DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives

We present a new parallel algorithm for probabilistic graphical model optimization. The algorithm relies on data-parallel primitives (DPPs), which provide portable performance over hardware architecture. We evaluate results on CPUs and GPUs for an image segmentation problem. Compared to a serial baseline, we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare our performance to a reference, OpenMP-based algorithm, and find speedups of up to 7X (CPU).Comment: LDAV 2018, October 201

The Design of a System Architecture for Mobile Multimedia Computers

This chapter discusses the system architecture of a portable computer, called Mobile Digital Companion, which provides support for handling multimedia applications energy efficiently. Because battery life is limited and battery weight is an important factor for the size and the weight of the Mobile Digital Companion, energy management plays a crucial role in the architecture. As the Companion must remain usable in a variety of environments, it has to be flexible and adaptable to various operating conditions. The Mobile Digital Companion has an unconventional architecture that saves energy by using system decomposition at different levels of the architecture and exploits locality of reference with dedicated, optimised modules. The approach is based on dedicated functionality and the extensive use of energy reduction techniques at all levels of system design. The system has an architecture with a general-purpose processor accompanied by a set of heterogeneous autonomous programmable modules, each providing an energy efficient implementation of dedicated tasks. A reconfigurable internal communication network switch exploits locality of reference and eliminates wasteful data copies

Brook Auto: High-Level Certification-Friendly Programming for GPU-powered Automotive Systems

Modern automotive systems require increased performance to implement Advanced Driving Assistance Systems (ADAS). GPU-powered platforms are promising candidates for such computational tasks, however current low-level programming models challenge the accelerator software certification process, while they limit the hardware selection to a fraction of the available platforms. In this paper we present Brook Auto, a high-level programming language for automotive GPU systems which removes these limitations. We describe the challenges and solutions we faced in its implementation, as well as a complete evaluation in terms of performance and productivity, which shows the effectiveness of our method.This work has been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P and the HiPEAC Network of Excellence.Peer ReviewedPostprint (author's final draft

Octopus - an energy-efficient architecture for wireless multimedia systems

Multimedia computing and mobile computing are two trends that will lead to a new application domain in the near future. However, the technological challenges to establishing this paradigm of computing are non-trivial. Personal mobile computing offers a vision of the future with a much richer and more exciting set of architecture research challenges than extrapolations of the current desktop architectures. In particular, these devices will have limited battery resources, will handle diverse data types, and will operate in environments that are insecure, dynamic and which vary significantly in time and location. The approach we made to achieve such a system is to use autonomous, adaptable modules, interconnected by a switch rather than by a bus, and to offload as much as work as possible from the CPU to programmable modules that is placed in the data streams. A reconfigurable internal communication network switch called Octopus exploits locality of reference and eliminates wasteful data copies

Parallel Astronomical Data Processing with Python: Recipes for multicore machines

High performance computing has been used in various fields of astrophysical research. But most of it is implemented on massively parallel systems (supercomputers) or graphical processing unit clusters. With the advent of multicore processors in the last decade, many serial software codes have been re-implemented in parallel mode to utilize the full potential of these processors. In this paper, we propose parallel processing recipes for multicore machines for astronomical data processing. The target audience are astronomers who are using Python as their preferred scripting language and who may be using PyRAF/IRAF for data processing. Three problems of varied complexity were benchmarked on three different types of multicore processors to demonstrate the benefits, in terms of execution time, of parallelizing data processing tasks. The native multiprocessing module available in Python makes it a relatively trivial task to implement the parallel code. We have also compared the three multiprocessing approaches - Pool/Map, Process/Queue, and Parallel Python. Our test codes are freely available and can be downloaded from our website.Comment: 15 pages, 7 figures, 1 table, "for associated test code, see http://astro.nuigalway.ie/staff/navtejs", Accepted for publication in Astronomy and Computin