Single Processor Research Articles

CFD Code Parallelization on GPU and the Code Portability

AbstractGoal of this paper is to develop a fully functional parallel Computational Fluid Dynamics (CFD) code that is optimized to run on a single Graphics Processing Unit (GPU). This is achieved by writing the code in FORTRAN and OpenACC (Open Accelerators), providing them with an easily portable, platform independent code. Existing CFD code is significantly modified to allow for parallel asynchronous execution. Also, due to strong recursive dependencies in Tridiagonal Matrix Algorithm (TDMA) solver, it is replaced with Jacobi, which provides fast execution in environments with large number of parallel cores. In this research a computer code for simulation of 2D flow of water through the axisymmetric channel is used as a base for development. The parallel code is executed on GPU, single, and multicore Central Processing Unit (CPU), and the execution times are compared between platforms. Even though that Jacobi solver performs worse on single core computers, compared to its Gauss‐seidel counterpart, it is used to provide a baseline for comparison. In this work, it is shown that computation on finer grids takes less time on GPU than on CPU. The computation time increase with the number of cells in grid on GPU should follow the observed linear trend until the GPUs physical limitations are reached depending on memory size and core count.

Explainable and Reduced-Feature Machine learning models for shape and drag prediction of a freely moving drop in the sub-critical Weber number regime

Accurately predicting the shape and drag of a moving drop is crucial in many spray applications. However, due to the complex interaction between the drag force and drop shape deformation, accurate prediction of drop shape and drag from Computational Fluid Dynamics (CFD) simulation requires large computational resources and time. A novel data-driven approach using NARXNN (Non-linear Auto Regressive eXogenous input Neural Network) is proposed in this study, which recurrently predicts the drop shape and drag for a given Weber number (We) and Reynolds number (Re), in the sub-critical We regime where there is no drop breakup. The average error in radius and drag coefficient for the test cases, as low as 0.36% and 0.49%, respectively, is achieved. Most importantly, SHAP (SHapley Additive exPlanations) analysis is performed to identify important features for the predictions, hence enabling the explainability of the physics involved in the predictions of machine learning (ML) models for drop deformation and the interaction between the drop and gas flows. Global interpretation of SHAP values identifies We and Re as the most important features to predict the drop shape and drag. Based on the SHAP analysis, several reduced-feature models are developed. All the reduced-feature models can predict drop shape and drag coefficient within 4% average error in radius and drag coefficient for any We and Re combinations within the prescribed parameter space. The ML models developed in this study demonstrate a tremendous computational advantage over the traditional CFD simulations. The drop shape and drag coefficient evolution can be predicted within seconds for a single case, whereas the CFD simulation takes several days to a week on a single processor.

Toward coherent quantum computation of scattering amplitudes with a measurement-based photonic quantum processor

In recent years, applications of quantum simulation have been developed to study the properties of strongly interacting theories. This has been driven by two factors: on the one hand, needs from theorists to have access to physical observables that are prohibitively difficult to study using classical computing; on the other hand, quantum hardware becoming increasingly reliable and scalable to larger systems. In this work, we discuss the feasibility of using quantum optical simulation for studying scattering observables that are presently inaccessible via lattice QCD and are at the core of the experimental program at Jefferson Laboratory, the future Electron-Ion Collider, and other accelerator facilities. We show that recent progress in measurement-based photonic quantum computing can be leveraged to provide deterministic generation of required exotic gates and implementation in a single photonic quantum processor. Published by the American Physical Society 2024

Improved modularity and new features in ipie: Toward even larger AFQMC calculations on CPUs and GPUs at zero and finite temperatures.

ipie is a Python-based auxiliary-field quantum Monte Carlo (AFQMC) package that has undergone substantial improvements since its initial release [Malone et al., J. Chem. Theory Comput. 19(1), 109-121 (2023)]. This paper outlines the improved modularity and new capabilities implemented in ipie. We highlight the ease of incorporating different trial and walker types and the seamless integration of ipie with external libraries. We enable distributed Hamiltonian simulations of large systems that otherwise would not fit on a single central processing unit node or graphics processing unit (GPU) card. This development enabled us to compute the interaction energy of a benzene dimer with 84 electrons and 1512 orbitals with multi-GPUs. Using CUDA and cupy for NVIDIA GPUs, ipie supports GPU-accelerated multi-slater determinant trial wavefunctions [Huang et al. arXiv:2406.08314 (2024)] to enable efficient and highly accurate simulations of large-scale systems. This allows for near-exact ground state energies of multi-reference clusters, [Cu2O2]2+ and [Fe2S2(SCH3)4]2-. We also describe implementations of free projection AFQMC, finite temperature AFQMC, AFQMC for electron-phonon systems, and automatic differentiation in AFQMC for calculating physical properties. These advancements position ipie as a leading platform for AFQMC research in quantum chemistry, facilitating more complex and ambitious computational method development and their applications.

Comment on “Recovering noise-free quantum observables”

Zero-noise extrapolation (ZNE) stands as the most widespread quantum error mitigation technique used in the recovery of noise-free expectation values of observables of interest by means of noisy intermediate-scale quantum (NISQ) machines. Recently, Otten and Gray proposed a multidimensional generalization of polynomial ZNE for systems where there is no tunable global noise source [M. Otten , ]. Specifically, the authors refer to multiqubit systems where each of the qubits experiences several noise processes with different rates, i.e., a nonidentically distributed noise model. The authors proposed a hypersurface method for mitigating such noise, which is technically correct in the sense that if the required samples are obtained, the observable can be mitigated. However, the proposed method presents an excessive experiment repetition overhead, making it impractical, at least from the perspective of quantum computing. Specifically, we discuss that in order to perform a simple mitigation task to multinomial order 3 considering 100 qubits, there, 2.5 years or over a million quantum processors running in parallel would be required. In this Comment, we show that the traditional extrapolation techniques can be applied for such a nonidentically distributed noise setting consisting of many different noise sources, implying that the measurement overhead is practical. In fact, we discuss that the previous mitigation task can be resolved in the order of minutes using a single quantum processor by means of standard ZNE. To do so, we clarify what it is meant by a tunable global noise source in the context of ZNE, a concept that we consider important to be clarified for a correct understanding of how and why these methods work. Published by the American Physical Society 2024

Rapid patient-specific organ dose estimation in computed tomography scans via integration of radiomics features and neural networks.

Computed tomography (CT) offers detailed cross-sectional images of internal anatomy for disease detection but carries a risk of solid cancer or blood malignancies due to exposure to X-ray radiation. This study aimed to develop a new method to quickly predict patient-specific organ doses from CT examinations by training neural networks (NNs) based on radiomics features. CT Digital Imaging and Communications in Medicine (DICOM) image data were exported to DeepViewer, a clinical autosegmentation software, to segment the regions of interest (ROIs) for patient organs. Radiomics feature extraction was performed based on the selected CT data and ROIs. Reference organ doses were computed using Monte Carlo (MC) simulations. Patient-specific organ doses were predicted by training a NN model based on radiomics features and reference doses. For the dose prediction performance, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were evaluated on the test sets. The robustness of the NN model was evaluated via the random rearrangement of patient samples in the training and test sets. The maximal difference between the reference and predicted doses was less than 1 mGy for all investigated organs. The range of MAPE was 1.68% to 5.2% for head organs, 11.42% to 15.2% for chest organs, and 5.0% to 8.0% for abdominal organs; the maximal R2 values were 0.93, 0.86, and 0.89 for the head, chest, and abdominal organs, respectively. The radiomics feature-based NN model can achieve accurate prediction of patient-specific organ doses at a high speed of less than 1 second using a single central processing unit, which supports its use as a user-friendly online clinical application.

Design and research of high-performance convolutional neural network accelerator based on Chipyard

Abstract Neural network accelerator performs well in the research and verification of neural network models. In this paper, a convolutional neural network accelerator system composed of RISC-V processor core and Gemmini array accelerator is designed in Chisel language within the Chipyard framework, and the acceleration effect of different Gemmini array configurations for different input matrices is further investigated. The result shows that the accelerator system can achieve thousands of times acceleration compared with a single processor for large matrix calculations.

Heralding entangled optical photons from a microwave quantum processor

Exploiting the strengths of different quantum hardware components may increase the capabilities of emerging quantum processors. Here we propose and analyze a quantum architecture that leverages the nonlocal connectivity of optics, along with the exquisite quantum control offered by superconducting microwave circuits, to produce entangled optical resource states. In contrast to previous proposals on optically distributing entanglement between superconducting microwave processors, we use squeezing between microwaves and optics to produce microwave-optical Bell pairs in a dual-rail encoding from a single microwave quantum processor. Moreover, the microwave quantum processor allows us to deterministically entangle microwave-optical Bell pairs into larger cluster states, from which entangled optical photons can be extracted through microwave measurements. Our scheme paves the way for small microwave quantum processors to create heralded entangled optical resource states for optical quantum computation, communication, and sensing using imperfect microwave-optics transducers. We expect that improved isolation of the superconducting processor from stray optical fields will allow the scheme to be demonstrated with currently available hardware. Published by the American Physical Society 2024

Replicated multistage interconnection networks: QoS evaluation for parallel and distributed computing

Introduction to big data becomes very important with dealing in high-performance parallel distributed computing, especially in those systems where communication among a number of processors is required. The current paper takes into consideration different topologies for the Shuffle Exchange Network (SEN) in order to attain optimal data transfer among such scenarios. SEN topologies are a key feature in connecting several processors and implementing the data transfer among them where a single processor fails to handle the load. The study hereby reports in the performance, reliability, and cost analysis of these topologies. These topologies, some of which are advocated to have better performance in many studies, include replicated networks. Our research demystifies claims made in earlier papers that replicated networks have inflated reliability and higher costs due to their additional links. Researchers are therefore guided on accurate performance data that will lead them to make optimum choices of SEN topologies for targeted applications. These findings further highlight that the trade-offs between reliability and cost must be carefully considered during network design so as to arrive at better results for big data communication and computation in parallel and distributed systems. This work provides important insights into the correct evaluation of SEN topologies, helping to correct the misleading facts and to have better network selection for various real-time application realizations.

Accelerating real-time deterministic discovery through single instruction multiple data graphical processor unit for executing distributed event logs

With the rapid expansion of process mining implementation in global enterprises distributed across numerous branches, there is a critical requirement to develop an application qualified for real-time operation with fast and precise data integration. To address this challenge, computational parallelism emerges as a feasible solution to accelerate data analytics, with graphical processor unit (GPU) computing currently trending for achieving parallelism acceleration. In this study, we developed a process mining application to optimize parallel and distributed process discovery through a combination of central processing unit (CPU) and GPU computing. The use of this computing combination is leveraged for executing multi-windowing threads within multi-instruction, multiple data (MIMD) in the CPU for streaming distributed event logs, using multi-instruction, single data (MISD) within the CPU to deploy a large footprint pipeline to the GPU, and then utilizing single instruction, multiple data (SIMD) to execute global thread discovery within the GPU. This method significantly accelerates performance in real-time distributed discovery. By reducing branch divergence in SIMD on the global thread GPU parallelism, it outperformed local-thread CPU execution in deterministic discovery, speeding up from 10 to 40 times under specific conditions using a novel min-max flag algorithm implemented within the main steps of the process discovery.

On the total energy consumption of scalable cache-aided multi-CPU cell-free massive MIMO systems

In conventional cell-free (CF) systems, the scalability and feasibility are hindered by the fact that a single central processing unit (CPU) manages all access points and the stringent synchronization requirement for coherent transmission (CT) across the entire network. Multi-CPU CF systems emerge as a scalable and feasible alternative, yet they still encounter the typical CF system challenge of high downlink transmit energy consumption from extensive fronthaul and backhaul transmissions. To tackle this issue, we propose a scalable downlink partial coherent transmission (PCT) strategy for multi-CPU CF systems to provide high spectral efficiency (SE), reduce transmission time and thereby lower energy consumption, while keeping decoding complexity manageable. Based on the PCT strategy, we introduce scalable cache-aided multi-CPU CF systems and develop the corresponding total energy consumption (TEC) model. A successive convex approximation algorithm is proposed to obtain the suboptimal cache placement to minimize the TEC. Simulation results indicate that the proposed PCT-based scalable cache-aided multi-CPU CF systems can significantly reduce the TEC and consistently maintain the TEC at a relatively low level across all Zipf parameters by leveraging the high SE of CT and the independent transmission of non-coherent transmission.

Ant Colony Algorithm for Single Processor Scheduling with Minimization of Peak Resource Usage

Ant Colony Algorithm for Single Processor Scheduling with Minimization of Peak Resource Usage

Bidirectional state transfer between superconducting and microwave-photon qubits by single reflection

The number of superconducting qubits contained in a single quantum processor is increasing steadily. However, to realize a truly useful quantum computer, it is inevitable to increase the number of qubits much further by distributing quantum information among distant processors using flying qubits. A key element towards this goal is a deterministic quantum interaction between superconducting-atom and propagating microwave-photon qubits. Here, we confirm the bidirectional state transfer between them, which completes by simply reflecting the photon at the atom. The averaged fidelity of the photon-to-atom (atom-to-photon) state transfer reaches 0.826 (0.801), limited mainly by the lifetime of the atom qubit. The present technology would be useful for future distributed quantum computation with superconducting qubits. Published by the American Physical Society 2024

Food wastes for bioproduct production and potential strategies for high feedstock variability

Unavoidable food wastes could be an important feedstock for industrial biotechnology, while their valorization could provide added value for the food processor. However, despite their abundance and low costs, the heterogeneous/mixed nature of these food wastes produced by food processors and consumers leads to a high degree of variability in carbon and nitrogen content, as well as specific substrates, in food waste hydrolysate. This has limited their use for bioproduct synthesis. These wastes are often instead used in anaerobic digestion and mixed microbial culture, creating a significant knowledge gap in their use for higher value biochemical production via pure and single microbial culture. To directly investigate this knowledge gap, various waste streams produced by a single food processor were enzymatically hydrolyzed and characterized, and the degree of variability with regard to substrates, carbon, and nitrogen was quantified. The impact of hydrolysate variability on the viability and performance of polyhydroxyalkanoates biopolymers production using bacteria (Cupriavidus necator) and archaea (Haloferax mediterranei) as well as sophorolipids biosurfactants production with the yeast (Starmerella bombicola) was then elucidated at laboratory-scale. After which, strategies implemented during this experimental proof-of-concept study, and beyond, for improved industrial-scale valorization which addresses the high variability of food waste hydrolysate were discussed in-depth, including media standardization and high non-selective microbial organisms growth-associated product synthesis. The insights provided would be beneficial for future endeavors aiming to utilize food wastes as feedstocks for industrial biotechnology.

Probing single electrons across 300-mm spin qubit wafers

Building a fault-tolerant quantum computer will require vast numbers of physical qubits. For qubit technologies based on solid-state electronic devices1–3, integrating millions of qubits in a single processor will require device fabrication to reach a scale comparable to that of the modern complementary metal–oxide–semiconductor (CMOS) industry. Equally important, the scale of cryogenic device testing must keep pace to enable efficient device screening and to improve statistical metrics such as qubit yield and voltage variation. Spin qubits1,4,5 based on electrons in Si have shown impressive control fidelities6–9 but have historically been challenged by yield and process variation10–12. Here we present a testing process using a cryogenic 300-mm wafer prober13 to collect high-volume data on the performance of hundreds of industry-manufactured spin qubit devices at 1.6 K. This testing method provides fast feedback to enable optimization of the CMOS-compatible fabrication process, leading to high yield and low process variation. Using this system, we automate measurements of the operating point of spin qubits and investigate the transitions of single electrons across full wafers. We analyse the random variation in single-electron operating voltages and find that the optimized fabrication process leads to low levels of disorder at the 300-mm scale. Together, these results demonstrate the advances that can be achieved through the application of CMOS-industry techniques to the fabrication and measurement of spin qubit devices.

A hybrid one‐step leapfrog ADI‐FDTD and subgridding approach based on a heterogeneous computing platform

AbstractThe finite‐difference time‐domain (FDTD) method often demands numerous grids for fine structure analysis, leading to high computational costs. To address this challenge, a subgridding scheme emerges as an attractive solution. However, the explicit nature of the FDTD subgrid method, constrained by Courant–Friedrichs–Lewy (CFL) conditions, can lead to inefficiencies with complex structures. In response, the leapfrog alternating‐direction implicit (ADI)‐FDTD method, exhibiting unconditional stability, has been developed to overcome CFL limitations. This paper presents a hybrid approach merging the one‐step leapfrog ADI‐FDTD method with a subgridding scheme using heterogeneous computing. By optimizing hardware usage and leveraging different device performances, this approach maximizes throughput in heterogeneous systems. Central processing units and graphics processing units handle workloads to minimize system latency. Simulation results on heterogeneous platforms demonstrate superior efficiency over single processor setups while maintaining simulation accuracy, especially in analyzing electromagnetic scattering from multiscale structures.

Simultaneous magnitude and slip distribution characterization from high-rate GNSS using deep learning: case studies of the 2021 Mw 7.4 Maduo and 2023 Turkey doublet events

SUMMARY Rapid and accurate characterization of earthquake sources is crucial for mitigating seismic hazards. In this study, based on 18 000 scenario ruptures ranging from Mw 6.4 to Mw 8.3 and corresponding synthetic high-rate Global Navigation Satellite System (HR-GNSS) waveforms, we developed a multibranch neural network framework, the continental large earthquake agile response (CLEAR), to simultaneously determine the magnitude and slip distributions. We apply CLEAR to recent large strike-slip events, including the 2021 Mw 7.4 Maduo earthquake and the 2023 Mw 7.8 and Mw 7.6 Turkey doublet. The model generally estimates the magnitudes successfully at 32 s with errors of less than 0.15, and predicts the slip distributions acceptably at 64 s, requiring only approximately 30 ms on a single CPU (Central Processing Unit). With optimal azimuthal coverage of stations, the system is relatively robust to the number of stations and the time length of the received data.

Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Cardiac digital twins provide a physics and physiology informed framework to deliver personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. We use Latent Neural Ordinary Differential Equations (LNODEs) to learn the pressure-volume dynamics of a heart failure patient. Our surrogate model is trained from 400 simulations while accounting for 43 parameters describing cell-to-organ cardiac electromechanics and cardiovascular hemodynamics. LNODEs provide a compact representation of the 3D-0D model in a latent space by means of an Artificial Neural Network that retains only 3 hidden layers with 13 neurons per layer and allows for numerical simulations of cardiac function on a single processor. We employ LNODEs to perform global sensitivity analysis and parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor.

ARM-CO-UP: ARM CO operative U tilization of P rocessors

HMPSoCs combine different processors on a single chip. They enable powerful embedded devices, which increasingly perform ML inference tasks at the edge. State-of-the-art HMPSoCs can perform on-chip embedded inference using different processors, such as CPUs, GPUs, and NPUs. HMPSoCs can potentially overcome the limitation of low single-processor CNN inference performance and efficiency by cooperative use of multiple processors. However, standard inference frameworks for edge devices typically utilize only a single processor. We present the ARM-CO-UP framework built on the ARM-CL library. The ARM-CO-UP framework supports two modes of operation – Pipeline and Switch. It optimizes inference throughput using pipelined execution of network partitions for consecutive input frames in the Pipeline mode. It improves inference latency through layer-switched inference for a single input frame in the Switch mode. Furthermore, it supports layer-wise CPU/GPU DVFS in both modes for improving power efficiency and energy consumption. ARM-CO-UP is a comprehensive framework for multi-processor CNN inference that automates CNN partitioning and mapping, pipeline synchronization, processor type switching, layer-wise DVFS , and closed-source NPU integration.

Application of the fractional Fourier transform for decryption in experimental optical cryptosystems

In this contribution, we introduce a new practical approach to apply the fractional Fourier transform (FrFT) in the modeling of two optical systems: free space propagation (FSP) and a single lens processor (SLP). This formulation presents a simple way to stablish a direct relationship between physical parameters of the two optical systems and a real-valued fractional order. Furthermore, we employ and compare two numerical methods for evaluating the FrFT: the convolution and the Fresnel transform. Consequently, we apply this innovative approach to the digital decryption process in an opto-digital joint transform correlator cryptosystem, considering both the FSP and the SLP variants. We analyze both numerically and experimentally encrypted data to support our proposed method and to investigate the sensitivity of the decryption process with the fractional order. Notably, we obtain similar decryption results for both numerically and experimentally encrypted objects, demonstrating excellent agreement between the theoretical model, the numerical test, and the experiment.