SPAR data handling utilities

The SPAR computer software system is a collection of processors that perform particular steps in the finite-element structural analysis procedure. The data generated by each processor are stored on a data base complex residing on an auxiliary storage device, and these data are then used by subsequent processors. The SPAR data handling utilities use routines to transfer data between the processors and the data base complex. A detailed description of the data base complex organization is presented. A discussion of how these SPAR data handling utilities are used in an application program to perform desired user functions is given with the steps necessary to convert an existing program to a SPAR processor by incorporating these utilities. Finally, a sample SPAR processor is included to illustrate the use of the data handling utilities

Coercivity of domain wall motion in thin films of amorphous rare earth-transition metal alloys

Computer simulations of a two dimensional lattice of magnetic dipoles are performed on the Connection Machine. The lattice is a discrete model for thin films of amorphous rare-earth transition metal alloys, which have application as the storage media in erasable optical data storage systems. In these simulations, the dipoles follow the dynamic Landau-Lifshitz-Gilbert equation under the influence of an effective field arising from local anisotropy, near-neighbor exchange, classical dipole-dipole interactions, and an externally applied field. Various sources of coercivity, such as defects and/or inhomogeneities in the lattice, are introduced and the subsequent motion of domain walls in response to external fields is investigated

Fat vs. thin threading approach on GPUs: application to stochastic simulation of chemical reactions

We explore two different threading approaches on a graphics processing unit (GPU) exploiting two different characteristics of the current GPU architecture. The fat thread approach tries to minimise data access time by relying on shared memory and registers potentially sacrificing parallelism. The thin thread approach maximises parallelism and tries to hide access latencies. We apply these two approaches to the parallel stochastic simulation of chemical reaction systems using the stochastic simulation algorithm (SSA) by Gillespie (J. Phys. Chem, Vol. 81, p. 2340-2361, 1977). In these cases, the proposed thin thread approach shows comparable performance while eliminating the limitation of the reaction system’s size

STOCHSIMGPU Parallel stochastic simulation for the Systems\ud Biology Toolbox 2 for MATLAB

Motivation: The importance of stochasticity in biological systems is becoming increasingly recognised and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU which exploits graphics processing units (GPUs)for parallel stochastic simulations of biological/chemical reaction systems and show that significant gains in efficiency can be made. It is integrated into MATLAB and works with the Systems Biology Toolbox 2 (SBTOOLBOX2) for MATLAB.\ud \ud Results: The GPU-based parallel implementation of the Gillespie stochastic simulation algorithm (SSA), the logarithmic direct method (LDM), and the next reaction method (NRM) is approximately 85 times faster than the sequential implementation of the NRM on a central processing unit (CPU). Using our software does not require any changes to the user’s models, since it acts as a direct replacement of the stochastic simulation software of the SBTOOLBOX2

Universality and Clustering in 1+1 Dimensional Superstring-Bit Models

We construct a 1+1 dimensional superstring-bit model for D=3 Type IIB superstring. This low dimension model escapes the problems encountered in higher dimension models: (1) It possesses full Galilean supersymmetry; (2) For noninteracting polymers of bits, the exactly soluble linear superpotential describing bit interactions is in a large universality class of superpotentials which includes ones bounded at spatial infinity; (3) The latter are used to construct a superstring-bit model with the clustering properties needed to define an $S$-matrix for closed polymers of superstring-bits.Comment: 11 pages, Latex documen