Lightwave communications is a necessity for the information age. Optical links provide enormous bandwidth, and the optical fiber is the only medium that can meet the modern society's needs for transporting massive amounts of data over long distances. Applications range from global high-capacity networks, which constitute the backbone of the internet, to the massively parallel interconnects that provide data connectivity inside datacenters and supercomputers. Optical communications is a diverse and rapidly changing field, where experts in photonics, communications, electronics, and signal processing work side by side to meet the ever-increasing demands for higher capacity, lower cost, and lower energy consumption, while adapting the system design to novel services and technologies. Due to the interdisciplinary nature of this rich research field, Journal of Optics has invited 16 researchers, each a world-leading expert in their respective subfields, to contribute a section to this invited review article, summarizing their views on state-of-the-art and future developments in optical communications.

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Roadmap of optical communications

Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics.

Exascale computing and big data

http://absimage.aps.org/image/MAR05/MWS_MAR05-2004-005765.pdf

Superconducting quantum bits

Computer simulations are growing in sophistication and producing results of ever greater fidelity. This trend has been enabled by advances in numerical methods and increasing computing power. Yet these advances come with several costs including massive increases in data size, difficulties examining output data, challenges in configuring simulation runs, and difficulty debugging running codes. Interactive visualization tools, like ParaView, have been used for post-processing of simulation results. However, the increasing data sizes, and limited storage and bandwidth make high fidelity post-processing impractical. In situ analysis is recognized as one of the ways to address these challenges. In situ analysis moves some of the post-processing tasks in line with the simulation code thus short circuiting the need to communicate the data between the simulation and analysis via storage. ParaView Catalyst is a data processing and visualization library that enables in situ analysis and visualization. Built on and designed to interoperate with the standard visualization toolkit VTK and the ParaView application, Catalyst enables simulations to intelligently perform analysis, generate relevant output data, and visualize results concurrent with a running simulation. In this paper, we provide an overview of the Catalyst framework and some of the success stories.

ParaView Catalyst: Enabling In Situ Data Analysis and Visualization

The advent of petascale computing has introduced new challenges (eg heterogeneity, system failure) for programming scalable parallel applications Increased complexity and dynamism in science and engineering applications of today have further exacerbated the situation Addressing these challenges requires more emphasis on concepts that were previously of secondary importance, including migratability, adaptivity, and runtime system introspection In this paper, we leverage our experience with these concepts to demonstrate their applicability and efficacy for real world applications Using the Charm++ parallel programming framework, we present details on how these concepts can lead to development of applications that scale irrespective of the rough landscape of supercomputing technology Empirical evaluation presented in this paper spans many mini-applications and real applications executed on modern supercomputers including Blue Gene/Q, Cray XE6, and Stampede

/pdf/parallel-programming-with-migratable-objects-charm-in-2w4p8o60ke.pdf

Parallel programming with migratable objects: charm++ in practice

Exascale computing systems are essential for the scientific fields that will transform the 21st century global economy, including energy, biotechnology, nanotechnology, and materials science. Progress in these fields is predicated on the ability to perform advanced scientific and engineering simulations, and analyze the deluge of data. On July 29, 2013, ASCAC was charged by Patricia Dehmer, the Acting Director of the Office of Science, to assemble a subcommittee to provide advice on exascale computing. This subcommittee was directed to return a list of no more than ten technical approaches (hardware and software) that will enable the development of a system that achieves the Department's goals for exascale computing. Numerous reports over the past few years have documented the technical challenges and the non¬-viability of simply scaling existing computer designs to reach exascale. The technical challenges revolve around energy consumption, memory performance, resilience, extreme concurrency, and big data. Drawing from these reports and more recent experience, this ASCAC subcommittee has identified the top ten computing technology advancements that are critical to making a capable, economically viable, exascale system.

DOE Advanced Scientific Computing Advisory Subcommittee (ASCAC) Report: Top Ten Exascale Research Challenges

The rapid development of quantum computing (QC) in the NISQ era urgently demands a low-level benchmark suite and insightful evaluation metrics for characterizing the properties of prototype NISQ devices, the efficiency of QC programming compilers, schedulers and assemblers, and the capability of quantum simulators in a classical computer. In this work, we fill this gap by proposing a low-level, easy-to-use benchmark suite called QASMBench based on the OpenQASM assembly representation. It consolidates commonly used quantum routines and kernels from a variety of domains including chemistry, simulation, linear algebra, searching, optimization, arithmetic, machine learning, fault tolerance, cryptography, etc., trading-off between generality and usability. To analyze these kernels in terms of NISQ device execution, in addition to circuit width and depth, we propose four circuit metrics including gate density, retention lifespan, measurement density, and entanglement variance, to extract more insights about the execution efficiency, the susceptibility to NISQ error, and the potential gain from machine-specific optimizations. Most of the QASMBench application code can be launched and verified in IBM-Q directly. With the help from q-convert, QASMBench can be evaluated on various platforms and simulation environments. QASMBench is released at: this http URL.

QASMBench: A Low-level QASM Benchmark Suite for NISQ Evaluation and Simulation

Los Alamos and Sandia National Laboratories have formed a new high performance computing center, the Alliance for Computing at the Extreme Scale (ACES). The two labs will jointly architect, develop, procure and operate capability systems for DOE's Advanced Simulation and Computing Program. This presentation will discuss a petascale production capability system, Cielo, that will be deployed in late 2010, and a new partnership with Cray on advanced interconnect technologies.

The Alliance for Computing at the Extreme Scale.

Many proposals to scale quantum technology rely on modular or distributed designs where individual quantum processors, called nodes, are linked together to form one large multinode quantum computer (MNQC). One scalable method to construct an MNQC is using superconducting quantum systems with optical interconnects. However, a key limiting factor of these machines will be internode gates, which may be two to three orders of magnitude noisier and slower than local operations. Surmounting the limitations of internode gates will require a range of techniques, including improvements in entanglement generation, the use of entanglement distillation, and optimized software and compilers, and it remains unclear how improvements to these components interact to aﬀect overall system performance, what performance from each is required, or even how to quantify the performance of a component. In this paper, we employ a ‘co-design’ inspired approach to quantify overall MNQC performance in terms of hardware models of internode links, entanglement distillation, and local architecture. In the particular case of superconducting MNQCs with microwave-to-optical interconnects, we uncover a tradeoﬀ between entanglement generation and distillation that threatens to degrade MNQC performance. We show how to navigate this tradeoﬀ in the context of algorithm performance, layout how compilers and software should optimize the balance between local gates and internode gates, and discuss when noisy quantum internode links have an advantage over purely classical links. Using these results, we introduce a research roadmap for the realization of early MNQCs, which illustrates potential improvements to the hardware and software of MNQCs and outlines criteria for evaluating the improvement landscape, from progress in entanglement generation to the use of quantum memory in entanglement distillation and dedicated algorithms such as distributed quantum phase estimation. While we focus on superconducting devices with optical interconnects, our approach is general across MNQC implementations

James A. Ang

Papers

DOE Advanced Scientific Computing Advisory Subcommittee (ASCAC) Report: Top Ten Exascale Research Challenges

QASMBench: A Low-level QASM Benchmark Suite for NISQ Evaluation and Simulation

The Alliance for Computing at the Extreme Scale.

Eqc

Architectures for Multinode Superconducting Quantum Computers