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he project PARADIGM (PARallel Approach of Differential and InteGral Measurements) answers this question by selecting via machine learning (ML) an optimal combination of differential and integral experiments to reduce 239 Pu nuclear data uncertainties from 1-600 keV by 50%..
The data acquisition system of the 80 by 120 foot wind tunnel of the National Full-Scale Aerodynamic Facility (NFAC) is described. How the various satellite data stations are connected to the data acquisition system is shown. As an illustrative example, a strain gage signal is traced from one of the satellite data locations to its final destination in the data system where the signal is processed, observed in real time on various parallel graphic displays, and stored on magnetic disks for postrun data reduction.
On the path to exascale the landscape of computer device architectures and corresponding programming models has become much more diverse. While various low-level performance portable programming models are available, support at the application level lacks behind. To address this issue, we present the performance portable block-structured adaptive mesh refinement (AMR) framework Parthenon, derived from the well-tested and widely used Athena++ astrophysical magnetohydrodynamics code, but generalized to serve as the foundation for a variety of downstream multi-physics codes. Parthenon adopts the Kokkos programming model, and provides various levels of abstractions from multidimensional variables, to packages defining and separating components, to launching of parallel compute kernels. Parthenon allocates all data in device memory to reduce data movement, supports the logical packing of variables and mesh blocks to reduce kernel launch overhead, and employs one-sided, asynchronous MPI calls to reduce communication overhead in multi-node simulations. Using a hydrodynamics miniapp, we demonstrate weak and strong scaling on various architectures including AMD and NVIDIA GPUs, Intel and AMD x86 CPUs, IBM Power9 CPUs, as well as Fujitsu A64FX CPUs. At the largest scale on Frontier (the first TOP500 exascale machine), the miniapp reaches a total of 1.7 × 10 13 zone-cycles/s on 9216 nodes (73,728 logical GPUs) at [Formula: see text] weak scaling parallel efficiency (starting from a single node). In combination with being an open, collaborative project, this makes Parthenon an ideal framework to target exascale simulations in which the downstream developers can focus on their specific application rather than on the complexity of handling massively-parallel, device-accelerated AMR.
Triangle counting is a fundamental building block in graph algorithms. In this article, we propose a block-based triangle counting algorithm to reduce data movement during both sequential and parallel execution. Our block-based formulation makes the algorithm naturally suitable for heterogeneous architectures. The problem of partitioning the adjacency matrix of a graph is well-studied. Our task decomposition goes one step further: it partitions the set of triangles in the graph. By streaming these small tasks to compute resources, we can solve problems that do not fit on a device. We demonstrate the effectiveness of our approach by providing an implementation on a compute node with multiple sockets, cores and GPUs. The current state-of-the-art in triangle enumeration processes the Friendster graph in 2.1 seconds, not including data copy time between CPU and GPU. Using that metric, our approach is 20 percent faster. When copy times are included, our algorithm takes 3.2 seconds. This is 5.6 times faster than the fastest published CPU-only time.
Triangle counting is a fundamental building block in graph algorithms. In this paper, we propose a block-based triangle counting algorithm to reduce data movement during both sequential and parallel execution. Our block-based formulation makes the algorithm naturally suitable for heterogeneous architectures. The problem of partitioning the adjacency matrix of a graph is well-studied. Our task decomposition goes one step further: it partitions the set of triangles in the graph. By streaming these small tasks to compute resources, we can solve problems that do not fit on a device. We demonstrate the effectiveness of our approach by providing an implementation on a compute node with multiple sockets, cores and GPUs. The current state-of-the-art in triangle enumeration processes the Friendster graph in 2.1 seconds, not including data copy time between CPU and GPU. Using that metric, our approach is 20 percent faster. When copy times are included, our algorithm takes 3.2 seconds. This is 5.6 times faster than the fastest published CPU-only time.
Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.
Pairwise sequence alignment is one of the most compute-intense challenges for the analysis of genomics data and it can account for more than 90% of the runtime depending on the application. Despite the effort to reduce the computational cost introducing heuristics such as banded and x-drop alignment, the performance of state-of-the-art software is often unsatisfactory for long sequences. Recent advances in single-molecule sequencing technologies keep increasing the length of the sequences to align at the cost of higher error rates b ranging from 5% to 15% b, emphasizing the need for fast and accurate software. Our long-read alignment software, named Xavier, computes high-performance x-drop adaptive banded pairwise alignment exploiting instruction-level parallelism via Single-Instruction-Multiple-Data (SIMD) operations. Xavier uses a narrow bandwidth that appreciably improves performance reducing the search space for the optimal alignment. In our experiment, the adaptive mechanism enables Xavier to find the optimal alignment between related sequences even when the error rate is as high as 50%. Xavier achieves speed-up up to 5X over competitor software when enabling the x-drop termination and up to 415X when disabling it, with negligible accuracy loss.
The design of distributed shared memory (DSM) computers liberates users from the duty to distribute data across processors and allows for the incremental development of parallel programs using, for example, OpenMP or Java threads. DSM architecture greatly simplifies the development of parallel programs having good performance on a few processors. However, to achieve a good program scalability on DSM computers requires that the user understand data flow in the application and use various techniques to avoid data traffic congestions. In this paper we discuss a number of such techniques, including data blocking, data placement, data transposition and page size control and evaluate their efficiency on the NAS (NASA Advanced Supercomputing) Parallel Benchmarks. We also present a tool which automates the detection of constructs causing data congestions in Fortran array oriented codes and advises the user on code transformations for improving data traffic in the application.
The OECD/NEA Radioactive Waste Repository Metadata Management (RepMet) initiative aims to bring about a better understanding of the identification and administration of metadata - a key aspect of data management - to support national programmes in managing their radioactive waste repository data, information and records in a way that is both harmonised internationally and suitable for long-term management and use. At this point in the project, the main focus lies on 'waste packages at their storage sites ready for geological disposal' but excluding spent fuel and high level waste (HLW). Later work will deal with metadata collected on spent fuel and HLW as well as with different phases of repository development: design and construction, and operation. In order to develop metadata, RepMet members have provided lists of data items they currently collect and manage for packaged waste. The lists, albeit it non comprehensive, suggest that the number and type of data items varies importantly from organisation to organisation. At term, RepMet plans to generate a comprehensive list of such data items and their metadata. In parallel, RepMet is developing a document which examines a selected number of existing metadata standards. Because of the specialist nature of radioactive waste management, including waste processing facilities, disposal facilities and waste packages, existing metadata standards do not seem to be transferable as defined. Rather, RepMet will explore such standards to identify relevant principles for possible adaptation to the radioactive waste management community. (authors)
In this paper, we present a first-principles lattice-QCD calculation of the unpolarized quark PDF for the pion and the kaon. The lattice data rely on matrix elements calculated for boosted mesons coupled to non-local operators containing a Wilson line. The calculations on this lattice ensemble correspond to two degenerate light, a strange, and a charm quark (𝑁 𝑓 = 2 + 1 + 1), using maximally twisted mass fermions with a clover term. The lattice volume is 32 3 × 64, with a lattice spacing of 0.0934 fm, and a pion mass of 260 MeV. Matrix elements are calculated for hadron boosts of |𝑃 3 | = 0, 0.41, 0.83, 1.25, 1.66, and 2.07 GeV. To match lattice QCD results to their light-cone counterparts, we employ two complementary frameworks: the large-momentum effective theory (LaMET) and the short-distance factorization (SDF). Using these approaches in parallel, we also test the lattice data to identify methodology-driven systematics. Results are presented for the standard quark PDFs, as well as the valence sector. Beyond obtaining the PDFs, we also explore the possibility of extracting information on SU(3) flavor-symmetry-breaking effects. For LaMET, we also parametrize the momentum dependence to obtain the infinite-momentum PDFs. Since the present calculation is performed on a single ensemble at a pion mass of 260 MeV and fixed lattice spacing, the uncertainties reported are statistical only, and systematic uncertainties remain to be addressed in future multi-ensemble studies.
Data from the XGC1 gyrokinetic simulation is analyzed to understand the three-dimensional spatial structure and the radial propagation of blob-filaments generated by quasisteady turbulence in the tokamak edge pedestal and scrape-off layer plasma. Spontaneous toroidal flows vary in the poloidal direction and shear the filaments within a flux surface resulting in a structure that varies in the parallel direction. Here, this parallel structure allows the curvature and grad-B induced polarization charge density to be shorted out via parallel electron motion. As a result, it is found that the blob-filament radial velocity is significantly reduced from estimates which neglect parallel electron kinetics, broadly consistent with experimental observations. Conditions for when this charge shorting effect tends to dominate blob dynamics are derived and compared with the simulation.
A recently initiated research project concerned with the concurrent detection of software errors and errors due to physical failures in the hardware of multiprocessor systems is described in this paper. An approach to error detection is described, which is specification based and relies on the structural verification of program control flow and data structure integrity. The techniques discussed utilize the hardware redundancy inherent in parallel processing systems to provide verification of both program structure and data concurrently with program execution.
Profiles of retrieved satellite observed temperatures provided to the Israel Meteorological Service (IMS) by the National Environmental Satellite Data and Information Service were used to enhance the specification of atmospheric conditions during three winter periods. Introduction of the satellite data was via three alternative schemes, and the enhanced specifications were used to initialize 48-hour numerical forecasts with the IMS five-layer primitive equation model; control forecasts were run in parallel from initial conditions without the satellite data. The study selects one forecast from each period for which discernible impacts on forecast synoptic fields can be attributed to the different initializations. The first two case studies examine the results of only one assimilation scheme and the third relates to all three schemes. Verification of the prognostics show the positive impact of including satellite temperatures in the initialization, but there were also examples of negative impact.
A collection of functions for statistical computing and data manipulation. These include routines for rapidly aggregating heterogeneous matrices, manipulating file names, loading R objects, sourcing multiple R files, formatting datetimes, multi-core parallel computing, stream editing, specialized plotting, etc. Smisc-package A collection of miscellaneous functions allMissing Identifies missing rows or columns in a data frame or matrix as.numericSilent Silent wrapper for coercing a vector to numeric comboList Produces all possible combinations of a set of linear model predictors cumMax Computes the maximum of the vector up to the current index cumsumNA Computes the cummulative sum of a vector without propogating NAs d2binom Probability functions for the sum of two independent binomials dataIn A flexible way to import data into R. dbb The Beta-Binomial Distribution df2list Row-wise conversion of a data frame to a list dfplapply Parallelized single row processing of a data frame dframeEquiv Examines the equivalence of two dataframes or matrices dkbinom Probability functions for the sum of k independent binomials factor2character Converts all factor variables in a dataframe to character variables findDepMat Identify linearly dependent rows or columns in a matrix formatDT Converts date or datetime strings into alternate formats getExtension Filename manipulations: remove the extension or path, extract the extension or path getPath Filename manipulations: remove the extension or path, extract the extension or path grabLast Filename manipulations: remove the extension or path, extract the extension or path ifelse1 Non-vectorized version of ifelse integ Simple numerical integration routine interactionPlot Two-way Interaction Plot with Error Bar linearMap Linear mapping of a numerical vector or scalar list2df Convert a list to a data frame loadObject Loads and returns the object(s) in an ".Rdata" file more Display the contents of a file to the R terminal movAvg2 Calculate the moving average using a 2-sided window openDevice Opens a graphics device based on the filename extension p2binom Probability functions for the sum of two independent binomials padZero Pad a vector of numbers with zeros parseJob Parses a collection of elements into (almost) equal sized groups pbb The Beta-Binomial Distribution pcbinom A continuous version of the binomial cdf pkbinom Probability functions for the sum of k independent binomials plapply Simple parallelization of lapply plotFun Plot one or more functions on a single plot PowerData An example of power data pvar Prints the name and value of one or more objects qbb The Beta-Binomial Distribution rbb And numerous others (space limits reporting).
A collection of functions for statistical computing and data manipulation. These include routines for rapidly aggregating heterogeneous matrices, manipulating file names, loading R objects, sourcing multiple R files, formatting datetimes, multi-core parallel computing, stream editing, specialized plotting, etc. Smisc-package A collection of miscellaneous functions allMissing Identifies missing rows or columns in a data frame or matrix as.numericSilent Silent wrapper for coercing a vector to numeric comboList Produces all possible combinations of a set of linear model predictors cumMax Computes the maximum of the vector up to the current index cumsumNA Computes the cummulative sum of a vector without propogating NAs d2binom Probability functions for the sum of two independent binomials dataIn A flexible way to import data into R. dbb The Beta-Binomial Distribution df2list Row-wise conversion of a data frame to a list dfplapply Parallelized single row processing of a data frame dframeEquiv Examines the equivalence of two dataframes or matrices dkbinom Probability functions for the sum of k independent binomials factor2character Converts all factor variables in a dataframe to character variables findDepMat Identify linearly dependent rows or columns in a matrix formatDT Converts date or datetime strings into alternate formats getExtension Filename manipulations: remove the extension or path, extract the extension or path getPath Filename manipulations: remove the extension or path, extract the extension or path grabLast Filename manipulations: remove the extension or path, extract the extension or path ifelse1 Non-vectorized version of ifelse integ Simple numerical integration routine interactionPlot Two-way Interaction Plot with Error Bar linearMap Linear mapping of a numerical vector or scalar list2df Convert a list to a data frame loadObject Loads and returns the object(s) in an ".Rdata" file more Display the contents of a file to the R terminal movAvg2 Calculate the moving average using a 2-sided window openDevice Opens a graphics device based on the filename extension p2binom Probability functions for the sum of two independent binomials padZero Pad a vector of numbers with zeros parseJob Parses a collection of elements into (almost) equal sized groups pbb The Beta-Binomial Distribution pcbinom A continuous version of the binomial cdf pkbinom Probability functions for the sum of k independent binomials plapply Simple parallelization of lapply plotFun Plot one or more functions on a single plot PowerData An example of power data pvar Prints the name and value of one or more objects qbb The Beta-Binomial Distribution rbb And numerous others (space limits reporting).
A major goal of our participation in the Edge Physics Simulation project has been to contribute to the understanding of the self-organization of tokamak turbulence fluctuations resulting in the formation of a staircase structure in the ion temperature. A second important goal is to demonstrate how small scale turbulence in plasmas self-organizes with dynamically driven quasi-stationary flow shear. These goals have been accomplished through the analyses of the statistical properties of XGC1 flux driven Gyrokinetic electrostatic ion temperature gradient (ITG) turbulence simulation data in which neutrals are included. The ITG turbulence data, and in particular fluctuation data, were obtained from a massively parallel flux-driven gyrokinetic full-f particle-in-cell simulation of a DIII-D like equilibrium. Below some the findings are summarized. It was observed that the emergence of staircase structure is related to the variations in the normalized temperature gradient length (R/LT) and the poloidal flow shear. Average turbulence intensity is found to be large in the vicinity of minima in R/LTi, where ITG growth is expected to be lower. The distributions of the occurrences of potential fluctuation are found to be Gaussian away from the staircase-step locations, but they are found to be non-Gaussian in the vicinity of staircase-step locations. The results of analytically derived expressions for the distribution of the occurrences of turbulence intensity and intensity flux were compared with the corresponding quantities computed using XGC1 simulation data and good agreement is found. The derived expressions predicts inward and outward propagation of turbulence intensity flux in an intermittent fashion. The outward propagation of turbulence intensity flux occurs at staircase-step locations and is related to the change in poloidal flow velocity shear and to the change in the ion temperature gradient. The standard deviation, skewness and kurtosis for turbulence quantities were computed and found to be large in the vicinity of the staircase-step structures. Large values of skewness and kurtosis can be explained by a temporary opening and closing of the structure which allows turbulence intensity events to propagate. The staircase patterns may reduce the ion heat transport and a manipulation of these patterns may be used to optimize heat transport in tokamaks. An additional objective of the research in support of the Edge Physics Simulation initiative has been to improve the understanding of scrape-off layer thermal transport. In planning experiments and designing future tokamaks, it is important to understand the physical effects that contribute to divertor heat-load fluxes. The research accomplished will contribute to developing new models for the scrape-off layer region. The XGC0 code was used to compute the heat fluxes and the heat-load width in the outer divertor plates of C-Mod and DIII-D tokamaks. It was observed that the width of the XGC0 neoclassical heat-load was approximately inversely proportional to the total plasma current. Anomalous transport in the H-mode pedestal region of five Alcator C-Mod discharges, representing a collisionality scan, was analyzed. The understanding of anomalous transport in the pedestal region is important for the development of a comprehensive model for the H-mode pedestal slope. It was found that the electron thermal anomalous diffusivities at the pedestal top increase with the electron collisionality. This dependence can point to the DRIBM as the modes that drive the anomalous transport in the plasma edge of highly collisional discharges. The effects of plasma shaping on the H-mode pedestal structure was also investigated. The differences in the predicted H-mode pedestal width and height for the DIII-D discharges with different elongation and triangularities were discussed. For the discharges with higher elongation, it was found that the gradients of the plasma profiles in the H-mode pedestal reach semi-steady states. In these simulations, the pedestal slowly continued to evolve to higher pedestal pressures and bootstrap currents until the peeling ballooning stability conditions were satisfied. The discharges with lower elongation do not reach the semi-steady state, and ELM crashes were triggered at earlier times. The plasma elongation was found to have a stronger stabilizing effect than the plasma triangularity. For the discharges with lower elongation and lower triangularity, the ELM frequency was large, and the H-mode pedestal evolves rapidly. It was found that the temperature of neutrals in the scrape-off-layer region can affect the dynamics of the H-mode pedestal buildup. However, the final pedestal profiles were nearly independent of the neutral temperature. The elongation and triangularity affected the pedestal widths of plasma density and electron temperature profiles differently. This study illustrated a new mechanism for controlling the pedestal bootstrap current and the pedestal stability.
In this paper, we describe an array-based hierarchical mesh refinement capability through uniform refinement of unstructured meshes for efficient solution of PDE's using finite element methods and multigrid solvers. A multi-degree, multi-dimensional and multi-level framework is designed to generate the nested hierarchies from an initial coarse mesh that can be used for a variety of purposes such as in multigrid solvers/preconditioners, to do solution convergence and verification studies and to improve overall parallel efficiency by decreasing I/O bandwidth requirements (by loading smaller meshes and in memory refinement). We also describe a high-order boundary reconstruction capability that can be used to project the new points after refinement using high-order approximations instead of linear projection in order to minimize and provide more control on geometrical errors introduced by curved boundaries.The capability is developed under the parallel unstructured mesh framework "Mesh Oriented dAtaBase" (MOAB Tautges et al. (2004)). We describe the underlying data structures and algorithms to generate such hierarchies in parallel and present numerical results for computational efficiency and effect on mesh quality. Furthermore, we also present results to demonstrate the applicability of the developed capability to study convergence properties of different point projection schemes for various mesh hierarchies and to a multigrid finite-element solver for elliptic problems.
As applications continue to generate multi-dimensional data at exponentially increasing rates, fast analytics to extract meaningful results is becoming extremely important. The database community has developed array databases that alleviate this problem through a series of techniques. In-situ mechanisms provide direct access to raw data in the original format---without loading and partitioning. Parallel processing scales to the largest datasets. In-memory caching reduces latency when the same data are accessed across a workload of queries. However, we are not aware of any work on distributed caching of multi-dimensional raw arrays. In this paper, we introduce a distributed framework for cost-based caching of multi-dimensional arrays in native format. Given a set of files that contain portions of an array and an online query workload, the framework computes an effective caching plan in two stages. First, the plan identifies the cells to be cached locally from each of the input files by continuously refining an evolving R-tree index. In the second stage, an optimal assignment of cells to nodes that collocates dependent cells in order to minimize the overall data transfer is determined. We design cache eviction and placement heuristic algorithms that consider the historical query workload. A thorough experimental evaluation over two real datasets in three file formats confirms the superiority -- by as much as two orders of magnitude -- of the proposed framework over existing techniques in terms of cache overhead and workload execution time.