Parallel Scheme Research Articles

A New High-Order Fractional Parallel Iterative Scheme for Solving Nonlinear Equations

Solving fractional-order nonlinear equations is crucial in engineering, where precision and accuracy are essential. This study introduces a novel fractional parallel technique for solving nonlinear equations. To enhance convergence, we incorporate a simple root-finding method of order 3γ + 1 as a correction term in the parallel scheme. Theoretical analysis shows that the parallel scheme achieves a convergence order of 6γ + 3. Using a dynamical system approach, we identify optimal parameter values, and the symmetry in the dynamical planes for different fractional parameters demonstrates the method’s stability and consistency in handling nonlinear problems. These parameter values are applied to the parallel scheme, yielding highly consistent results. Several engineering problems are examined to assess the method’s efficiency, stability, and consistency compared to existing methods.

Metabolic engineering of Escherichia coli for the utilization of methylsuccinate, the product of methane activation via fumarate addition.

Methylsuccinate is a branched-chain, 5-carbon (C5) dicarboxylate that can be generated from the O2-independent activation of methane via fumarate addition. However, no established metabolic pathway enables growth and product synthesis from methylsuccinate. Here, we report a synthetic pathway that converts methylsuccinate into two precursor metabolites: pyruvate and acetyl-CoA. The pathway was constructed through rational design and validated both in vitro and in vivo using E. coli as the host. Subsequently, growth on methylsuccinate as the sole carbon source was achieved using two parallel strategies: adaptive laboratory evolution and enzyme mining. Through the latter approach, we identified a heterologous electron transfer pathway mediated by previously uncharacterized enzymes and integrated into E. coli enabling the conversion of methylsuccinyl-CoA to mesaconyl-C4-CoA. The engineered strain demonstrated efficient growth on various C5 dicarboxylates including methylsuccinate, mesaconate, and itaconate, with a specific growth rate of 0.11 h-1 on methylsuccinate. This study represents an important step toward achieving synthetic methanotrophy, as the engineered strain can serve as a platform for screening potential methane activation enzymes and ultimately as a production chassis for the bioconversion of methane into various value-added products.

Leveraging the Hardware Resources to Accelerate cryo-EM Reconstruction of RELION on the New Sunway Supercomputer

The fast development of biomolecular structure determination has enabled the fine-grained study of objects in the micro-world, such as proteins and RNAs. The world is benefited. However, as the computational algorithms are constantly developed, the enrichment of features increases the algorithmic complexity and brings more computationally unfriendly modules. It calls for efficient solutions to leverage the rich and various hardware resources from the world’s most state-of-the-art supercomputing systems, and to fully accelerate the performance of the applications. In this paper, we present our efforts on porting and optimizing the 3D reconstruction of RELION, one of the most popular cryo-EM software for biomolecular structure determinations, by leveraging different resources of the latest generation of Sunway heterogeneous supercomputer. Several novel approaches are proposed to resolve different challenges faced by the complex algorithm, including a multi-level parallel scheme and operator optimizations to smartly map and scale RELION, efficient strategies to largely address the memory bottlenecks and improve data locality, lock-free writing solutions to minimize write-write conflicts, and pipelining approaches to obtain excellent computation and communication overlap. Combining all proposed optimizations, the computation time is greatly reduced to under 2 hours, achieving 11.9 × and 8.9 × speedups on two different datasets. The overall design scales to 131,072 cores, increasing parallel efficiency from 33% to 61% and from 46% to 70%, respectively. To the best of our knowledge, this is the first work that fully optimized and scaled the 3D reconstruction of RELION using the latest Sunway system.

Three-dimensional quasi-complete information numerical simulation of gravity anomalies and Parallel Computing on CPU-GPU

Abstract In order to improve the efficiency and accuracy of numerical simulation of gravity anomalies in large-scale complex models, this paper proposes a method for three-dimensional numerical simulation of gravity anomalies under arbitrary terrain, and implements its CPU-GPU parallel acceleration scheme. By performing a two-dimensional Fourier transform along the horizontal direction, three-dimensional partial differential satisfied by gravitational potential are transformed into one-dimensional ordinary differential equations in different wavenumbers, reducing computational and storage requirements. Moreover, the ordinary differential equations in different wavenumbers are independent of each other and have good parallelism. By retaining the vertical direction as the spatial domain and introducing traveling wave decomposition, the effects caused by upper and lower boundaries are eliminated. The vertical grid is arbitrary. A two-dimensional quasi-complete information Fourier transform is utilized to enhance both the accuracy and efficiency of the Fourier transform process, allowing both uniform and non-uniform flexible sampling. Based on the high parallelism, one-dimensional ordinary differential equations are solved in parallel using CPU, and quasi-complete information Fourier transform are computed in parallel using GPU. An abnormal sphere is designed to analyze the wavenumber spectral distribution characteristics of the anomalous potential and summarize the wavenumber sampling rules. The logarithmic uniform sampling in the wavenumber domain has high accuracy. Compared with Gauss-FFT, the quasi-complete information Fourier transform selects fewer wavenumbers and exhibits higher accuracy. The computational efficiency is greatly enhanced through the use of single-precision CPU-GPU parallel scheme, and a model with tens of millions of nodes only takes a few seconds. Two terrain models are designed to verify the algorithm's adaptability to arbitrary complex terrains. This is significant for achieving fine inversion imaging and interpretation of gravity anomalies under large-scale complex conditions.

PragFormer: Data-Driven Parallel Source Code Classification with Transformers

AbstractMulti-core shared memory architectures have become ubiquitous in computing hardware nowadays. As a result, there is a growing need to fully utilize these architectures by introducing appropriate parallelization schemes, such as OpenMP worksharing-loop constructs, to applications. However, most developers find introducing OpenMP directives to their code hard due to pervasive pitfalls in managing parallel shared memory. To assist developers in this process, many compilers, as well as source-to-source (S2S) translation tools, have been developed over the years, tasked with inserting OpenMP directives into code automatically. In addition to having limited robustness to their input format, these compilers still do not achieve satisfactory coverage and precision in locating parallelizable code and generating appropriate directives. Recently, many data-driven AI-based code completion (CC) tools, such as GitHub CoPilot, have been developed to ease and improve programming productivity. Leveraging the insights from existing AI-based programming-assistance tools, this work presents a novel AI model that can serve as a parallel-programming assistant. Specifically, our model, named PragFormer, is tasked with identifying loops that can benefit from conversion to parallel worksharing-loop construct (OpenMP directive) and even predict the need for specific data-sharing attributes clauses on the fly. We created a unique database, named Open-OMP, specifically for this goal. Open-OMP contains over 32,000 unique code snippets from different domains, half of which contain OpenMP directives, while the other half do not. We experimented with different model design parameters for these tasks and showed that our best-performing model outperforms a statistically-trained baseline as well as a state-of-the-art S2S compiler. In fact, it even outperforms the popular generative AI model of ChatGPT. In the spirit of advancing research on this topic, we have already released source code for PragFormer as well as Open-OMP dataset to public. Moreover, an interactive demo of our tool, as well as a Hugging Face webpage to experiment with our tool, are already available.

Investigation of ablation threshold and microchannel fabrication on stainless steel using ultrafast laser

ABSTRACT Ultrafast laser processing is a highly versatile tool for creating microscale structures in many materials. The ablation and micromachining characteristics of AISI 304 stainless steel were examined using a femtosecond-pulsed laser. Parameters such as power, scanning speed, transverse overlap, scanning strategies, and shielding gases were systematically varied. The ablation threshold fluence for SS 304 was calculated as 0.16 J/cm2. Two ablation regimes were observed: the gentle regime, where smooth crater walls were formed under low pulse energy (below 20 µJ), and the strong regime, characterized by rough walls created under high pulse energy (above 20 µJ). The optimal parameters for fabricating microchannels on SS 304 surfaces comprised a laser power of 100 mW, a scanning speed of 30 mm/s, a line-to-line distance of 5 µm, and a parallel scan strategy along the major axis. This study provides a practical approach for enhancing the microchannel fabrication process on stainless steel.

Parallel molecular data storage by printing epigenetic bits on DNA

DNA storage has shown potential to transcend current silicon-based data storage technologies in storage density, longevity and energy consumption1–3. However, writing large-scale data directly into DNA sequences by de novo synthesis remains uneconomical in time and cost4. We present an alternative, parallel strategy that enables the writing of arbitrary data on DNA using premade nucleic acids. Through self-assembly guided enzymatic methylation, epigenetic modifications, as information bits, can be introduced precisely onto universal DNA templates to enact molecular movable-type printing. By programming with a finite set of 700 DNA movable types and five templates, we achieved the synthesis-free writing of approximately 275,000 bits on an automated platform with 350 bits written per reaction. The data encoded in complex epigenetic patterns were retrieved high-throughput by nanopore sequencing, and algorithms were developed to finely resolve 240 modification patterns per sequencing reaction. With the epigenetic information bits framework, distributed and bespoke DNA storage was implemented by 60 volunteers lacking professional biolab experience. Our framework presents a new modality of DNA data storage that is parallel, programmable, stable and scalable. Such an unconventional modality opens up avenues towards practical data storage and dual-mode data functions in biomolecular systems.

Adjoint-Based Calibration of Nonlinear Stochastic Differential Equations

To study the nonlinear properties of complex natural phenomena, the evolution of the quantity of interest can be often represented by systems of coupled nonlinear stochastic differential equations (SDEs). These SDEs typically contain several parameters which have to be chosen carefully to match the experimental data and to validate the effectiveness of the model. In the present paper the calibration of these parameters is described by nonlinear SDE-constrained optimization problems. In the optimize-before-discretize setting a rigorous analysis is carried out to ensure the existence of optimal solutions and to derive necessary first-order optimality conditions. For the numerical solution a Monte–Carlo method is applied using parallelization strategies to compensate for the high computational time. In the numerical examples an Ornstein–Uhlenbeck and a stochastic Prandtl–Tomlinson bath model are considered.

A Review of the Homogenized Lattice Boltzmann Method for Particulate Flow Simulations: From Fundamentals to Applications

The homogenized lattice Boltzmann method (HLBM) has emerged as a flexible computational framework for studying particulate flows, providing a monolithic approach to modeling pure fluid flows and flows through porous media, including moving solid and porous particles, within a unified framework. This paper presents a thorough review of HLBM, elucidating its underlying principles and highlighting its diverse applications to particle-laden flows in various fields as reported in literature. These include studies leading to new fundamental knowledge on the settling of single arbitrarily shaped particles as well as application-oriented research on wall-flow filters, hindered settling, and evaluation of the damage potential during particle transport. Among the strengths of HLBM are its monolithic approach, which allows seamless simulation of different fluid-solid interactions, and its ability to handle arbitrary particle shapes, including irregular and concave geometries, while resolving surface interactions to capture local forces. In addition, its parallel scheme based on the lattice Boltzmann method (LBM) results in high computational efficiency, making it suitable for large-scale simulations, even though LBM requires small time steps. Important future development needs are identified, including the addition of a lubrication force correction model, performance enhancements, such as support for hybrid parallelization and GPU, and the extension of compatible contact models to accommodate concave shapes. These advances promise expanded capabilities for HLBM and broader applicability for solving complex real-world problems.

Device placement using Laplacian PCA and graph attention networks

Abstract The exponential growth in data and parameters in modern neural networks has created the need to distribute these models across multiple devices for efficient training, resulting in the device placement problem. Existing graph encoding approaches for device placement suffer from low efficiency when searching for optimal parallel strategies, primarily due to suboptimal positional information retrieval. To address these challenges, we propose the Laplacian Principal Component Analysis-graph attention networks (LPCA-GAT) model. Firstly, we employ GAT to capture complex relationships between nodes and generate node encodings. Secondly, we leverage LPCA on the graph Laplacian matrix to extract crucial low-dimensional positional information. Finally, by integrating these two components, we obtain the final node encodings. This enhances the representation capability of nodes within the graph, enabling efficient device placement. The experimental results demonstrate that LPCA-GAT achieves superior device placement results, specifically accelerating execution and computation time by 13.24% and 96.38%, respectively, leading to significant improvements in both operational efficiency and performance.

Parallel adaptive large neighborhood search based on spark to solve VRPTW

Aiming at the multi-objective vehicle path planning problem with time windows (VRPTW), a Spark-based parallel Adaptive Large Neighborhood Search algorithm (Spark-ALNS) is proposed to solve it. The main design of the 4-point strategy: (1) Design a new simulated annealing algorithm cooling strategy to achieve a better jump out of the local optimal solution. (2) Adopt CW initialization to accelerate the convergence speed. (3) Use three destruction operators and three repair operators to implement local path optimization. (4) A new parallel strategy is proposed to improve the algorithm’s accuracy and reduce the running time. To illustrate the algorithm’s effectiveness, the arithmetic example in Solomon is used as an example. The experimental results show that the proposed Spark-ALNS can find better solutions, get the known optimal solutions for 41 out of 56 instances, and find new optimal solutions for 31 algorithms, which outperforms other evolutionary algorithms. The runtime is 3–5 times better than other parallel algorithms and is able to solve VRPTW effectively.

Manipulation of high-fidelity sidebands under Large detuning by Floquet technology: application to Multi-Mode Cooling

Abstract The Floquet technology, a powerful way to manipulate quantum states, is employed to drive sidebands transition under large detuning. Our results demonstrate that high fidelities over 99% can be achieved through optimizing suitable modulation frequencies under large detuning. We observe high-fidelity transitions within a high bandwidth by utilizing a single modulation frequency and reveal this capability is due to the emergence of a flat-band structure in the bandwidth range. The key finding of high-fidelity sideband manipulation under large detuning is experimentally confirmed in nuclear magnetic resonance platform. Finally, we propose a new parallel sideband cooling scheme that enables simultaneous cooling of multiple motional modes. This approach improves the cooling rate compared to conventional schemes with fixed laser frequency and power, and eliminates the need for mode-specific addressing. Our Floquet parallel scheme is applicable to any harmonic oscillator system and is not limited by bandwidth in theory.

9284 Leveraging Inter-Individual Transcriptional Correlation Structure To Infer Discrete Signaling Mechanisms Across Metabolic Tissues

Abstract Disclosure: M. Zhou: None. I. Tamburini: None. C. Van: None. J. Molendijk: None. L.M. Velez: None. C. Nguyen: None. R. Yeo: None. C. Filho: None. A.L. Hevener: None. J. Justice: None. L.M. Sparks: None. E.E. Kershaw: None. D. Nicholas: None. M. Seldin: None. Inter-organ communication is a vital process to maintain physiologic homeostasis, and its dysregulation contributes to many human diseases. Given that circulating bioactive factors are stable in serum, occur naturally, and are easily assayed from blood, they present obvious focal molecules for therapeutic intervention and biomarker development. A major obstacle in the characterization of such soluble factors is that defining their tissues and pathways of action requires extensive experimental testing in cells and animal models. Recently, studies have shown that secreted proteins mediating inter-tissue signaling could be identified by “brute-force” surveys of all genes within RNA-sequencing measures across tissues within a population. Expanding on this intuition, we reasoned those parallel strategies could be leveraged to understand how individual genes mediate signaling across metabolic tissues through correlative analyses of genetic gene variation between individuals. Thus, genetics comparison of quantitative levels of gene expression relationships between organs in a population could aid in understanding cross-organ signaling by adopting a gene-centric approach. Here, we surveyed gene-gene genetic correlation structure for ∼6.1x10^12 gene pairs across 18 metabolic tissues in 310 human individuals and 7 tissues in 103 diverse strains of mice fed a normal chow or HFHS diet. where the variation of genes such as FGF21, ADIPOQ, GCG, and IL6 showed enrichments that recapitulate experimental observations. Further, similar analyses were applied to explore both local within-tissue signaling mechanisms (liver PCSK9) as well as genes encoding enzymes producing metabolites (adipose PNPLA2), where genetic inter-individual correlation structure aligned with known roles for these critical metabolic pathways. Examination of sex hormone receptor correlations in mice highlighted the difference in tissue-specific variation in relationships with metabolic traits. Finally, we utilized this resource to suggest new functions for metabolic coordination between organs. For example, we prioritized key proteins for putative signaling between skeletal muscle and hippocampus, and further suggest colon as a central coordinator for systemic circadian clocks. We refer to this resource as Genetically-Derived Correlations Across Tissues (GD-CAT) where all tools and data are built into a web portal enabling users to perform these analyses without a single line of code (gdcat.org). This resource enables the querying of any gene in any tissue to find genetic coregulation correlated patterns of genes, cell types, pathways, and network architectures across metabolic organs. Presentation: 6/1/2024

7226 Leveraging Inter-Individual Transcriptional Correlation Structure To Infer Discrete Signaling Mechanisms Across Metabolic Tissues

Abstract Disclosure: M. Zhou: None. I. Tamburini: None. C. Van: None. J. Molendijk: None. L.M. Velez: None. C. Nguyen: None. R. Yeo: None. C. Filho: None. A.L. Hevener: None. J. Justice: None. L.M. Sparks: None. E.E. Kershaw: None. D. Nicholas: None. M. Seldin: None. Inter-organ communication is a vital process to maintain physiologic homeostasis, and its dysregulation contributes to many human diseases. Given that circulating bioactive factors are stable in serum, occur naturally, and are easily assayed from blood, they present obvious focal molecules for therapeutic intervention and biomarker development. A major obstacle in the characterization of such soluble factors is that defining their tissues and pathways of action requires extensive experimental testing in cells and animal models. Recently, studies have shown that secreted proteins mediating inter-tissue signaling could be identified by “brute-force” surveys of all genes within RNA-sequencing measures across tissues within a population. Expanding on this intuition, we reasoned those parallel strategies could be leveraged to understand how individual genes mediate signaling across metabolic tissues through correlative analyses of genetic gene variation between individuals. Thus, genetics comparison of quantitative levels of gene expression relationships between organs in a population could aid in understanding cross-organ signaling by adopting a gene-centric approach. Here, we surveyed gene-gene genetic correlation structure for ∼6.1x10^12 gene pairs across 18 metabolic tissues in 310 human individuals and 7 tissues in 103 diverse strains of mice fed a normal chow or HFHS diet. where the variation of genes such as FGF21, ADIPOQ, GCG, and IL6 showed enrichments that recapitulate experimental observations. Further, similar analyses were applied to explore both local within-tissue signaling mechanisms (liver PCSK9) as well as genes encoding enzymes producing metabolites (adipose PNPLA2), where genetic inter-individual correlation structure aligned with known roles for these critical metabolic pathways. Examination of sex hormone receptor correlations in mice highlighted the difference in tissue-specific variation in relationships with metabolic traits. Finally, we utilized this resource to suggest new functions for metabolic coordination between organs. For example, we prioritized key proteins for putative signaling between skeletal muscle and hippocampus, and further suggest colon as a central coordinator for systemic circadian clocks. We refer to this resource as Genetically-Derived Correlations Across Tissues (GD-CAT) where all tools and data are built into a web portal enabling users to perform these analyses without a single line of code (gdcat.org). This resource enables the querying of any gene in any tissue to find genetic coregulation correlated patterns of genes, cell types, pathways, and network architectures across metabolic organs. Presentation: 6/1/2024

Dual-phase interface engineering via parallel modulation strategy for highly reversible Zn metal batteries

The reversibility and stability of aqueous Zn metal batteries (AZMBs) are largely limited by Zn dendrites and interfacial parasitic reactions. Herein, we propose a parallel modulation strategy to boost the reversibility of the Zn anode by introducing N,N,N’,N’-tetramethylchloroformamidinium hexafluorophosphate (TCFH) as an additive in the electrolyte. TCFH is composed of PF6− and TN+ with opposite charges. PF6− can spontaneously induce the in-situ generation of ZnF2 solid electrolyte interface (SEI) on the anode, which can improve the transport kinetics of Zn2+ at the interface, thus promoting the rapid and uniform deposition of Zn as well as inhibiting the growth of dendrites. In addition, TN+ is enriched at the anode surface during Zn deposition through the anchoring effect, which brings a reconfiguration of the ion/molecule distribution. The anchored-TN+ reduces the concentrations of H2O and SO42−, sufficiently restraining the parasitic reaction. Thanks to the dual-phase interface engineering constructed of PF6− and TN+ in parallel, the symmetric cell with the proposed electrolyte survives long cycling stability over 750 h at 20 mA cm−2, 10 mAh cm−2. This study offers a distinct viewpoint to the multidimensional optimization of Zn anodes for high-performance AZMBs.

GPU parallel implementation of a finite volume lattice Boltzmann method for incompressible flows

This work presents a graphics processing units (GPU) parallel algorithm of a cell-centered finite volume lattice Boltzmann method (FVLBM) on unstructured meshes. In the present GPU parallel algorithm, the parallelization is performed in the physical space. To reduce the frequency of GPU memory accesses, this algorithm develops coalesced access to GPU memory. In addition, to avoid the race for resources leading to data anomalies, such as dirty read or phantom read etc., and the double counting for flux calculation, the efficient face-based data structure often used for flux calculation in cells in the central processing unit (CPU) version of FVLBM is modified into a face-based data structure used for the fluxes on all faces, followed by a cell-based loop for the final residuals in all cells. Therefore, the proposed GPU parallel algorithm does not need to use the resource lock and retains the high efficiency of the face-based data structure in the fluxes computation to enhance its’ parallel efficiency. Additionally, to demonstrate the computational efficiency of the proposed GPU parallel algorithm, various benchmark studies are performed in this work by the proposed parallel scheme on a double precision NVIDIA GeForce RTX 3090Ti GPU card, including (a) the lid-driven flow in a two-dimensional (2D) square cavity, (b) a 2D flow past a cylinder, and (c) the lid-driven flow in a three-dimensional (3D) cubic cavity. The numerical results show that the proposed GPU parallel algorithm can be as accurate as the original CPU serial scheme with 1 to 2 orders of speedup.

Application of deep learning for automatic detection of table tennis balls from an intelligent serving machine

The Intelligent Table Tennis Serving Machine (TTSM) has revolutionized table tennis training by simulating the variability and precision of human opponents. However, current systems lack the capability to track and analyze ball landing points, limiting comprehensive performance evaluation. To address this, we propose a novel approach utilizing YOLOv8 as the benchmark model for detecting table tennis balls launched by a TTSM, tailored to meet the demands of intelligent training systems. Our key innovation involves integrating a Res2Net architecture enhanced with a Non-local Attention Module (NLAM) and a Dilated Atrous Spatial Pyramid Pooling (DASPP) unit into the neck network. This design improves the network’s ability to integrate multi-scale local features, thereby enhancing global information processing. The DASPP module, with adaptive dilation convolution, ensures thorough context comprehension. Additionally, we incorporate an Omni-dimensional Dynamic Convolution (ODConv) module in the detection head, employing parallel strategies to learn diverse and complementary attention cues, further enhancing detection accuracy and robustness. To validate our method, we conducted evaluations using the publicly available OpenTTGames dataset and a self-constructed dataset comprising over 10,000 table tennis ball images captured in various environments. Experimental results indicate that our method enhances mAP0.5, Precision, and Recall by 2.3%, 5.1%, and 0.9% on the OpenTTGames dataset, and by 3%, 2%, and 1.6% on the self-built dataset, respectively. Finally, we describe the optimization process of the intelligent TTSM system, which leverages keyframe analysis of ball landing points to provide feedback, thereby enabling continuous performance improvement in subsequent phases.

Three types of third-order non-Hermitian interaction of spontaneous six-wave and eight-wave mixing under multi-dressing effects

Non-Hermitian degeneracy, also known as exceptional point, has been recently seen as a new way to engineer the response of open physical systems. Based on natural non-Hermitian atomic coherence, we investigate spontaneous six-wave mixing (SSWM) and eight-wave mixing (SEWM) processes under both parity–time (PT) and anti-parity–time (anti-PT) symmetry, and we obtain high-dimensional coherent channels under third-order energy-level splitting. Finally, we reveal that the third-order non-Hermitian interaction between two dressing fields of the nested scheme is the strongest, and the parallel scheme is the weakest, with the cascade scheme considered intermediate between them. It can also be used to develop quantum memory devices with enhanced sensitivity in the atom-like system.

Deploying large language models on diverse computing architectures: A performance evaluation framework

Deploying large language models (LLMs) across diverse computing architectures is a critical challenge in the field of artificial intelligence, particularly as these models become increasingly complex and resource-intensive. This review presents a performance evaluation framework designed to systematically assess the deployment of LLMs on various computing architectures, including CPUs, GPUs, TPUs, and specialized accelerators. The framework is structured around key performance metrics such as computational efficiency, latency, throughput, energy consumption, and scalability. It considers the trade-offs associated with different hardware configurations, optimizing the deployment to meet specific application requirements. The evaluation framework employs a multi-faceted approach, integrating both theoretical and empirical analyses to offer comprehensive insights into the performance dynamics of LLMs. This includes benchmarking LLMs under varying workloads, data batch sizes, and precision levels, enabling a nuanced understanding of how these factors influence model performance across different hardware environments. Additionally, the framework emphasizes the importance of model parallelism and distribution strategies, which are critical for efficiently scaling LLMs on high-performance computing clusters. A significant contribution of this framework is its ability to guide practitioners in selecting the optimal computing architecture for LLM deployment based on application-specific needs, such as low-latency inference for real-time applications or energy-efficient processing for large-scale deployments. The framework also provides insights into cost-performance trade-offs, offering guidance for balancing the financial implications of different deployment strategies with their performance benefits. Overall, this performance evaluation framework is a valuable tool for researchers and engineers, facilitating the efficient deployment of LLMs on diverse computing architectures. By offering a systematic approach to evaluating and optimizing LLM performance, the framework supports the ongoing development and application of these models across various domains. This paper will evaluate the deployment of large language models (LLMs) on diverse computing architectures, including x86, ARM, and RISC-V platforms. It will discuss strategies for optimizing LLM performance, such as dynamic frequency scaling, core scaling, and memory optimization. The research will contribute to understanding the best practices for deploying AI applications on different architectures, supporting technological innovation and competitiveness.

An online tool with Google Earth Engine and cellular automata for seamlessly simulating global urban expansion at high resolutions

Projecting global urban expansion is crucial for environmental assessment under climate change scenarios. However, existing global future urban land products are typically provided at coarse resolutions (1 km) due to data and computing limitations. This hinders the accurate assessment of the impacts of global urban development at finer scales. Thus, we develop the first Cellular Automata (CA) online tool for simulating future global urban expansion in Google Earth Engine (GEE-CA), which can simulate future urban land change at a 30 m resolution under different SSP scenarios. GEE-CA enables seamless simulations of future urban land at high resolution through a partitioned parallel strategy. Seven large urban agglomerations are simulated under shared socioeconomic pathways as representative regions to present our fine-scale results. Comparatively, our datasets preserve significantly more spatial details than existing global urban land products. The improvement in resolution from 1 km to 30 m reduces errors by a range from 7.11% to 21.27% in the estimation of future urban area. So far, the proposed GEE-CA tool allows users to generate urban land projection maps for any defined region with a resolution as high as 30 m.