The Experts below are selected from a list of 540 Experts worldwide ranked by ideXlab platform

Takanori Koga - One of the best experts on this subject based on the ideXlab platform.

Makoto Matsumoto - One of the best experts on this subject based on the ideXlab platform.

  • cryptographic Mersenne Twister and fubuki stream block cipher
    IACR Cryptology ePrint Archive, 2014
    Co-Authors: Makoto Matsumoto, Takuji Nishimura, Mariko Hagita, Mutsuo Saito
    Abstract:

    We propose two stream ciphers based on a non-secure pseudorandom number generator (called the mother generator). The mother generator is here chosen to be the Mersenne Twister (MT), a widely used 32-bit integer generator having 19937 bits of internal state and period 219937 − 1. One proposal is CryptMT, which computes the accumulative product of the output of MT, and use the most significant 8 bits as a secure random numbers. Its period is proved to be 219937 − 1, and it is 1.5-2.0 times faster than the most optimized AES in counter-mode. The other proposal, named Fubuki, is designed to be usable also as a block cipher. It prepares nine different kinds of encryption functions (bijections from blocks to blocks), each of which takes a parameter. Fubuki encrypts a sequence of blocks (= a plain message) by applying these encryption functions iteratedly to each of the blocks. Both the combination of the functions and their parameters are pseudorandomly chosen by using its mother generator MT. The key and the initial value are passed to the initialization scheme of MT.

  • variants of Mersenne Twister suitable for graphic processors
    arXiv: Mathematical Software, 2010
    Co-Authors: Mutsuo Saito, Makoto Matsumoto
    Abstract:

    This paper proposes a type of pseudorandom number generator, Mersenne Twister for Graphic Processor (MTGP), for efficient generation on graphic processessing units (GPUs). MTGP supports large state sizes such as 11213 bits, and uses the high parallelism of GPUs in computing many steps of the recursion in parallel. The second proposal is a parameter-set generator for MTGP, named MTGP Dynamic Creator (MTGPDC). MT- GPDC creates up to 2^32 distinct parameter sets which generate sequences with high-dimensional uniformity. This facility is suitable for a large grid of GPUs where each GPU requires separate random number streams. MTGP is based on linear recursion over the two-element field, and has better high-dimensional equidistribution than the Mersenne Twister pseudorandom number generator.

  • Mersenne Twister and Fubuki Stream/Block Cipher
    2008
    Co-Authors: Makoto Matsumoto, Takuji Nishimura, Mariko Hagita, Mutsuo Saito
    Abstract:

    We propose two stream ciphers based on a non-secure pseudorandom number generator (called the mother generator). The mother generator is here chosen to be the Mersenne Twister (MT), a widely used 32-bit integer generator having 19937 bits of internal state and period 2 1

  • simd oriented fast Mersenne Twister a 128 bit pseudorandom number generator
    2008
    Co-Authors: Mutsuo Saito, Makoto Matsumoto
    Abstract:

    Mersenne Twister (MT) is a widely-used fast pseudorandom number generator (PRNG) with a long period of 219937 - 1, designed 10 years ago based on 32-bit operations. In this decade, CPUs for personal computers have acquired new features, such as Single Instruction Multiple Data (SIMD) operations (i.e., 128-bit operations) and multi-stage pipelines. Here we propose a 128-bit based PRNG, named SIMD-oriented Fast Mersenne Twister (SFMT), which is analogous to MT but making full use of these features. Its recursion fits pipeline processing better than MT, and it is roughly twice as fast as optimised MT using SIMD operations. Moreover, the dimension of equidistribution of SFMT is better than MT.

  • SIMD-oriented fast Mersenne Twister: A 128-bit pseudorandom number generator
    Springer-Verlag, 2007
    Co-Authors: Mutsuo Saito, Makoto Matsumoto
    Abstract:

    Summary. Mersenne Twister (MT) is a widely-used fast pseudorandom number generator (PRNG) with a long period of 2 19937 − 1, designed 10 years ago based on 32-bit operations. In this decade, CPUs for personal computers have acquired new features, such as Single Instruction Multiple Data (SIMD) operations (i.e., 128bit operations) and multi-stage pipelines. Here we propose a 128-bit based PRNG, named SIMD-oriented Fast Mersenne Twister (SFMT), which is analogous to MT but making full use of these features. Its recursion fits pipeline processing better than MT, and it is roughly twice as fast as optimised MT using SIMD operations. Moreover, the dimension of equidistribution of SFMT is better than MT. We also introduce a block-generation function, which fills an array of 32-bit integers in one call. It speeds up the generation by a factor of two. A speed comparison with other modern generators, such as multiplicative recursive generators, shows an advantage of SFMT. The implemented C-codes are downloadable fro

Kevin Noel - One of the best experts on this subject based on the ideXlab platform.

  • convergence analysis of random generators in monte carlo simulation Mersenne Twister and sobol
    Social Science Research Network, 2016
    Co-Authors: Kevin Noel
    Abstract:

    We investigate further the random generators used in finance Monte Carlo simulation: Mersenne Twister and Sobol Quasi-Random Generator. We focus the analysis on the statistical properties of the random numbers generated across multiple dimensions in Monte Carlo finance simulation. We particularly focus on the distributions and processes used in the estimation of conditional expectation in a finance framework when using Mersenne Twister and Sobol generators.We characterize the convergence properties of the sample distributions by relying on empirical distribution theory. Then, we provide practical analysis on the convergence of distribution encountered in finance problems. Finally, we characterize the differences of the random generators when simulating such processes.

  • analysis of random generators in monte carlo simulation Mersenne Twister and sobol
    Social Science Research Network, 2016
    Co-Authors: Kevin Noel
    Abstract:

    We investigate the random generators used in Finance: Mersenne Twister and Sobol Quasi Random Generator. We focus the analysis on the statistical properties of the random numbers generated at high dimension and over a wide range of dimensions (From 1 to 20000). We describe the degenerate patterns of random numbers produced by Sobol Generator and Randomized Sobol Generator across a wide series of dimension (800 dimension pairs), leading to charaterize those patterns. We provide an algorithm to filter Sobol sequences. Additionally, we mention the used cases in Finance, especially, we highlight the dimensions typically encountered in quantative finance model simulations. We highlight the simulation with CPU/GPU (Graphic Processor Unit) for random numbers generation and leading some conclusions on their practical usage.

Jun Igarashi - One of the best experts on this subject based on the ideXlab platform.

Mutsuo Saito - One of the best experts on this subject based on the ideXlab platform.

  • cryptographic Mersenne Twister and fubuki stream block cipher
    IACR Cryptology ePrint Archive, 2014
    Co-Authors: Makoto Matsumoto, Takuji Nishimura, Mariko Hagita, Mutsuo Saito
    Abstract:

    We propose two stream ciphers based on a non-secure pseudorandom number generator (called the mother generator). The mother generator is here chosen to be the Mersenne Twister (MT), a widely used 32-bit integer generator having 19937 bits of internal state and period 219937 − 1. One proposal is CryptMT, which computes the accumulative product of the output of MT, and use the most significant 8 bits as a secure random numbers. Its period is proved to be 219937 − 1, and it is 1.5-2.0 times faster than the most optimized AES in counter-mode. The other proposal, named Fubuki, is designed to be usable also as a block cipher. It prepares nine different kinds of encryption functions (bijections from blocks to blocks), each of which takes a parameter. Fubuki encrypts a sequence of blocks (= a plain message) by applying these encryption functions iteratedly to each of the blocks. Both the combination of the functions and their parameters are pseudorandomly chosen by using its mother generator MT. The key and the initial value are passed to the initialization scheme of MT.

  • Hiroshima University
    2012
    Co-Authors: Mutsuo Saito
    Abstract:

    This paper proposes a type of pseudorandom number generator,Mersenne Twister for Graphic Processor (MTGP), for efficient generation on graphic processessing units (GPUs). MTGP supports large state sizes such as 11213 bits, and uses the high parallelism of GPUs in computing many steps of the recursion in parallel. The second proposal is a parameter-set generator for MTGP, named MTGP Dynamic Creator (MTGPDC). MT-GPDC creates up to 232 distinct parameter sets which generate sequences with high-dimensional uniformity. This facility is suitable for a large grid of GPUs where each GPU requires separate random number streams. MTGP is based on linear recursion over the two-element eld, and has better high-dimensional equidistribution than the Mersenne Twister pseudorandom number generator

  • Hiroshima University
    2012
    Co-Authors: Mutsuo Saito
    Abstract:

    This paper proposes a type of pseudorandom number generator,Mersenne Twister for Graphic Processor (MTGP), for efficient generation on graphic processessing units (GPUs). MTGP supports large state sizes such as 11213 bits, and uses the high parallelism of GPUs in computing many steps of the recursion in parallel. The second proposal is a parameter-set generator for MTGP, named MTGP Dynamic Creator (MTGPDC). MT-GPDC creates up to 232 distinct parameter sets which generate sequences with high-dimensional uniformity. This facility is suitable for a large grid of GPUs where each GPU requires independent random number streams. MTGP is based on linear recursion over the two-element eld, and has better high dimensional equidistribution than the Mersenne Twister pseudorandom number generator. 1 Random number generation for GPU A Graphic Processing Unit (GPU) is a highly parallel processor designed fo

  • variants of Mersenne Twister suitable for graphic processors
    arXiv: Mathematical Software, 2010
    Co-Authors: Mutsuo Saito, Makoto Matsumoto
    Abstract:

    This paper proposes a type of pseudorandom number generator, Mersenne Twister for Graphic Processor (MTGP), for efficient generation on graphic processessing units (GPUs). MTGP supports large state sizes such as 11213 bits, and uses the high parallelism of GPUs in computing many steps of the recursion in parallel. The second proposal is a parameter-set generator for MTGP, named MTGP Dynamic Creator (MTGPDC). MT- GPDC creates up to 2^32 distinct parameter sets which generate sequences with high-dimensional uniformity. This facility is suitable for a large grid of GPUs where each GPU requires separate random number streams. MTGP is based on linear recursion over the two-element field, and has better high-dimensional equidistribution than the Mersenne Twister pseudorandom number generator.

  • Mersenne Twister and Fubuki Stream/Block Cipher
    2008
    Co-Authors: Makoto Matsumoto, Takuji Nishimura, Mariko Hagita, Mutsuo Saito
    Abstract:

    We propose two stream ciphers based on a non-secure pseudorandom number generator (called the mother generator). The mother generator is here chosen to be the Mersenne Twister (MT), a widely used 32-bit integer generator having 19937 bits of internal state and period 2 1