Analysing Astronomy Algorithms for GPUs and Beyond

Astronomy depends on ever increasing computing power. Processor clock-rates have plateaued, and increased performance is now appearing in the form of additional processor cores on a single chip. This poses significant challenges to the astronomy software community. Graphics Processing Units (GPUs), now capable of general-purpose computation, exemplify both the difficult learning-curve and the significant speedups exhibited by massively-parallel hardware architectures. We present a generalised approach to tackling this paradigm shift, based on the analysis of algorithms. We describe a small collection of foundation algorithms relevant to astronomy and explain how they may be used to ease the transition to massively-parallel computing architectures. We demonstrate the effectiveness of our approach by applying it to four well-known astronomy problems: Hogbom CLEAN, inverse ray-shooting for gravitational lensing, pulsar dedispersion and volume rendering. Algorithms with well-defined memory access patterns and high arithmetic intensity stand to receive the greatest performance boost from massively-parallel architectures, while those that involve a significant amount of decision-making may struggle to take advantage of the available processing power.Comment: 10 pages, 3 figures, accepted for publication in MNRA

Implementation Action Plan for organic food and farming research

The Implementation Action Plan completes TP Organics’ trilogy of key documents of the Research Vision to 2025 (Niggli et al 2008) and the Strategic Research Agenda (Schmid et al 2009). The Implementation Action Plan addresses important areas for a successful implementation of the Strategic Research Agenda. It explores the strength of Europe’s organic sector on the world stage with about one quarter of the world’s organic agricultural land in 2008 and accounting for more than half of the global organic market. The aims and objectives of organic farming reflect a broad range of societal demands on the multiple roles of agriculture and food production of not only producing commodities but also ecosystem services. These are important for Europe’s economic success, the resilience of its farms and prosperity in its rural areas. The organic sector is a leading market for quality and authenticity: values at the heart of European food culture. Innovation is important across the EU economy, and no less so within the organic sector. The Implementation Action Plan devotes its third chapter to considering how innovation can be stimulated through organic food and farming research and, crucially, translated into changes in business and agricultural practice. TP Organics argues for a broad understanding of innovation that includes technology, know-how and social/organisational innovations. Accordingly, innovation can involve different actors throughout the food sector. Many examples illustrate innovations in the organic sector includign and beyond technology. The various restrictions imposed by organic standards have driven change and turned organic farms and food businesses into creative living laboratories for smart and green innovations and the sector will continue to generate new examples. The research topics proposed by TP Organics in the Strategic Research Agenda can drive innovation in areas as wide ranging as production practices for crops, technologies for livestock, food processing, quality management, on-farm renewable energy or insights into the effects of consumption of organic products on disease and wellbeing and life style of citizens. Importantly, many approaches developed within the sector are relevant and useful beyond the specific sector. The fourth chapter addresses knowledge management in organic agriculture, focusing on the further development of participatory research methods. Participatory (or trans-disciplinary) models recognise the worth and importance of different forms of knowledge and reduced boundaries between the generators and the users of knowledge, while respecting and benefitting from transparent division of tasks. The emphasis on joint creation and exchange of knowledge makes them valuable as part of a knowledge management toolkit as they have the capacity to enhance the translation of research outcomes into practical changes and lead to real-world progress. The Implementation Action Plan argues for the wider application of participatory methods in publicly-funded research and also proposes some criteria for evaluating participatory research, such as the involvement and satisfaction of stakeholders as well as real improvements in sustainability and delivery of public goods/services. European agriculture faces specific challenges but at the same time Europe has a unique potential for the development of agro-ecology based solutions that must be supported through well focused research. TP Organics believes that the most effective approaches in agriculture and food research will be systems-based, multi- and trans-disciplinary, and that in the development of research priorities, the interconnections between biodiversity, dietary diversity, functional diversity and health must be taken into account. Chapter five of the action plan identifies six themes which could be used to organise research and innovation activities in agriculture under Europe’s 8th Framework Programme on Research Cooperation: • Eco-functional intensification – A new area of agricultural research which aims to harness beneficial activities of the ecosystem to increase productivity in agriculture. • The economics of high output / low input farming Developing reliable economic and environmental assessments of new recycling, renewable-based and efficiency-boosting technologies for agriculture. • Health care schemes for livestock Shifting from therapeutics to livestock health care schemes based on good husbandry and disease prevention. • Resilience and “sustainagility” Dealing with a more rapidly changing environment by focusing on ‘adaptive capacity’ to help build resilience of farmers, farms and production methods. • From farm diversity to food diversity and health and wellbeing of citizens Building on existing initiatives to reconnect consumers and producers, use a ‘whole food chain’ approach to improve availability of natural and authentic foods. • Creating centres of innovation in farming communities A network of centres in Europe applying and developing trans-disciplinary and participatory scientific approaches to support innovation among farmers and SMEs and improving research capacities across Europe

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

Low Power Processor Architectures and Contemporary Techniques for Power Optimization – A Review

The technological evolution has increased the number of transistors for a given die area significantly and increased the switching speed from few MHz to GHz range. Such inversely proportional decline in size and boost in performance consequently demands shrinking of supply voltage and effective power dissipation in chips with millions of transistors. This has triggered substantial amount of research in power reduction techniques into almost every aspect of the chip and particularly the processor cores contained in the chip. This paper presents an overview of techniques for achieving the power efficiency mainly at the processor core level but also visits related domains such as buses and memories. There are various processor parameters and features such as supply voltage, clock frequency, cache and pipelining which can be optimized to reduce the power consumption of the processor. This paper discusses various ways in which these parameters can be optimized. Also, emerging power efficient processor architectures are overviewed and research activities are discussed which should help reader identify how these factors in a processor contribute to power consumption. Some of these concepts have been already established whereas others are still active research areas. © 2009 ACADEMY PUBLISHER

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure