As quantum computers increase in size, the total energy used by a quantum data center, including the cooling, will become a greater concern. The cooling requirements of quantum computers, which operate at temperatures near absolute zero, are determined by computing system parameters, including the number and type of physical qubits, the packaging efficiency of the system, and the split between circuits operating at cryogenic temperatures and those operating at room temperature. When combined with thermal system parameters such as cooling efficiency and cryostat heat transfer, the total energy use can be determined using a first-principles energy model. These models show that cooling of quantum computers differs in two fundamental ways from conventional data centers: (1) the energy required for cooling is much greater than the energy required for computation, and (2) the cooling loads are sensitive to the computational architecture. The temperature requirements for different qubit types can change energy requirements by orders of magnitude. Power use and computational power, as quantified by quantum volume, are analytically correlated. Approaches are identified for minimizing energy use in integrated quantum systems relative to computational power. Designing a sustainable quantum computer will require both efficient cooling and system design that minimizes cooling requirements.

Abstract Recently, dissipation elements have been gaining popularity as a mechanism for measurement of fundamental properties of turbulent flow, such as turbulence length scales and zonal partitioning. Dissipation elements segment a domain according to the source and destination of streamlines in the gradient flow field of a scalar function f : → ℝ. They have traditionally been computed by numerically integrating streamlines from the center of each voxel in the positive and negative gradient directions, and grouping those voxels whose streamlines terminate at the same extremal pair. We show that the same structures map well to combinatorial topology concepts developed recently in the visualization community. Namely, dissipation elements correspond to sets of cells of the Morse‐Smale complex. The topology‐based formulation enables a more exploratory analysis of the nature of dissipation elements, in particular, in understanding their stability with respect to small scale variations. We present two examples from combustion science that raise significant questions about the role of small scale perturbation and indeed the definition of dissipation elements themselves.

Fast growing large-scale systems enable scientific applications to run at a much larger scale and accordingly produce gigantic volumes of simulation output. Such data imposes a grand challenge to post-processing tasks such as visualization and data analysis, because these tasks are often performed at a host machine that is remotely located and equipped with much less memory and storage resources. During the simulation runs, it is also desirable for scientists to be able to interactively monitor and steer the progress of simulation. This requires scientific data to be represented in an efficient form for initial exploration and computation steering. In this paper, we propose DynaM a software framework that can represent scientific data in a multiresolution form, and dynamically organize data blocks into an optimized layout for efficient scientific analysis. DynaM supports a convolution-based multiresolution data representation for abstracting scientific data for visualization at a wide spectrum of resolution. To support the efficient generation and retrieval of different data granularities from such representation, a dynamic data organization in DynaM is enabled to cater distinct peculiarities of different size data blocks for efficient and balanced I/O performance. Our experimental results demonstrate that DynaM can efficiently represent large scientific dataset and speed up the visualization of multidimensional scientific data. An up to 29 times speedup is achieved on Jaguar supercomputer at Oak Ridge National Laboratory.

Parallel I/O library performance can vary greatly in response to user-tunable parameter values such as aggregator count, file count, and aggregation strategy. Unfortunately, manual selection of these values is time consuming and dependent on characteristics of the target machine, the underlying file system, and the dataset itself. Some characteristics, such as the amount of memory per core, can also impose hard constraints on the range of viable parameter values. In this work we address these problems by using machine learning techniques to model the performance of the PIDX parallel I/O library and select appropriate tunable parameter values. We characterize both the network and I/O phases of PIDX on a Cray XE6 as well as an IBM Blue Gene/P system. We use the results of this study to develop a machine learning model for parameter space exploration and performance prediction.

Large-scale scientific applications on High End Computing systems produce a large volume of highly complex datasets. Such data imposes a grand challenge to conventional storage systems for the need of efficient I/O solutions during both the simulation runtime and data post-processing phases. With the mounting needs of scientific discovery, the read performance of large-scale simulations has becomes a critical issue for the HPC community. In this study, we propose a system-aware optimized data organization strategy that can organize data blocks of multidimensional scientific data efficiently based on simulation output and the underlying storage systems, thereby enabling efficient scientific analytics. Our experimental results demonstrate a performance speedup up to 72 times for the combustion simulation S3D, compared to the logically contiguous data layout.

Current I/O techniques have pushed the write performance close to the system peak, but they usually overlook the read side of problem. With the mounting needs of scientific discovery, it is important to provide good read performance for many common access patterns. Such demand requires an organization scheme that can effectively utilize the underlying storage system. However, the mismatch between conventional data layout on disk and common scientific access patterns leads to significant performance degradation when a subset of data is accessed. To this end, we design a system-aware Optimized Chunking model, which aims to find an optimized organization that can strike for a good balance between data transfer efficiency and processing overhead. To enable such model for scientific applications, we propose SMART-IO, a two-level data organization framework that can organize the blocks of multidimensional data efficiently. This scheme can adapt data layouts based on data characteristics and underlying storage systems, and enable efficient scientific analytics. Our experimental results demonstrate that SMART-IO can significantly improve the read performance for challenging access patterns, and speed up data analytics. For a mission critical combustion simulation code S3D, Smart-IO achieves up to 72 times speedup for planar reads of a 3-D variable compared to the logically contiguous data layout.

With the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading computations to a set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution of various analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. We demonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight.

Hierarchical, multiresolution data representations enable interactive analysis and visualization of large-scale simulations. One promising application of these techniques is to store high performance computing simulation output in a hierarchical Z (HZ) ordering that translates data from a Cartesian coordinate scheme to a one-dimensional array ordered by locality at different resolution levels. However, when the dimensions of the simulation data are not an even power of 2, parallel HZ ordering produces sparse memory and network access patterns that inhibit I/O performance. This work presents a new technique for parallel HZ ordering of simulation datasets that restructures simulation data into large (power of 2) blocks to facilitate efficient I/O aggregation. We perform both weak and strong scaling experiments using the S3D combustion application on both Cray-XE6 (65,536 cores) and IBM Blue Gene/P (131,072 cores) platforms. We demonstrate that data can be written in hierarchical, multiresolution format with performance competitive to that of native data-ordering methods.

Hybridization is the process of converting an application with a single level of parallelism to an application with multiple levels of parallelism. Over the past 15 years a majority of the applications that run on High Performance Computing systems have employed MPI for all of the parallelism within the application. In the Peta-Exascale computing regime, effective utilization of the hardware requires multiple levels of parallelism matched to the macro architecture of the system to achieve good performance. A hybridized code base is performance portable when sufficient parallelism is expressed in an architecture agnostic form to achieve good performance on a range of available systems. The hybridized S3D code is performance portable across today's leading many core and GPU accelerated systems. The OpenACC framework allows a unified code base to be deployed for either (Manycore CPU or Manycore CPU+GPU) while permitting architecture specific optimizations to expose new dimensions of parallelism to be utilized.

Effective analysis and visualization of time-varying multivariate data is crucial for understanding complex and dynamic variable interaction and temporal evolution. Advances made in this area are mainly on query-driven visualization and correlation exploration. Solutions and techniques that investigate the important aspect of causal relationships among variables have not been sought. In this paper, we present a new approach to analyzing and visualizing time-varying multivariate volumetric and particle data sets through the study of information flow using the information-theoretic concept of transfer entropy. We employ time plot and circular graph to show information transfer for an overview of relations among all pairs of variables. To intuitively illustrate the influence relation between a pair of variables in the visualization, we modulate the color saturation and opacity for volumetric data sets and present three different visual representations, namely, ellipse, smoke, and metaball, for particle data sets. We demonstrate this information-theoretic approach and present our findings with three time-varying multivariate data sets produced from scientific simulations.

Current simulations of turbulent flames are instrumented with particles to capture the dynamic behavior of combustion in next-generation engines. Categorizing the set of many millions of particles, each of which is featured with a history of its movement positions and changing thermo-chemical states, helps understand the turbulence mechanism. We introduce a dual-space method to analyze such data, starting by clustering the time series curves in the phase space of the data, and then visualizing the corresponding trajectories of each cluster in the physical space. To cluster time series curves, we adopt a model-based clustering technique in a two-stage scheme. In the first stage, the characteristics of shape and relative position are particularly concerned in classifying the time series curves, and in the second stage, within each group of curves, clustering is further conducted based on how the curves change over time. In our work, we perform the model-based clustering in a semi-supervised manner. Users' domain knowledge is integrated through intuitive interaction tools to steer the clustering process. Our dual-space method has been used to analyze particle data in combustion simulations and can also be applied to other scientific simulations involving particle trajectory analysis work.

The IDX data format provides efficient, cache oblivious, and progressive access to large-scale scientific datasets by storing the data in a hierarchical Z (HZ) order. Data stored in IDX format can be visualized in an interactive environment allowing for meaningful explorations with minimal resources. This technology enables real-time, interactive visualization and analysis of large datasets on a variety of systems ranging from desktops and laptop computers to portable devices such as iPhones/iPads and over the web. While the existing ViSUS API for writing IDX data is serial, there are obvious advantages of applying the IDX format to the output of large scale scientific simulations. We have therefore developed PIDX - a parallel API for writing data in an IDX format. With PIDX it is now possible to generate IDX datasets directly from large scale scientific simulations with the added advantage of real-time monitoring and visualization of the generated data. In this paper, we provide an overview of the IDX file format and how it is generated using PIDX. We then present a data model description and a novel aggregation strategy to enhance the scalability of the PIDX library. The S3D combustion application is used as an example to demonstrate the efficacy of PIDX for a real-world scientific simulation. S3D is used for fundamental studies of turbulent combustion requiring exceptionally high fidelity simulations. PIDX achieves up to 18 GiB/s I/O throughput at 8,192 processes for S3D to write data out in the IDX format. This allows for interactive analysis and visualization of S3D data, thus, enabling in situ analysis of S3D simulation.

Large scale scientific applications are often bottlenecked due to the writing of checkpoint-restart data. Much work has been focused on improving their write performance. With the mounting needs of scientific discovery from these datasets, it is also important to provide good read performance for many common access patterns, which requires effective data organization. To address this issue, we introduce Elastic Data Organization (EDO), which can transparently enable different data organization strategies for scientific applications. Through its flexible data ordering algorithms, EDO harmonizes different access patterns with the underlying file system. Two levels of data ordering are introduced in EDO. One works at the level of data groups (a.k.a process groups). It uses Hilbert Space Filling Curves (SFC) to balance the distribution of data groups across storage targets. Another governs the ordering of data elements within a data group. It divides a data group into sub chunks and strikes a good balance between the size of sub chunks and the number of seek operations. Our experimental results demonstrate that EDO is able to achieve balanced data distribution across all dimensions and improve the read performance of multidimensional datasets in scientific applications.

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.

Statistical analysis is widely used for countless scientific applications in order to analyze and infer meaning from data. A key challenge of any statistical analysis package aimed at large-scale, distributed data is to address the orthogonal issues of parallel scalability and numerical stability. In this paper we derive a series of formulas that allow for single-pass, yet numerically robust, pairwise parallel and incremental updates of both arbitrary-order centered statistical moments and co-moments. Using these formulas, we have built an open source parallel statistics framework that performs principal component analysis (PCA) in addition to computing descriptive, correlative, and multi-correlative statistics. The results of a scalability study demonstrate numerically stable, near-optimal scalability on up to 128 processes and results are presented in which the statistical framework is used to process large-scale turbulent combustion simulation data with 1500 processes.