FFT for the APE Parallel Computer

We present a parallel FFT algorithm for SIMD systems following the `Transpose Algorithm' approach. The method is based on the assignment of the data field onto a 1-dimensional ring of systolic cells. The systolic array can be universally mapped onto any parallel system. In particular for systems with next-neighbour connectivity our method has the potential to improve the efficiency of matrix transposition by use of hyper-systolic communication. We have realized a scalable parallel FFT on the APE100/Quadrics massively parallel computer, where our implementation is part of a 2-dimensional hydrodynamics code for turbulence studies. A possible generalization to 4-dimensional FFT is presented, having in mind QCD applications.Comment: 17 pages, 13 figures, figures include

A simple parallel prefix algorithm for compact finite-difference schemes

A compact scheme is a discretization scheme that is advantageous in obtaining highly accurate solutions. However, the resulting systems from compact schemes are tridiagonal systems that are difficult to solve efficiently on parallel computers. Considering the almost symmetric Toeplitz structure, a parallel algorithm, simple parallel prefix (SPP), is proposed. The SPP algorithm requires less memory than the conventional LU decomposition and is highly efficient on parallel machines. It consists of a prefix communication pattern and AXPY operations. Both the computation and the communication can be truncated without degrading the accuracy when the system is diagonally dominant. A formal accuracy study was conducted to provide a simple truncation formula. Experimental results were measured on a MasPar MP-1 SIMD machine and on a Cray 2 vector machine. Experimental results show that the simple parallel prefix algorithm is a good algorithm for the compact scheme on high-performance computers

MPI-Vector-IO: Parallel I/O and Partitioning for Geospatial Vector Data

In recent times, geospatial datasets are growing in terms of size, complexity and heterogeneity. High performance systems are needed to analyze such data to produce actionable insights in an efficient manner. For polygonal a.k.a vector datasets, operations such as I/O, data partitioning, communication, and load balancing becomes challenging in a cluster environment. In this work, we present MPI-Vector-IO 1 , a parallel I/O library that we have designed using MPI-IO specifically for partitioning and reading irregular vector data formats such as Well Known Text. It makes MPI aware of spatial data, spatial primitives and provides support for spatial data types embedded within collective computation and communication using MPI message-passing library. These abstractions along with parallel I/O support are useful for parallel Geographic Information System (GIS) application development on HPC platforms

Novel Method for Improving the Capacity of Optical MIMO System Using MGDM

In current local area networks, multimode fibers (MMFs), primarily graded index (GI) MMFs, are the main types of fibers employed for data communications. Due to their enormous bandwidth, it is considered that they are the main channel medium that can offer broadband multiservices using optical multiplexing techniques. Amongst these, mode group diversity multiplexing (MGDM) has been proposed as a way to integrate various services over an MMF network by exciting different groups of modes that can be used as independent and parallel communication channels. In this paper, we study optical multiple-input–multiple-output (O-MIMO) systems using MGDM techniques while also optimizing the launching conditions of light at the fiber inputs and the spot size, radial offset, angular offset, wavelength, and the radii of the segment areas of the detectors. We propose a new approach based on the optimization of launching and detection conditions in order to increase the capacity of an O-MIMO link using the MGDM technique. We propose a (3 $times$ 3) O-MIMO system, where our simulation results show significant improvement in GI MMFs' capacity compared with existing O-MIMO systems. Optical multiple-input-multiple-output multiplexing of parallel communication multichannels over a single multimode fiber network. Optical multiple-input-multiple-output multiplexing of parallel communication multichannels over a single multimode fiber network

Benchmarking hypercube hardware and software

It was long a truism in computer systems design that balanced systems achieve the best performance. Message passing parallel processors are no different. To quantify the balance of a hypercube design, an experimental methodology was developed and the associated suite of benchmarks was applied to several existing hypercubes. The benchmark suite includes tests of both processor speed in the absence of internode communication and message transmission speed as a function of communication patterns