21,141 research outputs found
The future of computing beyond Moore's Law.
Moore's Law is a techno-economic model that has enabled the information technology industry to double the performance and functionality of digital electronics roughly every 2 years within a fixed cost, power and area. Advances in silicon lithography have enabled this exponential miniaturization of electronics, but, as transistors reach atomic scale and fabrication costs continue to rise, the classical technological driver that has underpinned Moore's Law for 50 years is failing and is anticipated to flatten by 2025. This article provides an updated view of what a post-exascale system will look like and the challenges ahead, based on our most recent understanding of technology roadmaps. It also discusses the tapering of historical improvements, and how it affects options available to continue scaling of successors to the first exascale machine. Lastly, this article covers the many different opportunities and strategies available to continue computing performance improvements in the absence of historical technology drivers. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'
Energy challenges for ICT
The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT
The Universe at Extreme Scale: Multi-Petaflop Sky Simulation on the BG/Q
Remarkable observational advances have established a compelling
cross-validated model of the Universe. Yet, two key pillars of this model --
dark matter and dark energy -- remain mysterious. Sky surveys that map billions
of galaxies to explore the `Dark Universe', demand a corresponding
extreme-scale simulation capability; the HACC (Hybrid/Hardware Accelerated
Cosmology Code) framework has been designed to deliver this level of
performance now, and into the future. With its novel algorithmic structure,
HACC allows flexible tuning across diverse architectures, including accelerated
and multi-core systems.
On the IBM BG/Q, HACC attains unprecedented scalable performance -- currently
13.94 PFlops at 69.2% of peak and 90% parallel efficiency on 1,572,864 cores
with an equal number of MPI ranks, and a concurrency of 6.3 million. This level
of performance was achieved at extreme problem sizes, including a benchmark run
with more than 3.6 trillion particles, significantly larger than any
cosmological simulation yet performed.Comment: 11 pages, 11 figures, final version of paper for talk presented at
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Interpretable deep learning for guided structure-property explorations in photovoltaics
The performance of an organic photovoltaic device is intricately connected to
its active layer morphology. This connection between the active layer and
device performance is very expensive to evaluate, either experimentally or
computationally. Hence, designing morphologies to achieve higher performances
is non-trivial and often intractable. To solve this, we first introduce a deep
convolutional neural network (CNN) architecture that can serve as a fast and
robust surrogate for the complex structure-property map. Several tests were
performed to gain trust in this trained model. Then, we utilize this fast
framework to perform robust microstructural design to enhance device
performance.Comment: Workshop on Machine Learning for Molecules and Materials (MLMM),
Neural Information Processing Systems (NeurIPS) 2018, Montreal, Canad
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