#### Filter Results:

- Full text PDF available (71)

#### Publication Year

1956

2017

- This year (2)
- Last 5 years (10)
- Last 10 years (24)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Brain Region

#### Cell Type

#### Key Phrases

#### Method

Learn More

- Michael Mascagni, Ashok Srinivasan
- ACM Trans. Math. Softw.
- 1999

In this article we present background, rationale, and a description of the Scalable Parallel Random Number Generators (SPRNG) library. We begin by presenting some methods for parallel pseudorandom number generation. We will focus on methods based on parameterization, meaning that we will not consider splitting methods such as the leap-frog or blocking… (More)

In this article we outline some methods for parallel pseudorandom number generation. We will focus on methods based on parameterization, meaning that we will not consider splitting methods. We describe parameterized versions of the following pseudorandom number generators: (i) linear congruential generators, (ii) shift-register generators, and (iii)… (More)

- Yaohang Li, Michael Mascagni
- CCGRID
- 2003

High performance computing on a large-scale computational grid is complicated by the heterogeneous computational capabilities of each node, node unavailability, and unreliable network connectivity. Replicating computation on multiple nodes can significantly improve performance by reducing task completion time on a grid’s dynamic environment. We develop an… (More)

- Ashok Srinivasan, Michael Mascagni, David Ceperley
- Parallel Computing
- 2003

Ashok Srinivasan (ashok@cs.ucsb.edu) Department of Computer Science, University of California at Santa Barbara, Santa Barbara, CA 93106 USA Michael Mascagni (mascagni@cs.fsu.edu) Department of Computer Science, 203 Love Building, Florida State University, Tallahassee, FL 32308-4530 USA David Ceperley (ceperley@ncsa.uiuc.edu) National Center for… (More)

- Hongmei Chi, Michael Mascagni, T. Warnock
- Mathematics and Computers in Simulation
- 2005

- Michael Mascagni
- Parallel Computing
- 1998

Linear congruential generators (LCGs) remain the most popular method of pseudorandom number generation on digital computers. Ease of implementation has favored implementing LCGs with power-of-two moduli. However, prime modulus LCGs are superior in quality to power-of-two modulus LCGs, and the use of a Mersenne prime minimizes the computational cost of… (More)

We study the suitability of the additive lagged-Fibonacci pseudorandom number generator for parallel computation. This generator has relatively short period with respect to the size of its seed. However, the short period is more than made up for with the huge number of full-period cycles it contains. These diierent full-period cycles are called equivalence… (More)

- Michael Mascagni, Ashok Srinivasan
- Parallel Computing
- 2004

Monte Carlo computations are commonly considered to be naturally parallel. However, one needs to exercise care in parallelizing the underlying pseudorandom number generator (PRNG) to avoid correlations within, and between, random number streams. PRNGs are normally parallelized using one of the following two paradigms: (i) cycle division and (ii)… (More)

- Michael Mascagni, Nikolai A. Simonov
- SIAM J. Scientific Computing
- 2004

In this paper we describe Monte Carlo methods for solving some boundary-value problems for elliptic partial differential equations arising in the computation of physical properties of large molecules. The constructed algorithms are based on walk on spheres, Green’s function first passage, walk in subdomains techniques, and finite-difference approximations… (More)

- Chi-Ok Hwang, Michael Mascagni, Taeyoung Won
- Mathematics and Computers in Simulation
- 2010