Parallel linear system solution and its application to railway power network simulation

The Streaming SIMD extension (SSE) is a special feature embedded in the Intel Pentium III and IV classes of microprocessors. It enables the execution of SIMD type operations to exploit data parallelism. This article presents improving computation performance of a railway network simulator by means of SSE. Voltage and current at various points of the supply system to an electrified railway line are crucial for design, daily operation and planning. With computer simulation, their time-variations can be attained by solving a matrix equation, whose size mainly depends upon the number of trains present in the system. A large coefficient matrix, as a result of congested railway line, inevitably leads to heavier computational demand and hence jeopardizes the simulation speed. With the special architectural features of the latest processors on PC platforms, significant speed-up in computations can be achieved.

A parallel computation of power system equations

Streaming SIMD Extensions (SSE) is a unique feature embedded in the Pentium III and P4 classes of microprocessors. By fully exploiting SSE, parallel algorithms can be implemented on a standard personal computer and a theoretical speedup of four can be achieved. In this paper, we demonstrate the implementation of a parallel LU matrix decomposition algorithm for solving power systems network equations with SSE and discuss advantages and disadvantages of this approach.

SSE based parallel solution for power systems network equations

Streaming SIMD Extensions (SSE) is a unique feature embedded in the Pentium III class of microprocessors. By fully exploiting SSE, parallel algorithms can be implemented on a standard personal computer and a theoretical speedup of four can be achieved. In this paper, we demonstrate the implementation of a parallel LU matrix decomposition algorithm for solving power systems network equations with SSE and discuss advantages and disadvantages of this approach.

A parallel solution to linear systems

Streaming SIMD Extensions (SSE) is a unique feature embedded in the Pentium III and IV classes of microprocessors. By fully exploiting SSE, parallel algorithms can be implemented on a standard personal computer and a theoretical speedup of four can be achieved. In this paper, we demonstrate the implementation of a parallel LU matrix decomposition algorithm for solving linear systems with SSE and discuss advantages and disadvantages of this approach based on our experimental study.

Parallel solution for railway power network simulation

The Streaming SIMD extension (SSE) is a special feature that is available in the Intel Pentium III and P4 classes of microprocessors. As its name implies, SSE enables the execution of SIMD (Single Instruction Multiple Data) operations upon 32-bit floating-point data therefore, performance of floating-point algorithms can be improved. In electrified railway system simulation, the computation involves the solving of a huge set of simultaneous linear equations, which represent the electrical characteristic of the railway network at a particular time-step and a fast solution for the equations is desirable in order to simulate the system in real-time. In this paper, we present how SSE is being applied to the railway network simulation.

Teaching parallel computing concepts with a desktop computer

Parallel computing is currently used in many engineering problems. However, because of limitations in curriculum design, it is not always possible to offer students specific formal teaching in this topic. Furthermore, parallel machines are still too expensive for many institutions. The latest microprocessors, such as Intel’s Pentium III and IV, embody single instruction multiple-data (SIMD) type parallel features, which makes them a viable solution for introducing parallel computing concepts to students. Final year projects have been initiated utilizing SSE (streaming SIMD extensions) features and it has been observed that students can easily learn parallel programming concepts after going through some programming exercises. They can now experiment with parallel algorithms on their own PCs at home. Keywords