High Performance Computing Platforms Research Articles

High-performance computing for SARS-CoV-2 RNAs clustering: a data science‒based genomics approach.

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.

GShare: A centralized GPU memory management framework to enable GPU memory sharing for containers

Owing to low overhead and rapid deployment, containers are increasingly becoming an attractive system software platform for deep learning and high performance computing (HPC) applications that leverage GPUs. Unfortunately, existing container software does not concern how each container allocates GPU memory. Therefore, if a certain container consumes the majority of GPU memory, other containers may not run their workloads because of insufficient memory. This paper presents gShare, a centralized GPU memory management framework to enable GPU memory sharing for containers. As with a modern operating system, gShare allocates the entire GPU memory inside the framework and manages the memory with sophisticated memory allocators. gShare is then able to enforce the GPU memory limit of each container by mediating the memory allocation calls. To achieve its objective, gShare introduces the API remoting components, the mediator, and the three-level memory allocator, which enable lightweight and efficient GPU memory management. Our prototype implementation achieves near-native performance with secure isolation and little memory waste in popular deep learning and HPC workloads.

FSEI-GPU: GPU accelerated simulations of the fluid–structure–electrophysiology interaction in the left heart

The reliability of cardiovascular computational models depends on the accurate solution of the hemodynamics, the realistic characterization of the hyperelastic and electric properties of the tissues along with the correct description of their interaction. The resulting fluid–structure–electrophysiology interaction (FSEI) thus requires an immense computational power, usually available in large supercomputing centers, and requires long time to obtain results even if multi–CPU processors are used (MPI acceleration). In recent years, graphics processing units (GPUs) have emerged as a convenient platform for high performance computing, as they allow for considerable reductions of the time–to–solution.This approach is particularly appealing if the tool has to support medical decisions that require solutions within reduced times and possibly obtained by local computational resources. Accordingly, our multi–physics solver [1] has been ported to GPU architectures using CUDA Fortran to tackle fast and accurate hemodynamics simulations of the human heart without resorting to large–scale supercomputers. This work describes the use of CUDA to accelerate the FSEI on heterogeneous clusters, where both the CPUs and GPUs are used in synergistically with minor modifications of the original source code. The resulting GPU accelerated code solves a single heartbeat within a few hours (from three to ten depending on the grid resolution) running on premises computing facility made of few GPU cards, which can be easily installed in a medical laboratory or in a hospital, thus opening towards a systematic computational fluid dynamics (CFD) aided diagnostic.

It’s Time to Talk About HPC Storage: Perspectives on the Past and Future

High-performance computing (HPC) storage systems area key component of the success of HPC to date. Recently, we have seen major developments in storage-related technologies, as well as changes to how HPC platforms are used, especially in relation to artificial intelligence and experimental data analysis workloads. These developments merit a revisit of HPC storage system architectural designs. In this article, we discuss the drivers, identify key challenges to status quo posed by these developments, and discuss directions future research might take to unlock the potential of new technologies for the breadth of HPC applications.

Energy-efficient algebra kernels in FPGA for High Performance Computing

The dissemination of multi-core architectures and the later irruption of massively parallel devices, led to a revolution in High-Performance Computing (HPC) platforms in the last decades. As a result, Field-Programmable Gate Arrays (FPGAs) are re-emerging as a versatile and more energy-efficient alternative to other platforms. Traditional FPGA design implies using low-level Hardware Description Languages (HDL) such as VHDL or Verilog, which follow an entirely different programming model than standard software languages, and their use requires specialized knowledge of the underlying hardware. In the last years, manufacturers started to make big efforts to provide High-Level Synthesis (HLS) tools, in order to allow a grater adoption of FPGAs in the HPC community.Our work studies the use of multi-core hardware and different FPGAs to address Numerical Linear Algebra (NLA) kernels such as the general matrix multiplication GEMM and the sparse matrix-vector multiplication SpMV. Specifically, we compare the behavior of fine-tuned kernels in a multi-core CPU processor and HLS implementations on FPGAs. We perform the experimental evaluation of our implementations on a low-end and a cutting-edge FPGA platform, in terms of runtime and energy consumption, and compare the results against the Intel MKL library in CPU.

Fifteen quick tips for success with HPC, i.e., responsibly BASHing that Linux cluster.

High-performance computing (HPC) clusters are widely used in-house at scientific and academic research institutions. For some users, the transition from running their analyses on a single workstation to running them on a complex, multi-tenanted cluster, usually employing some degree of parallelism, can be challenging, if not bewildering, especially for users whose role is not predominantly computational in nature. On the other hand, there are more experienced users, who can benefit from pointers on how to get the best from their use of HPC. This Ten Simple Rules guide is aimed at helping you identify ways to improve your utilisation of HPC, avoiding common pitfalls that can negatively impact other users and will also help ease the load (pun intended) on your HPC sysadmin. It is intended to provide technical advice common to the use of HPC platforms such as LSF, Slurm, PBS/Torque, SGE, LoadLeveler and YARN, the scheduler used with Hadoop/Spark platform.

Extension of the NEAMS workbench to parallel sensitivity and uncertainty analysis of thermal hydraulic parameters using Dakota and Nek5000

With the increasing availability of high-performance computing (HPC) platforms, uncertainty quantification (UQ) and sensitivity analyses (SA) can be efficiently leveraged to optimize design parameters of complex engineering problems using modeling and simulation tools. The workflow involved in such studies heavily relies on HPC resources and hence requires pre-processing and post-processing capabilities of large amounts of data along with remote submission capabilities. The NEAMS Workbench addresses all aspects of the workflows involved in these studies by relying on a user-friendly graphical user interface and a python application program interface.This paper highlights the NEAMS Workbench capabilities by presenting a semiautomated coupling scheme between Dakota and any given package integrated with the NEAMS Workbench, yielding a simplified workflow for users. This new capability is demonstrated by running a SA of a turbulent flow in a pipe using the open-source Nek5000 CFD code. A total of 54 jobs were run on a HPC platform using the remote capabilities of the NEAMS Workbench. The results demonstrate that the semiautomated coupling scheme involving Dakota can be efficiently used for UQ and SA while keeping scripting tasks to a minimum for users. All input and output files used in this work are available in https://code.ornl.gov/neams-workbench/dakota-nek5000-study.

High Performance Computing in Parallel Electromagnetics Simulation Code suite ACE3P

A comprehensive set of parallel finite-element codes suite ACE3P (Advanced Computational Electromagnetics 3D Parallel) is developed by SLAC for multi-physics modeling of particle accelerators running on massively parallel computer platforms for high fidelity and high accuracy simulation. ACE3P enables rapid virtual prototyping of accelerator and RF component design, optimization and analysis. Advanced modeling capabilities have been facilitated by implementations of novel algorithms for numerical solvers. Code performance on state-of-the-art high performance computing (HPC) platforms for large-scale RF modeling in accelerator applications will be presented in this paper. All the simulations have been performed on the supercomputers at National Energy Research Computer Center (NERSC).

Dynamic Workflow Engine of Atmospheric Big Remote Sensing Data Processing Powered by Heterogenous Platform for High Performance Computing

The development of big remote sensing data related technologies and applications poses a big challenge that massive computing capability is needed to support big data processing. In order to solve this challenge, this paper proposes an architecture of heterogeneous platform of high performance computing, which employs the computer hardware resources to improve the efficiency of big remote sensing data processing by optimizing scheduling strategies and designing high-performance algorithms. Furthermore, the proposed platform can dynamically incorporated with a workflow engine regarding big remote sensing data processing. These algorithms are modular to meet the flexible combination of different processes.

Resilient Scheduling Heuristics for Rigid Parallel Jobs

This paper focuses on the resilient scheduling of parallel jobs on high-performance computing (HPC) platforms to minimize the overall completion time, or the makespan. We revisit the classical problem while assuming that jobs are subject to failures caused by transient or silent errors, and hence may need to be re-executed each time they fail to complete successfully. This work generalizes the classical framework where jobs are known offline and do not fail: in this framework, list scheduling that gives priority to the longest jobs is known to be a 3-approximation when imposing to use shelves, and a 2-approximation without this restriction. We show that when jobs can fail, using shelves can be arbitrarily bad, but unrestricted list scheduling remains a 2-approximation. The paper focuses on the design of several heuristics, some list-based and some shelf-based, along with different priority rules and backfilling strategies. We assess and compare their performance through an extensive set of simulations using both synthetic jobs and log traces from the Mira supercomputer.

A Proposed Data Partitioning Approach on Heterogeneous HPC Platforms: Data Locality Perspective

We propose a new data partitioning approach to improve the performance of heterogeneous parallel applications in modern high-performance computing (HPC) systems. Existing approaches do not consider an important aspect that has a critical impact on the performance of parallel applications: the method of assigning partitions to each processor so as to minimize the communication cost and hence minimize data movement, which dominates energy and performance cost. Such an aspect for managing data locality is important for a large range of applications. Therefore, to achieve efficient data partitioning, we propose a method for distribution considering this aspect. Our algorithm seeks to minimize execution time by using two models. The first is a fine-grained computational model of heterogeneous processors, which is sufficiently adequate and accurate to guarantee efficient partitioning results that maximize utilization. The second is a communication model of heterogeneous processors to minimize data motion and hide communication overheads. The correctness of our algorithm was analyzed and validated. The complexity of our algorithm is approximately of order <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> ( <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> × <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">log</b> <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> + <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> × <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>2</b></sup> ), where s is problem size/steps (where steps is the step size between data points in the computational model of each processor), and p is the number of heterogeneous processors. The experiments were performed on AZIZ supercomputer using two types of applications: an application with no dependency between its partitions, i.e., matrix multiplication, and another one with high dependency between its partitions, i.e., the Jacobi method. The results show the efficiency of our algorithm in improving performance.

Rare-Earth Metal Boroxide with Formal Triple Metal–Oxygen Orbital Interaction: Synthesis from B(C 6 F 5 ) 3 ·H 2 O and Radical-Anion Ligated Rare-Earth Metal Amides

Rare-earth metal boroxides are not only rare and of great synthetic challenge but also important theoretically due to their unrevealed bonding characteristics. In this paper, we report the synthesi...

Super-strong interactions between multivalent anions and graphene* *Project supported by the National Natural Science Foundation of China for Outstanding Young Scholars (Grant No. 11722548), the China Postdoctoral Science Foundation (Grant No. 2019M651462), the National Natural Science Foundation of China (Grant No. U1932123), the Innovative Research Team of High-Level Local Universities in Shanghai, the Open Project of State Key Laboratory of

Based on the density functional theory (DFT) calculations, we showed that the interactions between different valence anions (, , ) and graphene significantly increased as the valence of anion increased from negative monovalence to negative trivalence. The adsorption energy of on the electron-rich graphene flake (C84H24) is −8.3 kcal/mol. The adsorption energy of on the electron-rich graphene flake (C84H24) is –48.0 kcal/mol, which is about six times that of adsorption on electron-rich graphene flake (C84H24) and is even much larger than that of adsorption on electron-deficient aromatic ring C6F6 (–28.4 kcal/mol). The adsorption energy of on the electron-rich graphene flake (C84H24) is –159.2 kcal/mol, which is about 20 times that of adsorption on the graphene flake (C84H24). The super-strong adsorption energy is mainly attributed to the orbital interactions between multivalent anions and graphene. This work provides new insights for understanding the interaction between multivalent anions and π–electron-rich carbon-based nanomaterials and is helpful for the design of graphene-based DNA biosensor.

Many-Body Quantum Chemistry on Massively Parallel Computers.

The deployment of many-body quantum chemistry methods onto massively parallel high-performance computing (HPC) platforms is reviewed. The particular focus is on highly accurate methods that have become popular in predictive description of chemical phenomena, such as the coupled-cluster method. The account of relevant literature is preceded by a discussion of the modern and near-future HPC landscape and the relevant computational traits of the many-body methods, in their canonical and reduced-scaling formulations, that underlie the challenges in their HPC realization.

EQSIM—A multidisciplinary framework for fault-to-structure earthquake simulations on exascale computers part I: Computational models and workflow

Computational simulations have become central to the seismic analysis and design of major infrastructure over the past several decades. Most major structures are now “proof tested” virtually through representative simulations of earthquake-induced response. More recently, with the advancement of high-performance computing (HPC) platforms and the associated massively parallel computational ecosystems, simulation is beginning to play a role in increased understanding and prediction of ground motions for earthquake hazard assessments. However, the computational requirements for regional-scale geophysics-based ground motion simulations are extreme, which has restricted the frequency resolution of direct simulations and limited the ability to perform the large number of simulations required to numerically explore the problem parametric space. In this article, recent developments toward an integrated, multidisciplinary earth science-engineering computational framework for the regional-scale simulation of both ground motions and resulting structural response are described with a particular emphasis on advancing simulations to frequencies relevant to engineered systems. This multidisciplinary computational development is being carried out as part of the US Department of Energy (DOE) Exascale Computing Project with the goal of achieving a computational framework poised to exploit emerging DOE exaflop computer platforms scheduled for the 2022–2023 timeframe.

Calculation of Feynman loop integration and phase-space integration via auxiliary mass flow * *Supported in part by the National Natural Science Foundation of China (11875071, 11975029) and the High-performance Computing Platform of Peking University

We extend the auxiliary-mass-flow (AMF) method originally developed for Feynman loop integration to calculate integrals which also involve phase-space integration. The flow of the auxiliary mass from the boundary ( ) to the physical point ( ) is obtained by numerically solving differential equations with respective to the auxiliary mass. For problems with two or more kinematical invariants, the AMF method can be combined with the traditional differential-equation method, providing systematic boundary conditions and a highly nontrivial self-consistency check. The method is described in detail using a pedagogical example of at NNLO. We show that the AMF method can systematically and efficiently calculate integrals to high precision.

Extensible and Scalable Adaptive Sampling on Supercomputers.

The accurate sampling of protein dynamics is an ongoing challenge despite the utilization of high-performance computer (HPC) systems. Utilizing only "brute force" molecular dynamics (MD) simulations requires an unacceptably long time to solution. Adaptive sampling methods allow a more effective sampling of protein dynamics than standard MD simulations. Depending on the restarting strategy, the speed up can be more than 1 order of magnitude. One challenge limiting the utilization of adaptive sampling by domain experts is the relatively high complexity of efficiently running adaptive sampling on HPC systems. We discuss how the ExTASY framework can set up new adaptive sampling strategies and reliably execute resulting workflows at scale on HPC platforms. Here, the folding dynamics of four proteins are predicted with no a priori information.

Improved probabilistic I/O scheduling for limited-size Burst-Buffers deployed HPC

I/O bottleneck is a critical problem in current High Performance Computing (HPC) systems which hinges the performance scalability of a system. Some techniques, such as I/O scheduling and Burst-Buffering, had been proposed to accelerate data exchange between the compute and storage components on HPC platforms. Probabilistic I/O scheduling, a Markov-chain-based hybrid method combined the above-mentioned two techniques, controls the data transmission considering the whole load states of the Burst-Buffers system to mitigate the I/O congestion caused by unpredictable concurrent I/O bursts. However, this method requires a large amount of computation to make online scheduling, resulting in significant wastage of computing resources and decreased efficiency in scheduling. In this paper, we first introduce the architecture of Burst-Buffers deployed HPC platform, the probabilistic execution model of applications, and the basic probabilistic I/O scheduling method with a proof of its efficiency based on the Markov-chain framework. Then, we propose the modularization technique, as the first improvement, to reduce the repeated computation by isolating the heuristic application selection module from the original method and reusing the application ranking result to adjust the I/O scheduling. Next, we propose the thresholding technique, as the second improvement, to reduce the number of data transferring on burst-buffers by considering the write amplification characteristic of the underlying storage devices. Finally, we conduct extensive simulation experiments to show that our proposed I/O scheduling methods outperform the existing I/O scheduling methods without introducing burst-buffers states and without considering the characteristics of storage devices.

Benchmark on Indexing Algorithms for Accelerating Molecular Similarity Search.

Structurally similar analogues of given query compounds can be rapidly retrieved from chemical databases by the molecular similarity search approaches. However, the computational cost associated with the exhaustive similarity search of a large compound database will be quite high. Although the latest indexing algorithms can greatly speed up the search process, they cannot be readily applicable to molecular similarity search problems due to the lack of Tanimoto similarity metric implementation. In this paper, we first implement Python or C++ codes to enable the Tanimoto similarity search via several recent indexing algorithms, such as Hnsw and Onng. Moreover, there are increasing interests in computational communities to develop robust benchmarking systems to access the performance of various computational algorithms. Here, we provide a benchmark to evaluate the molecular similarity searching performance of these recent indexing algorithms. To avoid the potential package dependency issues, two separate benchmarks are built based on currently popular container technologies, Docker and Singularity. The Singularity container is a rather new container framework specifically designed for the high-performance computing (HPC) platform and does not need the privileged permissions or the separated daemon process. Both benchmarking methods are extensible to incorporate other new indexing algorithms, benchmarking data sets, and different customized parameter settings. Our results demonstrate that the graph-based methods, such as Hnsw and Onng, consistently achieve the best trade-off between searching effectiveness and searching efficiencies. The source code of the entire benchmark systems can be downloaded from https://github.uconn.edu/mldrugdiscovery/MssBenchmark.

Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure

Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry.