

Barry S. Fagin

Thayer School of Engineering

Dartmouth College

Hanover, NH 03755

barry.fagin@dartmouth.edu

 

 

 

 

 

ABSTRACT

 

We present results of a technique for multiplying large integers using the Fermat Number Transform. When the Fermat Number Transform was first proposed, word length constraints limited its effectiveness. Despite the development of multidimensional techniques to extend the length of the FNT, the relatively small word length of existing machines made the transform of little more than academic interest. The emergence of new computer architectures, however, has made the Fermat Number Transform more attractive. On machines like the Connection Machine, for example, we may implement the Fermat Number Transform without regard to word length considerations.

 

We present a convolution algorithm on a massively parallel processor, based on the Fermat Number Transform. We present some examples of the tradeoffs between modulus, interprocessor communication steps, and input size. We then discuss the application of this algorithm in the multiplication of large integers, and report performance results on a Connection Machine. Our results show multiplication times ranging from about 50 milliseconds for 2K-bit integers to 2600 milliseconds for 8M-bit integers.

 


The Fermat Number Transform

 

The Fermat Number Transform, or FNT, is computationally similar to an FFT, but is performed in the ring of integers modulo some integer M = 2B + 1. Numbers of this form are called Fermat Numbers. We refer to a ring of integers modulo a Fermat Number as a Fermat ring. Fermat rings are particularly desirable for transform operations because multiplication of some binary integer i by 2n in a Fermat ring is equivalent to a left rotation of i by n bits, followed by an inversion of the lower n bits.

 

There are well known difficulties in using a Fermat ring for computing digital convolution [1]. For computing transforms in a ring modulo 2B+1, the maximum possible transform length is 4B. Since B is the number of bits necessary to represent integers in the input sequences, it is usually the word length of the host computer. Most computers have words no larger than 32 bits, making the use of one dimensional Fermat Number Transforms impractical for large input sequences.

 

Agarwal and Burrus [2] discuss extending the transform length of the FNT through multidimensional techniques. For a given sequence length N, a one dimensional convolution with B proportional to N can be computed using a d-dimensional convolution with B proportional to the dth root of N. This technique dramatically extends the range of the FNT to the point where realistic input sizes become feasible. We use this technique in our work.

 

Other minor difficulties remain. Arithmetic in a Fermat ring mod 2B+1 on a machine with B-bit words is complicated by the necessity to represent 2B+1 numbers 0, 1, ... 2B with B bits. Several techniques have been proposed to solve this problem. For our algorithm, we adopt the technique of 1's reduced notation, first proposed by Leibowitz [6]. Briefly, a binary number i such that 1 <= i < 2B is represented by i-1 in 1's reduced notation. 0 is represented by 2B. This means that 0 can be identified by a 1 in the B+1st bit, and special procedures invoked to handle this as a special case. If neither operand is zero, multiplication of a 1's reduced number by a power of two is equivalent to a rotation and inversion of bits. Addition of non-zero operands is similar to binary addition, with the complement of the carry added to the result. Since addition and multiplication when one or both of the operands are zero are trivial operations, the added complexity of this notation is minimal.

 

We note that the technological framework of computer design has evolved considerably since these ideas were first proposed. For new computer architectures like the Connection Machine, word length considerations are not important. Thus we may implement the Fermat Number Transform on current multiprocessors with much less emphasis on word length considerations. Our concern will instead be focused on reducing the interprocessor communication time and the number of processors, as we will see shortly.

 

Digital Convolution Using Multidimensional FNT's

 

We first present an analysis of the tradeoffs involved in using multidimensional FNT's to compute digital convolution. Let us assume two input sequences of total bit length N, which we may partition into digits of some size P >= 1 and convolve using the FNT. For ease of presentation, we assume N, P, and B are powers of two. Lower case letters will denote the base 2 logarithm of their upper case equivalents.

 

We begin with two sequences of N bits each. We wish to partition each sequence into P-bit digits, and then compute their convolution modulo M = 2B+1 using a d-dimensional FNT. For a given N, we wish to understand the relationships between d, B, and P. In order for the convolution to be uniquely representable in the Fermat ring mod M = 2B+1, the maximum value of a convolution product must be less than or equal to the largest integer in the ring. Each digit contains P bits, The largest any single digit can be is 2P-1. Each term in a convolution product can be no greater than (2P-1)2, and there are at most N/P terms contributing to a single convolution product.

This implies that

 

(2P-1)2(N/P) <= 2B (I)

 

as shown below:

 

 

Figure 1: Convolution Products of N/P P-bit digits

 

 

Taking the log of both sides, we have

 

2 log (2P-1) + n - p <= B (II)

 

It would be convenient to approximate log(2P-1) by P. Letting e = P - log(2P-1), we have

 

2(P-e) + n - p <= B (III)

 

or equivalently

 

2P + n - p <= B + 2e (IV)

 

For P = 1 or a power of two > 2, e is positive and we may thus neglect it in IV. If P=2, e is approximately -.58. Neglecting e will give the relation n <= B - 3, while including it gives n <= B-4.16. Taking n = B-3, the largest value that satisfies the approximate inequality but not the true one, we plug P=2 and N = 2n = 2B-3 back into (I) to obtain

 

9(2B-4) <= 2B (for P=2, N=2B-3) (V)

 

This inequality simplifies to 9 <= 16. Therefore we may neglect e in (IV) for the case of P=2 as well. This gives:

 

2P + n - p <= B (VI)

 

Suppose we now employ a d-dimensional FNT to compute the convolution of the input sequences, d > 1. We continue the analysis presented in [2]. Although only 2-dimensional transforms are discussed, the process of extension to higher dimensions is straightforward. Employing a d-dimensional transform extends a 1-dimensional array into a d-dimensional hypercube, doubling the length along d-1 axes. Thus a sequence of length L digits is transformed into a d-dimensional cube of 2d-1*L elements. For convenience, we assume L is a power of 2. The lengths of each axis are distributed as evenly as possible, again as powers of 2. To perform a 4-dimensional FNT on a sequence of 210 elements, for example, would require a total of 23*210 = 213 processors, arranged in a 24 x 23 x 23 x 23 4-cube.

 

Let n-p, the logarithm of the sequence length in digits, be congruent to r mod d. We distinguish 3 cases: r = 0, r = 1, and r >= 2. If r = 0, there will be d-1 long axes with log length equal to (n-p)/d _+1, and one short axis of length (n-p)/d. If r = 1, all axes are of log length (n-p)/d _ + 1; in this case we say the transform is balanced. If r >= 2, d-1 axes must have their lengths doubled, but d-r of them can be doubled without increasing the longest axis length. Any axes remaining must have their lengths doubled. Thus d-1-(d-r) = r-1 long axes will have log length (n-p)/d_+2, and d-r+1 axes will have log length (n-p)/d_+1.

 

For example, suppose that n-p = 10, corresponding to a sequence of 1K digits, and that d=4. Then r = n-p mod d = 2. We can split 210 into 23 x 23 x 22 x 22. Three axis lengths will be doubled by the transformation, giving 24 x 23 x 23 x 23. We see that r-1 = 1 axis has log length = (n-p)/d_+2 = 4, and that d-r+1 = 3 axes have log length = (n-p)/d_+1 = 3. If instead n-p=8, we split 28 into 22 x 22 x 22 x 22, and double three axis lengths to obtain 23 x 23 x 23 x 22. r=0, and we see that d-1 = 3 axes have log length 8/4 + 1 = 3, and that one axis has log length 2.

 

A multidimensional FNT is similar to other multidimensional transforms; it is computed by computing 1-dimensional transforms along each dimension. Thus B must be large enough for an FNT of length 4B to cover any side of the cube. Depending on the congruence class of n-p mod d, we have either

 

4B = 2 ( (n-p)/d_+2) (n-p mod d >= 2) (VII)

or

4B = 2 ( (n-p)/d_+1) (n-p mod d _ 0, 1) (VIII)

 

 

Using logarithmic notation, these become

 

b = (n-p)/d_ (n-p mod d >= 2 ) (IX)

 

and

 

b = (n-p)/d_ -1 (n-p mod d _ 0, 1) (X)

 

Equations VI, IX, and X give us the defining relationships between n,p,d, and b. For a given N and d, we may examine the various tradeoffs involved in digit size, interprocessor communication, and the number of processors required.

 

Tradeoffs in the FNT

 

For any input sequence of N bits, there are a variety of choices regarding its partitioning into digits, the dimensionality of the transform, the number of processing elements required, and the number of interprocessor communication steps.

 

We see that the log of the total number of processors required for a d-dimensional FNT is d -1 + n-p, where n = log N and p is the log of the digit size. We also note that the number of communication steps required, assuming sufficient processors are available, is equal to the sum of the logarithms of the lengths of each dimension of the d-cube. For balanced transforms, we substitute (X) into (VI) to obtain

 

2p+1+n-p <= 2 ((n-p)/d_ -1 (n-p _ 1 mod d) (XI)

 

For unbalanced transforms, we may try differing values of p, using (XI) if n-p _ 0 mod d, or (XII) if n-p mod d >= 2:

 

2p+1+n p <= 2 ((n-p)/d_) (n-p >= 2 mod d) (XII)

 

(XI) and (XII) have no closed form solution for p as a function of n and d. However, for n sufficiently large, we may neglect the linear terms if we subtract 1 from the exponent on the left hand side. Thus (XI) simplifies to

 

p+1 <= ((n-p)/d_) - 2 (XIII)

 

for n sufficiently large, using balanced transforms.

 

Since n-p _ 1 mod d, (n-p)/d_ = (n-p-1)/d. Multiplying both sides by d and solving for p gives

 

p <= (n-3d -1)/(d+1) (n-p _ 1 mod d) (XIV)

 

Since p must be an integer, we have

 

p <= (n-3d-1)/(d+1)_ (n-p _ 1 mod d) (XV)

 

(XV) applies only for n sufficiently large. (XI) may be used for any n by an examination of all feasible values of p. This value of p can then be used in (X) to compute the smallest b for which the appropriate FNT exists. A graph of the minimum value of b for balanced transforms, 1 <= n <= 30, 1 <= d <= 4, is shown below.

 

 

Figure 1

 


We have seen that the log of the number of processing elements required is n-p+d-1. Combining this with (X) shows that the log number of PEs is d(b+2), making the number of PEs 2d(b+2). The computation of a 1-d FNT along an axis of the d-cube (using a transform based on a = 4B) requires b+2 interprocessor communication steps. Thus if n-p _ 1 mod d, the total number of communication steps required for a d-dimensional FNT is d(b+2), and for convolution 2d(b+2). We note that this is just 2 times the log of the number of PEs.

 

(Convolution actually requires the computation of three transforms: two forward and one inverse. We combine the interprocessor communication operations of the two forward transforms, transmitting two data values at once. Thus the communication operations of the forward transform may take slightly longer than those of the inverse transform, depending on the values of b and the overhead associated with interprocessor communication).

 

We see that the geometric decrease in word size caused by increasing d is accompanied by an exponential growth in the number of PE's, and linear growth in the number of communication steps. This is shown below in Figures 2 and 3.

 

Figure 2

 

Figure 3

 

Due to the dramatic increase in the number of PEs with increasing d, it may be desirable to trade off word size for processing elements. This can be done by using primitive roots of unity <> 2(1/2), or by using unbalanced transforms. This is discussed in more detail in [5].

 

An Implementation on a Massively Parallel Processor

 

The FNT and other number-theoretic transforms can be used for any application in which convolution is required. They are particularly well suited for the computation of exact convolution, and hence are useful for the multiplication of large integers. We have implemented a program to multiply large integers on the Connection Machine, using the parallel FNT.

 

The Connection Machine is a massively parallel SIMD processor, with processing elements interconnected in a boolean n-cube. CM software provides support for "virtual processors", permitting programs to be written for a number of PEs greater than those physically available. When the number of virtual processors exceeds the number of physical processors, the system is said to be saturated. Further increases in the number of virtual processors result in a corresponding decrease in execution time, although this can be affected by interprocessor communication patterns. For more information on the Connection Machine, the reader is referred to [7] and [8].

 

Figure 4 shows a log/log plot of multiplication time as a function of input size, shown numerically in Table I. We have implemented a 2D FNT-based algorithm, using balanced transforms with a=2. The values of b mandated by a 1-D transform would make the point-by-point multiplication performance unacceptable, while transforms with d > 2 would saturate the system with processing elements. The results shown are for a 32K Connection Machine. Values of n corresponding to n-p+1 > 15, in this case n >= 17, would execute in approximately half the time on a 64K CM. (The +1 is due to zero padding of the input sequences). Execution times range from 49 milliseconds for 2Kbit numbers to 2600 milliseconds for 8Mbit numbers.

 

We see that the execution time is a very jagged function of n. This is because the dominant factor in execution time is the number of digits, not the input size in bits. Since a doubling of the input size does not necessarily result in any increase in the number of digits, resulting in similar execution times. Implementing unbalanced transforms would permit the optimal value of p to be used, resulting in a smoother function.

 

The largest numbers our program can multiply are 8Mbits wide. Larger numbers exceed the memory limitations of a 32K CM, due to the number of processing elements required.

 

Figure 4

 

 


Table I

Multiplication Time For 2n-bit Integers

 

n time (msec)

 

11 49

12 50

13 40

14 38

15 76

16 72

17 65

18 275

19 275

20 287

21 2500

22 2500

23 2600

We have reported in previous work [4] our implementation of another integer convolution algorithm on the Connection Machine. The results reported here represent an improvement of 6 times for 2K numbers, decreasing to about 4 times for 8M numbers. The decrease in performance improvement is due to system saturation, which dampens the performance effects of otherwise more efficient algorithms.

 

Conclusions

 

The Fermat Number Transform was proposed several years ago as an alternative to the FFT, but not pursued extensively due to transform length restrictions. For transforms in a Fermat ring modulo 2B+1, the longest transform length is 4B. Since b = log B is limited by the word length of the machine computing the transform, existing computers were limited to short sequences. Multidimensional techniques were proposed to overcome this restriction, and were partially successful.

 

The existence of computers like the Connection Machine, however, make such restrictions obsolete. The CM does not make use of the concept of "word length"; operations can be performed on bit quantities of virtually arbitrary size. The CM is particularly attractive for the FNT due to the parallel nature of the algorithm.

 

Our results show that while word length restrictions no longer limit the length of sequences to be used as inputs to the FNT, the parameter b = log B remains important. The FNT convolution algorithm requires the point-by-point multiplication of B-bit numbers; if b becomes too large, point-by-point multiplication time will dominate. For small b, the number of processors and the number of interprocessor communication steps become the principal factors affecting performance.

 

We have implemented an algorithm for the multiplication of large integers using the parallel FNT on the Connection Machine. Our times range from about 50 msec for 2K-bit numbers to 2600 msec for 8Mbit numbers. These results are about 5 times faster than our previous work, an implementation of another integer convolution algorithm on the Connection Machine [4].

 

Future work should focus on reducing interprocessor communication requirements, perhaps through the use of alternative CM configurations or lower-level CM instructions than those currently employed by our program. Point-by-point multiplication time could also be improved through the application of any of the well-known efficient algorithms for integer multiplication. Extended-precision FFTs for the CM could also be developed and compared with the parallel FNT to determine which algorithm is more suitable for large integer multiplication. Comparisons with other high-performance architectures would also be useful. Presently, we are combining the program discussed here with previous work [3] to develop a massively parallel software library for the arithmetic manipulation of large integers.

 

Acknowledgements

 

The author gratefully acknowledges the use of the Connection Machine Network Server Pilot Facility, supported under terms of DARPA contract DACA76-88-C-0012 and made available to the author by Thinking Machines Corporation. Thanks are due in particular to TMC employees Roger Frye, David Gingold, and Alan Mainwaring for their patience and assistance.

 

 

References

 

[1] Agarwal, R.C. and Burrus, C.S. "Fast Convolution Using Fermat Number Transforms With Applications to Digital Filtering", IEEE Transactions on Acoustics, Speech, and Signal Processing, vol ASSP-22 no 2 pp 87-97 Apr 1974.

 

[2] Agarwal, R.C. and Burrus, C.S. "Fast One-dimensional Digital Convolution by Multidimensional Techniques", IEEE Transactions on Acoustics, Speech, and Signal Processing, vol ASSP-22 pp 1-10 Feb 1974.

 

[3] Fagin, Barry, "Fast Addition of Large Integers", submitted to IEEE Transactions on Computers, 1989. (Copies available from the author).

 

[4] Fagin, Barry, "Negacyclic Convolution Using Polynomial Transforms on Hypercubes", submitted to IEEE Transactions on Acoustics, Speech and Signal Processing. (Copies available from the author).

 

[5] Fagin, Barry, "Large Integer Multiplication on Massively Parallel Processors", submitted to the Journal of Parallel and Distributed Computing, 1990 (Copies available from the author).

 

[6] Leibowitz, L.M. "A Simplified Binary Arithmetic for the Fermat Number Transform", IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-24, pp 356-359, October 1976.

 

[7] Thinking Machines Corporation, Connection Machine Technical Summary, Cambridge Massachusetts,1989.

 

[8] Thinking Machines Corporation, C/Paris 5.1 User's Manual, Cambridge Massachusetts, 1988.