Performance Evaluation Of Parallel Algorithms Research Articles

Design, analysis and performance evaluation of parallel algorithms for solving triangular linear systems on multicore platforms

In this paper, we focus on the schedulings of 2-steps graph with constant task cost obtained when parallelizing algorithm solving a triangular linear system. We present three scheduling approaches having the same least theoretical execution time. The first is designed through solving a 0-1 integer problem by Mixed Integer Programming (MIP), the second is based on the Critical Path Algorithm (CPA) and the third is a particular Column-Oriented Scheduling (COS). The MIP approach experiments were carried out and confirmed that the makespan values of the MIP scheduling coincide with those of the corresponding lower bound already reached. Experimental results of the last two approaches detailing both makespans and efficiencies are presented and show that their practical performances differ though they are theoretically identical. We compare also these results to those of the appropriate procedure into so-called PLASMA library (Parallel Linear Algebra for Scalable Multi-core Architectures).

Full tensor gravity gradiometry data inversion: Performance analysis of parallel computing algorithms

We apply reweighted inversion focusing to full tensor gravity gradiometry data using message-passing interface (MPI) and compute unified device architecture (CUDA) parallel computing algorithms, and then combine MPI with CUDA to formulate a hybrid algorithm. Parallel computing performance metrics are introduced to analyze and compare the performance of the algorithms. We summarize the rules for the performance evaluation of parallel algorithms. We use model and real data from the Vinton salt dome to test the algorithms. We find good match between model and real density data, and verify the high efficiency and feasibility of parallel computing algorithms in the inversion of full tensor gravity gradiometry data.

To a Performance Evaluation of Parallel Algorithms in Now

A recent trend in high performance computing (HPC) is to use networks of workstations (NOW) as a cheaper alternative to massively parallel multiprocessors or supercomputers. In such parallel systems (NOW's) individual workstations are connected through widely used communication standard networks and co-operate to solve one large problem. Every workstation is treated similarly as a processing element in a conventional multiprocessor system. To make the whole system appear to the applications as a single parallel computing engine (a virtual parallel system), run-time environments such as PVM (Parallel virtual machine), MPI (Message passing interfaces) are often used to provide an extra layer of abstraction. In this paper, we discuss a new performance evaluation method on the example of multidimensional DFFT (Discrete Fast Fourier Transform) in a NOW's based on Intel's personal computers.

Advances in Parallel Algorithms.

Supercomputers, pipelining and farming Parallel computing paradigms Neural network paradigm Parallel task-driven versus process-driven approaches Parallel branch-and-bound method Parallel divide-and-conquer approach Parallel tree and graph searching Parallel algebraic transformation approach Parallel approximate and probabilistic methods Parallel dynamic programming method Parallel exploration of maze Parallel graph reduction algorithms Parallel algorithms for matrix operations Parallel pattern recognition methods Parallel stable marriage problem Parallel travelling salesman problem Neural network approach Computational complexity of parallel algorithms Performance evaluation of parallel algorithms.

Simulation of parallel systems: PSEE (Parallel System Evaluation Environment)

We have developed an interactive environment, PSEE (Parallel System Evaluation Environment) to study the behaviour of linked oriented parallel systems. It allows parallel programming simulation and performance evaluation of parallel algorithms in parallel architectures. PSEE permits to manage the main characteristic parameters involved in the system to show the tuning grade of the measurement, visualization and modification of parallel system parameters.

SIMPAC-T : A simulator for multitransputer systems

Performance evaluation of parallel algorithms and architectures has several advantages. We present an event driven simulator for performance evaluation and modelling of multitransputer systems. The system topology and architectural details are input to the simulator. The application program is mapped by the user on to the system using the higher level primitives of the simulation language provided. The simulator produces performance metrics of resource (processor, communication link) utilization besides process sojourns in various states. A trace file containing the log of the system snapshots is output which can be used for various analyses by the user and the system designer.

SHAMP: An experimental shared memory multimicroprocessor system for performance evaluation of parallel algorithms

In this paper, the design and implementation of a single shared bus, shared memory multiprocessing system using Intel's single board computers is presented. The hardware configuration and the operating system developed to execute the parallel algorithms are discussed. The performance evaluation studies carried out on shamp are outlined.