An FPGA Implementation of a Parallelized MT19937 Uniform Random Number Generator
© V. Sriram and D. Kearney. 2009
Received: 20 August 2008
Accepted: 21 April 2009
Published: 30 July 2009
Recent times have witnessed an increase in use of high-performance reconfigurable computing for accelerating large-scale simulations. A characteristic of such simulations, like infrared (IR) scene simulation, is the use of large quantities of uncorrelated random numbers. It is therefore of interest to have a fast uniform random number generator implemented in reconfigurable hardware. While there have been previous attempts to accelerate the MT19937 pseudouniform random number generator using FPGAs we believe that we can substantially improve the previous implementations to develop a higher throughput and more area-time efficient design. Due to the potential for parallel implementation of random numbers generators, designs that have both a small area footprint and high throughput are to be preferred to ones that have the high throughput but with significant extra area requirements. In this paper, we first present a single port design and then present an enhanced 624 port hardware implementation of the MT19937 algorithm. The 624 port hardware implementation when implemented on a Xilinx XC2VP70-6 FPGA chip has a throughput of 32 bit random numbers per second which is more than 17x that of the previously best published uniform random number generator. Furthermore it has the lowest area time metric of all the currently published FPGA-based pseudouniform random number generators.
Reconfigurable computing is increasingly being seen as an attractive solution for accelerating simulations that require fast generation of large quantities of random numbers. Although random numbers are often a very small part of these algorithms inability to generate them fast enough, them can cause a bottleneck in the reconfigurable computing implementation of the simulation. For example, in the simulated generation of infrared scenes that take into account the effects of a turbulent atmosphere and the effects of CCD camera sensor electronic noise, each scene generated by the simulation requires more than gaussian random numbers. A real-time simulation sequence at 15 scenes/s thus needs more than random samples generated per second. Since a typical software uniform generator  can only manage per second you would need 29 PCs to keep up with this rate.
A key requirement of Infrared (IR) scene simulation is the necessity to generate large sequences of random numbers on the provision of a single seed. Not all random number generators are capable of doing this (e.g., see those presented in ). Moreover, in order to achieve the high throughput required, it is important to make use of algorithm's internal parallelism (i.e., by splitting the algorithm into independent subsequences) as well as external parallelism (i.e., through parallel implementations of the algorithm). It has been recommended in  and reinforced in  that in order to prevent possible correlations between output sequences in parallel implementation of the same algorithm using different initial seeds, it is necessary to use a random number generator that has a period greater than . In summary the requirements of an FPGA optimized uniform random number generator for IR scene simulation are as follows:
(1)should be seeded random number generator (so that the same sequence may be regenerated);
(2)have the ability to generate a large quantity of random numbers from one seed;
(3)can be split in many independent subsequences;
(4)have a very large period;
(5)generate random numbers quickly;
(6)satisfy statistical tests from randomness;
(7)able to generate parallel streams of uncorrelated random numbers.
2. Survey of FPGA-Based Uniform Random Number Generators
As discussed in the previous section IR scene simulation  requires fast generation of large sequences of random numbers on the provision of a single seed. From the extensive literature in the field of software pseudouniform random number generators, some algorithms that achieve this are the generalized feedback shift register and the MT19937. They both have the ability to generate large sequences of random numbers on the provision of a single seed and have the ability to be split in independent subsequences to allow for a more parallelized implementation in hardware. An additional benefit of these algorithms is that they have large periods. It has been recommended in  and reinforced in  that in order to prevent possible problems with correlations when implementing the same algorithm with different initial seeds in parallel, the algorithm needs to have a period in excess of . The MT19937 algorithm has a period of , which therefore allows for parallel implementation of MT19937 algorithm with different initial seeds.
There are currently two FPGA optimized implementations of the MT19937, including a single port design, see , and a multiport design presented in . The well-known generalized feedback shift register has been modified for FPGA implementation in  to achieve the smallest area time design to date. Thus it is of interest to see if a hardware implementation of a 624 port MT19937 algorithm can be made more competitive. This is the subject of investigation of this paper. This paper is organized as follows, in Section 2 the MT19937 algorithm is briefly described. In Sections 3 and 4 we present single port and 624 port hardware implementations of the MT19937 algorithm. In Section 5, diehard test results of the two hardware implementations along with the performance comparisons of these implementations with other published FPGA-based pseudouniform random number generators are presented.
where each is a vector of size with components 0 or 1. The maximum possible period of of this generator is achieved when the primitive trinomial divides for , for the smallest value of . The maximum period can be achieved by selecting as a Mersenne Prime. It was later identified that the effectiveness, that is, the randomness of the numbers produced, of this algorithm was dependent on the selection of initial seeds. Furthermore, the algorithm required words working area (which was memory consuming) and the randomness of the numbers produced was dependent on the selection of initial seeds. This discovery led Matsumoto and Kurita 1994 to further revise this algorithm to develop the twisted generalized feedback shift register II in . This generator used linear combinations of only relatively few bits of the preceding numbers and was thus considerably faster and was named TT800. Later Matsumoto and Kurita in 1998 further revised the TT800 to admit a Mersenne-prime period, and this new algorithm was called the MT19937; see .
4. Single Port Version
Typically the user provides one number as a seed; however, the MT19937 algorithm works with a pool of 624 seeds so that generator stage generates 624 seeds from the single input from the user. In stage two (the seed value modulator), which is the core of the algorithm, three values seed , seed , and seed are read from the pool and based on the computation defined in the algorithm; seed is updated. In the final stage, the output generator reads one of the pool values and generates the output uniform random number from this value.
The single port version provided is similar to the software implementation of the MT19937 algorithm as it does not provide any significant parallelization in the generation of the seeds. The only parallelism that is exploited is in the concurrent execution of seed value modulator (stage 2) and output generator (stage 3). It was also found that it was not possible to pipeline the output generator to more than 3 stages as it was tied to the seed value modulator. Significant improvements in throughput could be achieved by the parallelization of the stages 2 and 3 in addition to executing them in parallel as shown above. However, the problem with parallelizing stages 2 and 3 is that currently the seeds are all stored in a single dual port BRAM. It is not possible to carry out multiple reads and multiple writes to a single BRAM in one clock cycle. Previously in  parallelization of both these stages was achieved by dividing the seeds into multiple BRAMs. This however significantly increased the area requirements of the design. In the next section we study this problem in more detail and present our new design that has a high throughput and is area efficient.
5. 624 Port Version
There has previously been an attempt to parallelize the MT19937 algorithm by dividing the seeds into various pools and then replicating the stages 2 and 3 to generate multiple outputs; see . However, it was noted that this was found not to be area time efficient. A close examination of the design reveals that in order to parallelize the generation of random numbers, the authors divide the seeds into multiple BRAMs. Although this did increase the throughput, it greatly increased the area of the design as well. The reason for this is that the logic required to generate the necessary BRAM address increased in complexity with the dividing of seeds across multiple BRAMs.
It is important to note here that the problem is not the parallelization of the generation of the uniform random numbers but is the storing of seeds in multiple BRAMs. Thus if the seeds were to be stored in registers rather than BRAMs the logic used to generate the BRAM address could be saved. The excessive use of BRAMs to store seeds was always considered problematic. For example, in  it was found that the TT800 algorithm suffered in a similar manner when the seeds were distributed across multiple BRAMs. In this paper it was reported that the single port version used 81 Xilinx slices while the 3 port one used 132 slices. Of the 132 slices used, 60 slices were used for the complex BRAM address generation logic. We believe that we can parallelize the MT19937 algorithm to the maximum possible 624 port by storing seeds in registers rather than BRAMs. In this section, we study a more simplified design for a 624 port MT19937 random number generator that uses registers to store seeds.
We also present a generic model which makes use of each of these seed pools to modify the seed value and generate new random numbers per clock cycle. Now on each seed pool the two stages of the MT19937 presented in Figure 3 work together to modify each seed value and generate a new one. This is illustrated in Figure 3(b). From a point of view of circuit speed and complexity, no register is shared by more than two reading channels and one writing channel. The consequence is that register access logic is simpler, smaller, and faster.
6.1. Test for Randomness
Diehard test results.
624 port implementation
6.2. Comparison with Existing FPGA-Based Uniform Random Number Generators
In this section we compare our designs with those that are currently published. We compare our designs on the basis of area time rating and throughput. In contrasting these solutions we take into account the amount of total resources used, including slices, LUTs, and flip flops.
Period, area, time, and throughput comparisons.
Xilinx XC2VP70 Slices
Clock rate MHz
Our best implementation, which is the 624 port MT19937, uses only 1253 Xilinx slices. This is significantly less than all of the other multiport designs currently published in literature as we use registers to store seeds and have arranged our seed value modulator and output generator pipelines in an area efficient manner. Thus we do not require any complex BRAM address generation logic and access to BRAMs. As a result we save on area and since our design if 624 port we generate 624 uniform random numbers per clock cycle. In a reconfigurable computing implementation, where only the random number generation is accelerated in hardware, like all of the other FPGA-based random number generators, the 624 port implementation is limited by the I/O bandwidth of the FPGA.
In this paper we have presented a unique 624 port MT19937 hardware implementation. Whilst currently there are hardware implementations of uniform random number generators published none seem to be able to offer a high throughput as well as area time efficiency. It was demonstrated that the 624 port design presented in this paper is a high throughput, area time efficient FPGA optimized pseudouniform random number generator with a large period and with the ability to generate large quantities of uniform random numbers from a single seed. Thus making suitable for use in a reconfigurable computing implementation of real-time IR scene simulation.
Research undertaken for this report has been assisted with an international scholarship from the Maurice de Rohan fund. This support is acknowledged and greatly appreciated.
- L'Ecuyer P: Random number generation. In Handbook of Simulation. Edited by: Banks J. John Wiley & Sons, New York, NY, USA; 1998:93-137.Google Scholar
- Press WH, Flannery BP, et al.: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, Cambridge, UK; 1986.Google Scholar
- Srinivasan A, Mascagni M, Ceperley D: Testing parallel random number generators. Parallel Computing 2003,29(1):69-94. 10.1016/S0167-8191(02)00163-1MathSciNetView ArticleGoogle Scholar
- L'Ecuyer P, Panneton R: Fast random number generators based on linear recurrences modulo 2: overview and comparison. In Proceedings of the Winter Simulation Conference, 2005. IEEE Press; 110-119.Google Scholar
- Sriram V, Kearney D: High speed high fidelity infrared scene simulation using reconfigurable computing. In Proceedings of the IEEE International Conference on Field Programmable Logic and Applications, August 2006, Madrid, Spain. IEEE Press;Google Scholar
- Sriram V, Kearney DA: An area time efficient field programmable mersenne twister uniform random number generator. In Proceedings of the International Conference on Engineering of Reconfigurabe Systems and Algorithms, June 2006. CSREA Press;Google Scholar
- Konuma S, Ichikawa S: Design and evaluation of hardware pseudo-random number generator MT19937. IEICE Transactions on Information and Systems 2005,E88-D(12):2876-2879. 10.1093/ietisy/e88-d.12.2876View ArticleGoogle Scholar
- Thomas DB, Luk W: High quality uniform random number generation through LUT optimised linear recurrences. Proceedings of the IEEE International Conference on Field Programmable Technology (FPT '05), December 2005, Singapore 61-68.Google Scholar
- Tausworthe R: Random numbers generated by linear recurrence modulo two. Mathematics of Computation 1965, 19: 201-209. 10.1090/S0025-5718-1965-0184406-1MATHMathSciNetView ArticleGoogle Scholar
- Lewis T, Payne W: Generalized feedback shift register pseudorandom number algorithm. Journal of the ACM 1973,20(3):456-468. 10.1145/321765.321777MATHView ArticleGoogle Scholar
- Matsumoto M, Kurita Y: Twisted GFSR generators–II. ACM Transactions on Modeling and Computer Simulation 1994,4(3):254-266. 10.1145/189443.189445MATHView ArticleGoogle Scholar
- Matsumoto M, Nishimura T: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation 1998,8(1):3-30. 10.1145/272991.272995MATHView ArticleGoogle Scholar
- Sriram V, Kearney D: Towards a multi-FPGA infrared simulator. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 2007,4(4):50-63.Google Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.