MPI vs OpenMP: Un caso de estudio sobre la generación del conjunto de Mandelbrot

Nowadays, some of the most popular tools for parallel programming are Message Passing Interface and Open Multi-Processing. It is of interest to compare these tools in solving the same kind of problems, because of the use of different approaches to inter-task communication. This work attempts to contribute to this goal by running trials in a centralized shared memory architecture in the case of problems with an entirely parallel solution. The selected case study was the parallel computation of Mandelbrot set. Trials were conducted for different iteration limits, processors amount, and C++ implementation variants. The results show better performance in the case of Open Multi-Processing.Algunas de las herramientas más populares hoy en día para la programación paralela son Interfaz de Paso de Mensajes y Multiprocesamiento Abierto. Es de interés comparar estas herramientas en la resolución de los mismos tipos de problemas, debido a la utilización de diferentes enfoques en la comunicación entre tareas. Este trabajo tiene como objetivo contribuir a este empeño al ejecutar pruebas en una arquitectura de memoria compartida y centralizada en el caso de problemas con una solución completamente paralela. El caso de estudio seleccionado fue la computación paralela del conjunto de Mandelbrot. Las pruebas se realizaron para diferentes límites de iteración, cantidad de procesadores y variantes de implementación en C++. Los resultados muestran un mejor desempeño en el caso de Multiprocesamiento Abierto

Investigation of Parallel Data Processing Using Hybrid High Performance CPU + GPU Systems and CUDA Streams

The paper investigates parallel data processing in a hybrid CPU+GPU(s) system using multiple CUDA streams for overlapping communication and computations. This is crucial for efficient processing of data, in particular incoming data stream processing that would naturally be forwarded using multiple CUDA streams to GPUs. Performance is evaluated for various compute time to host-device communication time ratios, numbers of CUDA streams, for various numbers of threads managing computations on GPUs. Tests also reveal benefits of using CUDA MPS for overlapping communication and computations when using multiple processes. Furthermore, using standard memory allocation on a GPU and Unified Memory versions are compared, the latter including programmer added prefetching. Performance of a hybrid CPU+GPU version as well as scaling across multiple GPUs are demonstrated showing good speed-ups of the approach. Finally, the performance per power consumption of selected configurations are presented for various numbers of streams and various relative performances of GPUs and CPUs