Narrow Results

A data parallel implementation of the finite element method on the Connection Machine system CM-2^® is presented. This implementation assumes that the elementary unit of data is an unassembled nodal point. In the context of the CM-2, each virtual processor represents an unassembled nodal point and nodal points shared between elements are replicated on different virtual processors. An algorithm for computing each elemental stiffness matrix concurrently, as well as different elemental stiffness matrices concurrently, without inter-processor communication is presented. The performance of the elemental stiffness matrix computation is in the range 1.6–1.9 GFlops s⁻¹. The sparse system of linear equations that results from the finite element discretization has been solved by a conjugate gradient method with a diagonal preconditioner. The rate of convergence of the conjugate gradient iterations for boundary conditions which correspond to uniaxial deformations depends nonlinearly on the order of interpolation of the elements and linearly on the mesh discretization. Sample code segments are provided to illustrate the programming environment on a data parallel architecture.

The paper reviews existing methods for generating discrete random variables and their suitability for vector processing. A new method for generating discrete random variables for use in vectorized Monte Carlo simulations is presented. The method uses the concept of importance sampling and generates random variables by employing uniform distribution to speed up the computation. The sampled random variables are subsequently adjusted so that unbiased estimates are obtained. The method preserves both the mean and variance of the original distribution. It is demonstrated that the method requires simpler coding and shorter execution time for both scalar and vector processing, when compared with other existing methods. The vectorization speedup of the method is demonstrated on an IBM 3090–180 machine with a vector facility.

In this paper we report on our first experience with a portable, easily usable communication environment, 'Sciddle', for distributing computations over a homogeneous network of UNIX computers. We demonstrate the usefulness of the system with applications from linear algebra and quantum chemistry on a network of Ethernet-connected Sun workstations and of Internet-connected Cray supercomputers, respectively.

Algorithms exhibiting parallelization on many different levels are discussed for short-and long-range cellular automata implemented on scalar, vector, SIMD and MIMD machines. Short range cellular automata are commonly used for simulating hydrodynamic fluid flows, while long range cellular automata are applicable to neural networks at zero temperature. A common programming approach based upon multi-spin coding and including higher levels of parallelization when possible, has been used to implement these models on the SUN SPARC-1, the IBM-3090, the Alliant FX/2800, the NEC-SX3/11, the Cray-YMP/832 and the Connection Machine, CM-2. Section 4 of the paper compares the performance of these computers for the algorithms discussed in the text. Additionally, the major subroutines for each computer type are given in the Appendix.

This article discusses how public research and infrastucture development helps in building the Indian biotech sector. It mentioned the research and development centers and the various research centers and institutes in India.

For the month of August 2020, APBN explores the wonders of the human brain and how researchers and scientists are leveraging on technological advancements to gain insights to its inner mechanisms. In the Columns section, scientists from ACT Genomics discuss the topic of Precision Oncology and patient selection for PARP inhibitor treatment in BRCA- & HRR-associated cancers. This month APBN interviewed Dr Ling Kai Yi, co-founder and Chief Science/Technology Officer of Singapore-based clean meat company, ShiokMeats to take a closer look at how the team is fighting for sustainability in the seafood industry.