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High-Performance Computing Storage System Challenges for Theoreticians.
Abstract not provided.
The Opportunity and Challenge of Serverless Computing and Large Scale Computational Science.
Abstract not provided.
The Reversible Computing Scaling Path: Challenges and Opportunities.
Abstract not provided.
Petaflops Computing: The Key Algorithmic Challenges
The prospect of petaflops-class computers brings to the fore some important algorithmic issues that have been considered in the high performance computing community for several years. Key among them are (1) concurrency (whether the fundamental concurrency of an algorithm is sufficient to keep thousands of processors productively busy); (2) data locality; (3) latency tolerance; and (4) memory and operation count scaling. This introductory presentation will give an overview of these issues.
HPC resources for CMS offline computing: An integration and scalability challenge for the Submission Infrastructure
The computing resource needs of LHC experiments are expected to continue growing significantly during the Run 3 and into the HL-LHC era. The landscape of available resources will also evolve, as High Performance Computing (HPC) and Cloud resources will provide a comparable, or even dominant, fraction of the total compute capacity. The future years present a challenge for the experiments’ resource provisioning models, both in terms of scalability and increasing complexity. The CMS Submission Infrastructure (SI) provisions computing resources for CMS workflows. This infrastructure is built on a set of federated HTCondor pools, currently aggregating 400k CPU cores distributed worldwide and supporting the simultaneous execution of over 200k computing tasks. Incorporating HPC resources into CMS computing represents firstly an integration challenge, as HPC centers are much more diverse compared to Grid sites. Secondly, evolving the present SI, dimensioned to harness the current CMS computing capacity, to reach the resource scales required for the HLLHC phase, while maintaining global flexibility and efficiency, will represent an additional challenge for the SI. To preventively address future potential scalability limits, the SI team regularly runs tests to explore the maximum reach of our infrastructure. In this note, the integration of HPC resources into CMS offline computing is summarized, the potential concerns for the SI derived from the increased scale of operations are described, and the most recent results of scalability test on the CMS SI are reported.
Exascale Computing and Data Handling: Challenges and Opportunities for Weather and Climate Prediction
The emergence of exascale computing and artificial intelligence offer tremendous potential to significantly advance Earth system prediction capabilities. However, enormous challenges must be overcome to adapt models and prediction systems to use these new technologies effectively. A 2022 WMO report on exascale computing recommends “urgency in dedicating efforts and attention to disruptions associated with evolving computing technologies that will be increasingly difficult to overcome, threatening continued advancements in weather and climate prediction capabilities.” Further, the explosive growth in data from observations, model and ensemble output, and postprocessing threatens to overwhelm the ability to deliver timely, accurate, and precise information needed for decision-making. Artificial intelligence (AI) offers untapped opportunities to alter how models are developed, observations are processed, and predictions are analyzed and extracted for decision-making. Given the extraordinarily high cost of computing, growing complexity of prediction systems, and increasingly unmanageable amount of data being produced and consumed, these challenges are rapidly becoming too large for any single institution or country to handle. This paper describes key technical and budgetary challenges, identifies gaps and ways to address them, and makes a number of recommendations.
Cyber Security Challenges in Using Cloud Computing in the Electric Utility Industry
This document presents a summary of cyber security challenges associated with utilization of cloud computing in the electric utility industry. The reader is assumed to have familiarity with both the operational processes of the utility industry and cloud computing, including public (e.g., Amazon EC2, Microsoft Azure) or private clouds. We also assume that the reader is familiar with basic networking and web infrastructure constructs. Further, we recommend that the reader reviews the information presented in (Birman 2011; NIST 2012).
Reconstructing thermal quantum quench dynamics from pure states
Simulating the nonequilibrium dynamics of thermal states is a fundamental problem across scales from high-energy to condensed-matter physics. Quantum computers may provide a way to solve this problem efficiently. Preparing a thermal state on a quantum computer is challenging, but there exist methods to circumvent this by computing a weighted sum of time-dependent matrix elements in a convenient basis. Further, while the number of basis states can be large, in this paper we show that it can be reduced by simulating only the largest density matrix elements by weight, capturing the density matrix to a specified precision. Leveraging Hamiltonian symmetries enables further reductions. This approach paves the way to more accurate thermal-state dynamics simulations on near-term quantum hardware.
Prepare Ground States of Highly Frustrated Magnetic Clusters on Quantum Computers
Solving challenging problems in physical, chemical, and materials sciences is one of the most promising applications of quantum utility that can be realized on current noisy hardware, considering (i) the direct map (encoding) from the quantum particles and their interactions to the qubits and their entangling gates and (ii) the rapidly improved quantum hardware and advanced error-mitigation techniques. Understanding quantum spin liquid in frustrated magnetic materials is a longstanding challenge in condensed matter physics and the nature of the ground-state phases is highly debated among researchers. Using IBM quantum computers with superconducting qubits, we implemented a variational quantum eigensolver (VQE) algorithm to prepare the ground states of two 12-site cluster approximations of these highly frustrated magnetic materials. The interaction graphs of the two corresponding Hamiltonians are (a) the six-pointed star graph (a unit cell of the kagome lattice) and (b) the cuboctahedral graph (the kagome on a sphere). These are also two instances of Quantum Max Cut problem. With the VQE based on the Hamiltonian variational ansatz acting on a valence bond solid initial trial state, we prepared the ground states and obtained the exact ground energy on simulator and high accuracy on noisy hardware. The deep ansatz necessary to reach the ground state of the cuboctahedral graph indicates that it is a hard instance of Quantum Max Cut.
Challenges in reducing the computational time of QSTS simulations for distribution system analysis.
The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modelling and impact analysis. Unlike conventional scenario - based studies , quasi - static time - series (QSTS) simulation s can realistically model time - dependent voltage controllers and the diversity of potential impacts that can occur at different times of year . However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1 - second resolution is often required , which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled . This computational burden is a clear l imitation to the adoption of QSTS simulation s in interconnection studies and for determining optimal control solutions for utility operations . Our ongoing research to improve the speed of QSTS simulation has revealed many unique aspects of distribution system modelling and sequential power flow analysis that make fast QSTS a very difficult problem to solve. In this report , the most relevant challenges in reducing the computational time of QSTS simulations are presented: number of power flows to solve, circuit complexity, time dependence between time steps, multiple valid power flow solutions, controllable element interactions, and extensive accurate simulation analysis.
The impact of different neutrino transport methods on multidimensional core-collapse supernova simulations
Neutrinos play a crucial role in the core-collapse supernova (CCSN) explosion mechanism. The requirement of accurately calculating the transport of neutrinos makes simulations of the CCSN mechanism extremely challenging and computationally expensive. Historically, this stiff challenge has been met by making approximations to the full transport equation. In this work, we compare CCSN simulations in one- and two-dimensions with three approximate neutrino transport schemes, each implemented in the FLASH simulation framework. We compare a two-moment M1 scheme with an analytic closure (M1), the isotropic diffusion source approximation (IDSA), and the advanced spectral leakage method. We identify and discuss the advantages and disadvantages of each scheme. For each approximate transport scheme, we use identical grid setups, hydrodynamics, and gravity solvers to investigate the transport effects on supernova shock dynamics and neutrino quantities. We find that the transport scheme has a small effect on the evolution of protoneutron star (PNS) radius, PNS mass, and the mass accretion rate. The neutrino luminosities, mean energies, and shock radii have a ~10%–20% quantitative difference but the overall qualitative trends are fairly consistent between all three approximations. We find larger differences in the gain region properties, including the gain region mass and the net heating rate in the gain region, as well as the strength of PNS convection in the core. We investigate the progenitor, nuclear equation of state, and stochastic perturbation dependence of our simulations and find similar magnitudes of impact on key quantities. We also compare the computational expense of the various approximations.
Comparison of sparse domain approaches for 4D SPECT dynamic image reconstruction
Purpose: Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of being computationally more challenging. Dynamic single photon emission computed tomography (SPECT) reconstruction is particularly difficult because of the limitations in the sampling geometry present in most existing scanners. We have developed an algorithm Spline Initialized Factor Analysis of Dynamic Structures (SIFADS) that is a matrix factorization method for reconstructing the dynamics of tracers in tissues and blood directly from the projections in dynamic cardiac SPECT, without first resorting to any 3D reconstruction. Methods: SIFADS is different from "pure" factor analysis in dynamic structures (FADS) in that it employs a dedicated spline-based pre-initialization. In this paper, we analyze the convergence properties of SIFADS and FADS using multiple metrics. The performances of the two approaches are evaluated for numerically simulated data and for real dynamic SPECT data from canine and human subjects. Results: For SIFADS, metrics analyzed for reconstruction algorithm convergence show better features of the metric curves vs iterations. In addition, SIAFDS provides better tissue segmentations than that from pure FADS. Measured computational times are also typically better for SIFADS implementations than those with pure FADS. Conclusion: The analysis supports the utility of the pre-initialization of a factorization algorithm for better dynamic SPECT image reconstruction.
Quantum computing tutorial on quantum computing for 'space mission challenges for information technology' (SMC-IT'03)
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Intensity Frontier Computing at Fermilab
The Intensity Frontier (IF) experiments at Fermilab require computing, software, data handling, and infrastructure development for detector and beamline design and to extract maximum scientific output from the data. The emphasis of computing at Fermilab for many years has been on the Tevatron collider Run 2 experiments and CMS. Using the knowledge and experience gained from those experiments as well as new computing developments, preparations for computing for IF experiments are ramping up. There are many challenges in IF computing. These include event generators and detector simulation, beamline simulation, detector design and optimization, data acquisition, data handling, data analysis, and all of the associated services required. In this presentation the computing challenges and requirements will be described and the approaches being taken to address them will be shown.
Ocean Modeling and Visualization on Massively Parallel Computer
Climate modeling is one of the grand challenges of computational science, and ocean modeling plays an important role in both understanding the current climatic conditions and predicting future climate change.
Role of High-End Computing in Meeting NASA's Science and Engineering Challenges
High-End Computing (HEC) has always played a major role in meeting the modeling and simulation needs of various NASA missions. With NASA's newest 62 teraflops Columbia supercomputer, HEC is having an even greater impact within the Agency and beyond. Significant cutting-edge science and engineering simulations in the areas of space exploration, Shuttle operations, Earth sciences, and aeronautics research, are already occurring on Columbia, demonstrating its ability to accelerate NASA s exploration vision. The talk will describe how the integrated supercomputing production environment is being used to reduce design cycle time, accelerate scientific discovery, conduct parametric analysis of multiple scenarios, and enhance safety during the life cycle of NASA missions.