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NASA NTRS2006

Implementing Scientific Simulation Codes Highly Tailored for Vector Architectures Using Custom Configurable Computing Machines

The motivation for this work comes from an observation that amidst the push for Massively Parallel (MP) solutions to high-end computing problems such as numerical physical simulations, large amounts of legacy code exist that are highly optimized for vector supercomputers. Because re-hosting legacy code often requires a complete re-write of the original code, which can be a very long and expensive effort, this work examines the potential to exploit reconfigurable computing machines in place of a vector supercomputer to implement an essentially unmodified legacy source code. Custom and reconfigurable computing resources could be used to emulate an original application's target platform to the extent required to achieve high performance. To arrive at an architecture that delivers the desired performance subject to limited resources involves solving a multi-variable optimization problem with constraints. Prior research in the area of reconfigurable computing has demonstrated that designing an optimum hardware implementation of a given application under hardware resource constraints is an NP-complete problem. The premise of the approach is that the general issue of applying reconfigurable computing resources to the implementation of an application, maximizing the performance of the computation subject to physical resource constraints, can be made a tractable problem by assuming a computational paradigm, such as vector processing. This research contributes a formulation of the problem and a methodology to design a reconfigurable vector processing implementation of a given application that satisfies a performance metric. A generic, parametric, architectural framework for vector processing implemented in reconfigurable logic is developed as a target for a scheduling/mapping algorithm that maps an input computation to a given instance of the architecture. This algorithm is integrated with an optimization framework to arrive at a specification of the architecture parameters that attempts to minimize execution time, while staying within resource constraints. The flexibility of using a custom reconfigurable implementation is exploited in a unique manner to leverage the lessons learned in vector supercomputer development. The vector processing framework is tailored to the application, with variable parameters that are fixed in traditional vector processing. Benchmark data that demonstrates the functionality and utility of the approach is presented. The benchmark data includes an identified bottleneck in a real case study example vector code, the NASA Langley Terminal Area Simulation System (TASS) application.

Rutishauser, David↗

DOE OSTI2018

SIRIUS: Enabling Progressive Data Exploration for Extreme-Scale Scientific Data

Scientific simulations on high performance computing (HPC) platforms generate large quantities of data. To bridge the widening gap between compute and I/O, and enable data to be more efficiently stored and analyzed, simulation outputs need to be refactored, reduced, and appropriately mapped to storage tiers. However, a systematic solution to support these steps has been lacking in the current HPC software ecosystem. To that end, this paper develops SIRIUS, a progressive JPEG-like data management scheme for storing and analyzing big scientific data. It co-designs data decimation, compression, and data storage, taking the hardware characteristics of each storage tier into considerations. With reasonably low overhead, our approach refactors simulation data, using either topological or uniform decimation, into a much smaller, reduced-accuracy base dataset, and a series of deltas that is used to augment the accuracy if needed. The base dataset and deltas are compressed and written to multiple storage tiers. Data saved on different tiers can then be selectively retrieved to restore the level of accuracy that satisfies data analytics. Thus, SIRIUS provides a paradigm shift towards elastic data analytics and enables end users to make trade-offs between analysis speed and accuracy on-the-fly. This paper further develops algorithms to preserve statistics for data decimation, a common requirement for reducing data. Here, we assess the impact of SIRIUS on unstructured triangular meshes, a pervasive data model used in scientific simulations. In particular, we evaluate two realistic use cases: the blob detection in fusion and high-pressure area extraction in computational fluid dynamics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

A Benchmark Suite for Evaluating Scientific AI Workloads on GPUs

AI applications have been steadily increasing in the allocation portfolio among leadership computing facilities. These applications depend on deep learning frameworks with hardware acceleration and underlying software systems. With the rapid development of applications, software stacks, and hardware devices, it is essential to evaluate the performance of core operations in AI workloads for direction of optimizations and procurement of next-generation high-performance computing (HPC) infrastructures. Currently, most benchmarks lack scientific AI workloads. So, we present DeepKernelBench and the experimental results of evaluating the benchmark suite for early observations and performance comparisons on datacenter GPUs using representative workloads for scientific AI, including Attentions, General matrix multiplications, Geometrics and Fourier neural operations.

Jin, Zheming [Advanced Micro Devices (AMD)]↗

DOE OSTI2015

Towards Exascale Scientific Metadata Management

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Mixed-Precision S/DGEMM Using the TF32 and TF64 Frameworks on Low-Precision AI Tensor Cores

Using NVIDIA graphics processing units (GPUs) equipped with Tensor Cores has enabled the significant acceleration of general matrix multiplication (GEMM) for applications in machine learning (ML) and artificial intelligence (AI) and in high-performance computing (HPC) generally. The use of such power-efficient, specialized accelerators can provide a performance increase between 8 × and 20 ×, albeit with a loss in precision. However, a high level of precision is required in many large scientific and HPC applications, and computing in single or double precision is still necessary for many of these applications to maintain accuracy. Fortunately, mixed-precision methods can be employed to maintain a higher level of numerical precision while also taking advantage of the performance increases from computing with lower-precision AI cores. With this in mind, we extend the state of the art by using NVIDIA’s new TF32 framework. This new framework not only burdens some constraints of the previous frameworks, such as costly 32 16-bit castings but also provides an equivalent precision and performance by using a much simpler approach. We also propose a new framework called TF64 that attempts double-precision arithmetic with low-precision Tensor Cores. Although this framework does not exist yet, we validated the correctness of this idea and achieved an equivalent of 64-bit precision on 32-bit hardware.

Valero Lara, Pedro↗

DOE OSTI2019

Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP

With ever-increasing volumes of scientific data produced by high-performance computing applications, significantly reducing data size is critical because of limited capacity of storage space and potential bottlenecks on I/O or networks in writing/reading or transferring data. SZ and ZFP are two leading BSD licensed open source C/C++ libraries for compressed floating-point arrays that support high throughput read and write random access. However, their performance is not consistent across different data sets and across different fields of some data sets, which raises the need for an automatic online (during compression) selection between SZ and ZFP, with minimal overhead. In this paper, the automatic selection optimizes the rate-distortion, an important statistical quality metric based on the signal-to-noise ratio. To optimize for rate-distortion, we investigate the principles of SZ and ZFP. We then propose an efficient online, low-overhead selection algorithm that predicts the compression quality accurately for two compressors in early processing stages and selects the best-fit compressor for each data field. Here, we implement the selection algorithm into an open-source library, and we evaluate the effectiveness of our proposed solution against plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results on three data sets representing about 100 fields show that our selection algorithm improves the compression ratio up to 70 percent with the same level of data distortion because of very accurate selection (around 99 percent) of the bestfit compressor, with little overhead (less than 7 percent in the experiments).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we, in this work, propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

An Overview of the Computational Physics and Methods Group at Los Alamos National Laboratory

CCS Division was formed to strengthen the visibility and impact of computer science and computational physics research on strategic directions for the Laboratory. Both computer science and computational science are now central to scientific discovery and innovation. They have become indispensable tools for all other scientific missions at the Laboratory. CCS Division forms a bridge between external partners and Laboratory programs, bringing new ideas and technologies to bear on today’s important problems and attracting high-quality technical staff members to the Laboratory. The Computational Physics and Methods Group CCS-2 conducts methods research and develops scientific software aimed at the latest and emerging HPC systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Accelerating Relative-error Bounded Lossy Compression for HPC datasets with Precomputation-Based Mechanisms

Scientific simulations in high-performance computing (HPC) environments are producing vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for post-analysis. Unlike the traditional data reduction schemes (such as deduplication or lossless compression), not only can error-controlled lossy compression significantly reduce the data size but it can also hold the promise to satisfy user demand on error control. Point-wise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications in the lossy compression, since error control can adapt to the precision in the dataset automatically. Point-wise relative error bounded compression is complicated and time consuming. In this work, we develop efficient precomputation-based mechanisms in the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error bounded compression with excellent compression ratios. In addition, our mechanisms also help reduce traversing operations for Huffman decoding, and thus significantly accelerate the decompression process in SZ. Experiments with four well-known real-world scientific simulation datasets show that our solution can improve the compression rate by about 30% and decompression rate by about 70% in most of cases, making our designed lossy compression strategy the best choice in class in most cases.

compression rate↗

DOE OSTI2025

DUNE Software and Computing Research and Development

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The ambitious physics program of Phase I and Phase II of DUNE is dependent upon deployment and utilization of significant computing resources, and successful research and development of software (both infrastructure and algorithmic) in order to achieve these scientific goals. This submission discusses the computing resources projections, infrastructure support, and software development needed for DUNE during the coming decades as an input to the European Strategy for Particle Physics Update for 2026. The DUNE collaboration is submitting four main contributions to the 2026 Update of the European Strategy for Particle Physics process. This submission to the 'Computing' stream focuses on DUNE software and computing. Additional inputs related to the DUNE science program, DUNE detector technologies and R&D, and European contributions to Fermilab accelerator upgrades and facilities for the DUNE experiment, are also being submitted to other streams.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1990

Constructing an advanced software tool for planetary atmospheric modeling

Scientific model building can be an intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot be easily distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientist/programmer to understand. We believe that advanced software techniques can facilitate both the model building and model sharing process. In this paper, we describe a prototype for a scientific modeling software tool that serves as an aid to the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities. Our prototype has been developed in the domain of planetary atmospheric modeling, and is being used to construct models of Titan's atmosphere.

Keller, Richard M.↗

DOE OSTI2024

2D-EFICACY: Control of Metastable 2D Carbide[1]Chalcogenide Heterolayers: Strain and Moire Engineering

The experimental isolation of graphene led to the discovery of an entirely new world of two-dimensional (2D) materials in which the 2D nature often leads to emergent behaviors not seen in bulk systems. 2D transition metal dichalcogenides (TMDs) exhibit physico-chemical properties that depend on the transition metal, polymorph, thickness, and presence and type of defects. Recently, a group of thin (10-100nm) transition metal carbides (TMCs), such as Mo2C, has been synthesized that exhibit a thickness-dependent superconducting critical temperature (Tc). These thin TMCs are different from MXenes, another class of 2D materials consisting of few layers of nitrides or carbides (<5nm) produced by chemical etching and delamination. The goal of this renewal proposal is to combine experiment and computation to synthesize and elucidate the guiding principles that control the growth, orientation and strain of heterostacks of thin TMCs and TMDs composed with Nb, Ti and W. We expect to stabilize metastable hybrid phases of TMCs sandwiched between TMDs (H-TMD/Cs) with unprecedented physico-chemical properties. As part of previous DOE-funded work by the Terrones/Sinnott groups, thin (10-100 nm thick) Mo2C flakes were successfully synthesized by chemical vapor deposition (CVD). By subsequently exposing Mo2C to H2S, partial chalcogenization was achieved, resulting in heterostacks of MoCx phases and MoS2. The formation of MoS2 led to a deficiency of Mo atoms in the underlying Mo2C, resulting in an inhomogeneous phase change from α-Mo2C to γ’-MoCx and then to γ-MoC. The γ’-MoCx is a strained metastable phase and the heterostack of all three phases demonstrated an increased Tc relative to that of α-Mo2C, from 4 to 6K; its interleaved layered structure consisting of superconducting and semiconducting phases is ideal for future studies of Josephson junction series arrays. Moiré patterns in these heterostacked systems could result in new phenomena, as moiré patterns in bilayer graphene showed unconventional superconductivity and moiré excitons have been observed in twisted TMD heterobilayers. The scientific hypothesis of the proposed synergistic computational and experimental research is that orientation and strain control within confined thin metastable TMCs, sandwiched by stable phases of TMCs and layered TMDs, will depend on kinetic and thermodynamic “knobs” that include fast temperature changes, chalcogen diffusion through preferred crystallographic planes, reaction times, pressure, reactive atmosphere, precursors, and surfactants, which will also tailor properties such as superconductivity, magnetism, ferroelectricity, piezoelectricity, and catalytic performance. We will develop the guiding principles for the synthesis and stabilization of metastable H-TMD/Cs based on Nb, Ti and W. In order to validate the hypothesis, four tasks are proposed: The first task will synthesize ultra-thin TMCs based on Nb, W and Ti, by: 1) adapting the CVD method used for Mo2C, 2) plasma assisted CVD, 3) defect-mediated CVD processes, and 4) cryo-milling of carbide powders. The second task will accomplish the synthesis and basic physico-chemical characterizations of H-TMD/Cs by chalcogenization of the materials synthesized in task one, and by carbonization of TMDs. H-TMD/Cs will also be investigated for their suitability in energy conversion applications such as supercapacitors, Li and multivalent ion batteries, and electrocatalysts, topics of interest to DoE. These tasks will be carried out in close conjunction with density functional theory (DFT) calculations with insights into energetics, lattice parameters, stability, phase diagrams, band structures, and density of states of H-TMD/Cs. The third task will characterize and evaluate strain and moiré patterns at the interfaces of different H-TMD/Cs by high-resolution scanning transmission electron microscopy (HR-STEM), scanning tunneling microscopy (STM), and conductive tip atomic force microscopy. Nudged elastic band calculations with DFT will be performed to understand the chalcogen diffusion process, which will provide insights into the interfaces between different phases of TMCs and TMDs. The fourth task aims at quantifying the stability and dynamics of H-TMD/Cs by in-situ TEM and Raman studies under heating, strain, and electrical biasing. Phonon calculations using DFT will provide a basis for interpreting Raman spectra. This coherent framework involving synthesis, characterization, and computation will result in a broad scientific impact for energy related applications. The ability to develop new H-TMD/Cs will enhance a range of applications that include batteries, catalysts, switches, sensors, quantum computing components and smart coatings.

2-Dimensional materials↗

DOE OSTI2024

2D-EFICACY: Control of Metastable 2D Carbide Chalcogenide Heterolayers: Strain and Moire Engineering

The experimental isolation of graphene led to the discovery of an entirely new world of two-dimensional (2D) materials in which the 2D nature often leads to emergent behaviors not seen in bulk systems. 2D transition metal dichalcogenides (TMDs) exhibit physico-chemical properties that depend on the transition metal, polymorph, thickness, and presence and type of defects. Recently, a group of thin (10-100nm) transition metal carbides (TMCs), such as Mo 2 C, has been synthesized that exhibit a thickness-dependent superconducting critical temperature (Tc). These thin TMCs are different from MXenes, another class of 2D materials consisting of few layers of nitrides or carbides (<5nm) produced by chemical etching and delamination. The goal of this renewal proposal is to combine experiment and computation to synthesize and elucidate the guiding principles that control the growth, orientation and strain of heterostacks of thin TMCs and TMDs composed with Nb, Ti and W. We expect to stabilize metastable hybrid phases of TMCs sandwiched between TMDs (H-TMD/Cs) with unprecedented physico-chemical properties. As part of previous DOE-funded work by the Terrones/Sinnott groups, thin (10-100 nm thick) Mo 2 C flakes were successfully synthesized by chemical vapor deposition (CVD). By subsequently exposing Mo 2 C to H 2 S, partial chalcogenization was achieved, resulting in heterostacks of MoCx phases and MoS 2 . The formation of MoS 2 led to a deficiency of Mo atoms in the underlying Mo 2 C, resulting in an inhomogeneous phase change from α-Mo 2 C to γ’-MoCx and then to γ-MoC. The γ’-MoCx is a strained metastable phase and the heterostack of all three phases demonstrated an increased Tc relative to that of α-Mo 2 C, from 4 to 6K; its interleaved layered structure consisting of superconducting and semiconducting phases is ideal for future studies of Josephson junction series arrays. Moiré patterns in these heterostacked systems could result in new phenomena, as moiré patterns in bilayer graphene showed unconventional superconductivity and moiré excitons have been observed in twisted TMD heterobilayers. The scientific hypothesis of the proposed synergistic computational and experimental research is that orientation and strain control within confined thin metastable TMCs, sandwiched by stable phases of TMCs and layered TMDs, will depend on kinetic and thermodynamic “knobs” that include fast temperature changes, chalcogen diffusion through preferred crystallographic planes, reaction times, pressure, reactive atmosphere, precursors, and surfactants, which will also tailor properties such as superconductivity, magnetism, ferroelectricity, piezoelectricity, and catalytic performance. We will develop the guiding principles for the synthesis and stabilization of metastable H-TMD/Cs based on Nb, Ti and W. In order to validate the hypothesis, four tasks are proposed: The first task will synthesize ultra-thin TMCs based on Nb, W and Ti, by: 1) adapting the CVD method used for Mo 2 C, 2) plasma assisted CVD, 3) defect-mediated CVD processes, and 4) cryo-milling of carbide powders. The second task will accomplish the synthesis and basic physico-chemical characterizations of H-TMD/Cs by chalcogenization of the materials synthesized in task one, and by carbonization of TMDs. H-TMD/Cs will also be investigated for their suitability in energy conversion applications such as supercapacitors, Li and multivalent ion batteries, and electrocatalysts, topics of interest to DoE. These tasks will be carried out in close conjunction with density functional theory (DFT) calculations with insights into energetics, lattice parameters, stability, phase diagrams, band structures, and density of states of H-TMD/Cs. The third task will characterize and evaluate strain and moiré patterns at the interfaces of different H-TMD/Cs by high-resolution scanning transmission electron microscopy (HR-STEM), scanning tunneling microscopy (STM), and conductive tip atomic force microscopy. Nudged elastic band calculations with DFT will be performed to understand the chalcogen diffusion process, which will provide insights into the interfaces between different phases of TMCs and TMDs. The fourth task aims at quantifying the stability and dynamics of H-TMD/Cs by in-situ TEM and Raman studies under heating, strain, and electrical biasing. Phonon calculations using DFT will provide a basis for interpreting Raman spectra. This coherent framework involving synthesis, characterization, and computation will result in a broad scientific impact for energy related applications. The ability to develop new H-TMD/Cs will enhance a range of applications that include batteries, catalysts, switches, sensors, quantum computing components and smart coatings.

2-Dimensional Materials↗

DOE OSTI2016

Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science

The.LHC, operating at CERN, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, LHC data taking runs require more resources than grid can possibly provide. To alleviate these challenges, LHC experiments are engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, in particular with Titan supercomputer at Oak Ridge Leadership Computing Facility. Current approach utilizes modified PanDA pilot framework for job submission to the supercomputers batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on LCFs multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms for ALICE and ATLAS experiments and it is in full pro duction for the ATLAS since September 2015. We will present our current accomplishments with running PanDA at supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications, such as bioinformatics and astro-particle physics.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1996

Downsizing Visualization Platforms: From Crays to Indigos and Beyond

Despite its increasingly negative connotation in other domains, "downsizing" is a positive trend as it applies to the computer hardware necessary to perform scientific visualization. In this talk, we will consider how the visualization of a particular data set, the Digital Terrain Model (DTM) derived from the Viking Orbiter imagery, has been realized in three distinct projects over the past decade. These examples serve to demonstrate how the vast improvements in computational performance both decrease the cost of such visualization efforts, and permit an increasing level of interactivity. We then consider how even today's graphical systems require the visualization designer to make intelligent choices and trade-offs in database rendering. Finally, we discuss how insights from an understanding of human visual perception can guide these design decisions, and suggest new options for visualization hardware and software.

Kaiser, Mary K.↗

DOE OSTI2016

Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, LHC data taking runs require more resources than Grid computing can possibly provide. To alleviate these challenges, LHC experiments are engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, Europe and Russia (in particular with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF), MIRA supercomputer at Argonne Leadership Computing Facilities (ALCF), Supercomputer at the National Research Center Kurchatov Institute , IT4 in Ostrava and others). Current approach utilizes modified PanDA pilot framework for job submission to the supercomputers batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on LCFs multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms for ALICE and ATLAS experiments and it is in full production for the ATLAS experiment since September 2015. We will present our current accomplishments with running PanDA WMS at supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications, such as bioinformatics and astro-particle physics.

Data-intensive science↗

DOE OSTI2024

Performance on HPC Platforms Is Possible Without C++

Computing at large scales has become extremely challenging due to increasing heterogeneity in both hardware and software. More and more scientific workflows must tackle a range of scales and use machine learning and AI intertwined with more traditional numerical modeling methods, placing more demands on computational platforms. These constraints indicate a need to fundamentally rethink the way computational science is done and the tools that are needed to enable these complex workflows. The current set of C++-based solutions may not suffice, and relying exclusively upon C++ may not be the best option, especially because several newer languages and boutique solutions offer more robust design features to tackle the challenges of heterogeneity. In June 2023, we held a mini symposium that explored the use of newer languages and heterogeneity solutions that are not tied to C++ and that offer options beyond template metaprogramming and Parallel. For for performance and portability. In conclusion, we describe some of the presentations and discussion from the mini symposium in this article.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Special Topic on High Performance Computing in Chemical Physics

Computational modeling and simulation have become indispensable scientific tools in virtually all areas of chemical, biomolecular, and materials systems research. Computation can provide unique and detailed atomic level information that is difficult or impossible to obtain through analytical theories and experimental investigations. In addition, recent advances in micro-electronics have resulted in computer architectures with unprecedented computational capabilities, from the largest supercomputers to common desktop computers. In conclusion, combined with the development of new computational domain science methodologies and novel programming models and techniques, this has resulted in modeling and simulation resources capable of providing results at or better than experimental chemical accuracy and for systems in increasingly realistic chemical environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗