IEEE Transactions on Parallel and Distributed Systems最新文献

{"title":"TraDE: Network and Traffic-Aware Adaptive Scheduling for Microservices Under Dynamics","authors":"Ming Chen;Muhammed Tawfiqul Islam;Maria Rodriguez Read;Rajkumar Buyya","doi":"10.1109/TPDS.2025.3626424","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3626424","url":null,"abstract":"The transition from monolithic architecture to microservices has enhanced flexibility in application design and its scalable execution. This approach typically uses a computing cluster managed by a container orchestration platform to deploy microservices. However, this shift introduces significant challenges, particularly in the efficient scheduling of containerized services. These challenges are compounded by unpredictable scenarios such as dynamic incoming workloads with various execution traffic and variable communication delays among cluster nodes. Existing works often overlook the real-time traffic impacts of dynamic requests on running microservices, as well as the varied communication delays across cluster nodes. Consequently, even optimally deployed microservices could suffer from significant performance degradation over time. To address these issues, we propose a network and traffic-aware adaptive scheduling framework, <monospace>TraDE</monospace>, which can adaptively redeploy microservice instances to maintain desired performance amid changing traffic and network conditions within the hosting cluster. We have implemented <monospace>TraDE</monospace> as an extension to the Kubernetes platform. Additionally, we deployed realistic microservice applications in a real compute cluster and conducted extensive experiments to assess our framework’s performance in various scenarios. The results demonstrate the effectiveness of <monospace>TraDE</monospace> in rescheduling running microservices to enhance end-to-end performance while maintaining a high goodput ratio. Compared with the existing method NetMARKS, <monospace>TraDE</monospace> outperforms it by reducing the average response time of the application by up to 48.3%, and improving the throughput by up to 1.2–1.5 × across workloads while maintaining a goodput ratio of 95.36%, and showing robust adaptive capability to meet QoS targets under sustained workloads and dynamic networking conditions.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"37 1","pages":"76-89"},"PeriodicalIF":6.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Discrete Morse Sandwich: Efficient Computation of Persistence Diagrams for Massive Scalar Data","authors":"Eve Le Guillou;Pierre Fortin;Julien Tierny","doi":"10.1109/TPDS.2025.3626047","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3626047","url":null,"abstract":"The persistence diagram, which describes the topological features of a dataset, is a key descriptor in Topological Data Analysis. The <i>“Discrete Morse Sandwich”</i> (DMS) method has been reported to be the most efficient algorithm for computing persistence diagrams of 3D scalar fields on a single node, using shared-memory parallelism. In this work, we extend DMS to distributed-memory parallelism for the efficient and scalable computation of persistence diagrams for massive datasets across multiple compute nodes. On the one hand, we can leverage the embarrassingly parallel procedure of the first and most time-consuming step of DMS (namely the discrete gradient computation). On the other hand, the efficient distributed computations of the subsequent DMS steps are much more challenging. To address this, we have extensively revised the DMS routines by contributing a new self-correcting distributed pairing algorithm, redesigning key data structures and introducing computation tokens to coordinate distributed computations. We have also introduced a dedicated communication thread to overlap communication and computation. Detailed performance analyses show the scalability of our hybrid MPI+thread approach for strong and weak scaling using up to 16 nodes of 32 cores (512 cores total). Our algorithm outperforms <i>DIPHA</i>, a reference method for the distributed computation of persistence diagrams, with an average speedup of <inline-formula><tex-math>$times 8$</tex-math></inline-formula> on 512 cores. We show the practical capabilities of our approach by computing the persistence diagram of a public 3D scalar field of 6 billion vertices in 174 seconds on 512 cores. Finally, we provide a usage example of our open-source implementation at <uri>https://github.com/eve-le-guillou/DDMS-example</uri>.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"37 1","pages":"137-154"},"PeriodicalIF":6.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Richie: A Framework for Agile Design and Exploration of RISC-V-Based Accelerator-Rich Heterogeneous SoCs","authors":"Gianluca Bellocchi;Alessandro Capotondi;Luca Benini;Andrea Marongiu","doi":"10.1109/TPDS.2025.3624958","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3624958","url":null,"abstract":"Modern Heterogeneous Systems-on-Chip (HeSoCs) rely on the accelerator-rich paradigm to achieve performance and energy efficiency through the on-chip integration of many application-specific functional units. However, the lack of a standard System-Level Design (SLD) methodology and the heterogeneity of the HW/SW components complicate the costly and time-consuming process of deploying accelerator-rich systems and applications. In this work, we present <sc>Richie</small>, an open-source research SLD framework featuring a modular and composable RISC-V-based accelerator-rich platform and a support toolchain to automate the assembly and specialization of accelerator-rich HeSoCs. <sc>Richie</small> exploits Field Programmable Gate Arrays (FPGAs) to deploy full-stack applications and explore the HeSoC design space. We show how <sc>Richie</small> facilitates the investigation of platform non-idealities as the system scales up in accelerator count, identifying key design solutions and exploring platform costs, such as area usage. This yields comparable trade-off improvements over manually-optimized designs. Finally, we assess the methodology ease-of-use and extensibility by deploying a real-world workload and adding support for a Network-on-Chip (NoC) architecture.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"37 2","pages":"533-547"},"PeriodicalIF":6.0,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CGA: Accelerating BFS Through an Sparsity-Aware Adaptive Framework on Heterogeneous Platforms","authors":"Lei Xu;Haipeng Jia;Yunquan Zhang","doi":"10.1109/TPDS.2025.3624289","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3624289","url":null,"abstract":"Direction optimization determines whether to use Sparse Matrix-Sparse Vector Multiplication (SpMSpV) or Sparse Matrix-Dense Vector Multiplication (SpMV) based on the input vector’s sparsity at each iteration of Breadth-First Search (BFS), aiming to achieve the fastest graph traversal. Although prior work on direction optimization has achieved state-of-the-art performance on either CPUs or GPUs, it has not fully leveraged the capabilities of modern heterogeneous platforms. This is because SpMSpV/SpMV execution times on GPUs do not consistently outperform those on CPUs, particularly for SpMSpV. In response, this paper introduces <bold><u>CGA</u></b>, a machine learning-based adaptive framework for BFS that optimally selects between <bold><u>C</u></b>PU and <bold><u>G</u></b>PU kernels, effectively <bold><u>A</u></b>dapting to diverse real-world graphs, vectors, and computing platforms. Our contributions include a novel set of bucket-based SpMSpV algorithms that significantly enhance kernel performance in high-sparsity scenarios, along with a low-overhead decision tree model and reduced CPU-GPU data transfers. Experimental results show that our framework outperforms previous state-of-the-art methods, achieving up to a 4.91x speedup over CPU-only baseline and 3.27x speedup over GPU-only baseline.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"37 1","pages":"45-59"},"PeriodicalIF":6.0,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PHIDE: A Parallel Hybrid Direct–Iterative Eigensolver for Hermitian Eigenvalue Problems","authors":"Shengguo Li;Xinzhe Wu;Jose E. Roman;Ziyang Yuan;Ruibo Wang;Tiejun Li;Yi Xie;Bo Yang;Xuguang Chen","doi":"10.1109/TPDS.2025.3623188","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3623188","url":null,"abstract":"In this paper, we propose a Parallel Hybrid Direct–Iterative Eigensolver for Hermitian Eigenvalue Problems without tridiagonalization, denoted by <monospace>PHIDE</monospace>, which combines direct and iterative methods. <monospace>PHIDE</monospace> first reduces a Hermitian matrix to banded form, then applies a spectrum slicing algorithm to the banded matrix, and finally computes the eigenvectors of the original matrix via backtransformation. Compared with conventional direct eigensolvers, <monospace>PHIDE</monospace> avoids tridiagonalization, which involves many memory-bound operations. In <monospace>PHIDE</monospace>, the banded eigenvalue problem is solved using the contour integral method implemented in FEAST, which may yield slightly lower accuracy than tridiagonalization-based approaches. For sequences of correlated Hermitian eigenvalue problems arising in density functional theory (DFT), <monospace>PHIDE</monospace> achieves an average speedup of <inline-formula><tex-math>$1.22times$</tex-math></inline-formula> over the state-of-the-art direct solver in ELPA when using 1024 processes. Numerical experiments are conducted on dense Hermitian matrices from real applications as well as large sparse matrices from the SuiteSparse and ELSES collections.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"37 1","pages":"260-271"},"PeriodicalIF":6.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating Point Cloud Sampling by Parallel Structure Deconstruction","authors":"Hengzhe Chi;Jihe Wang;Jinzhe Zhang;Danghui Wang","doi":"10.1109/TPDS.2025.3622691","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3622691","url":null,"abstract":"Point cloud processing is fundamental to applications such as autonomous driving, robotic navigation, and 3D reconstruction. Sampling is a crucial process in point cloud processing, but current sampling algorithms perform poorly when deployed on edge devices due to limited computational resources. This stems from three main reasons. First, the robustness of sampling is weak; even after denoising, residual noise remains, and existing edge sampling methods struggle to ignore noisy sampling nodes. Second, there’s a lack of structural sampling, popular algorithms like FPS tend to cover global information without considering special structures for focused sampling. Third, system efficiency is poor, traditional Farthest Point Sampling (FPS) and k-Nearest Neighbor Search (kNN) have terrible time and space complexities of <inline-formula><tex-math>$O(n^{2})$</tex-math></inline-formula> with low parallelism, limiting system throughput, especially for large-scale point cloud processing. To bridge this gap, we propose a novel point cloud sampling algorithm based on parallel graph deconstruction named DPS (Deconstruction-based Point Cloud Sampling) and its multi-scale version DPS_MS with better denoising effect. First, during graph construction, we employ multi-scale graphing and add a preprocessing stage to filter out some noise nodes, reducing the noise rate. Second, in the sampling stage, we categorize important structures into edge nodes and dense nodes based on the reverse neighbor count of graph nodes, assigning them higher sampling weights to supplement structural information. Finally, we use a highly parallel locality-sensitive hashing algorithm to accelerate neighbor search and reduce memory consumption, we achieved data-level parallelization through the comprehensive parallelization of our algorithm. Through rigorous qualitative and quantitative validation on classification and segmentation tasks, we demonstrate that DPS, while maintaining accuracy, increases point cloud sampling speed by 22.08 times compared to the FPS algorithm, the implementation achieved a 75.1-fold improvement in system throughput and a maximum parallel speedup of 10.32x, and improves the Accuracy/FLOPs (M) ratio to 8.68.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"37 1","pages":"60-75"},"PeriodicalIF":6.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guest Editorial: New Tools and Techniques for the Distributed Computing Continuum","authors":"Jesus Carretero;Javier García-Blas;Sameer Shende","doi":"10.1109/TPDS.2025.3612151","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3612151","url":null,"abstract":"","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 12","pages":"2451-2454"},"PeriodicalIF":6.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11205817","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-Scale Neural Network Quantum States Calculation for Quantum Chemistry on a New Sunway Supercomputer","authors":"Yangjun Wu;Wenhao Zhou;Li Shen;Hong Qian;Honghui Shang","doi":"10.1109/TPDS.2025.3620251","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3620251","url":null,"abstract":"Quantum many-body system can be solved with neural-network method. Nonetheless, the practical deployment of neural network quantum states (NNQS) in large-scale electronic structure analyses faces challenges, chiefly the high sampling cost and the complexity of local energy computations. To overcome these computational barriers, we present an innovative data-parallel NNQS-Transformer implementation. This implementation introduces a hybrid multi-layer workload balancing strategy that effectively addresses previous load imbalance issues while leveraging Julia’s portability to achieve targeted performance optimizations. Through extensive testing, we validate our approach using comprehensive quantum chemistry calculations on systems containing up to 120 spin orbitals, where previous methods were limited to much smaller scales. The implementation demonstrates exceptional scalability on the Sunway platform, achieving 92% strong scaling and 98% weak scaling efficiencies when utilizing up to 37 million processor cores. These significant performance improvements mark a crucial step toward making NNQS calculations practical for real-world quantum chemistry applications.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 12","pages":"2724-2732"},"PeriodicalIF":6.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autonomous Model Aggregation for Decentralized Learning on Edge Devices","authors":"Jinru Chen;Jingke Tu;Lei Yang;Jiannong Cao","doi":"10.1109/TPDS.2025.3621058","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3621058","url":null,"abstract":"Edge AI applications enable edge devices to collaboratively learn a model via repeated model aggregations, aiming to utilize the distributed data on the devices for achieving high model accuracy. Existing methods either leverage a centralized server to directly aggregate the model updates from edge devices or need a central coordinator to group the edge devices for localized model aggregations. The centralized server (or coordinator) has a performance bottleneck and a high cost of collecting the global state needed for making the grouping decision in large-scale networks. In this paper, we propose an Autonomous Model Aggregation (AMA) method for large-scale decentralized learning on edge devices. Instead of needing a central coordinator to group the edge devices, AMA allows the edge devices to autonomously form groups using a highly efficient protocol, according to model functional similarity and historical grouping information. Moreover, AMA adopts a reinforcement learning approach to optimize the size of each group. Evaluation results on our self-developed edge computing testbed demonstrate that AMA outperforms the benchmark approaches by up to 20.71% in accuracy and reduced the convergence time by 75.58%.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"37 1","pages":"15-28"},"PeriodicalIF":6.0,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FEditor: Consecutive Task Placement With Adjustable Shapes Using FPGA State Frames","authors":"Yanyan Li;Yu Chen;Zhiqian Xu;Yawen Wang;Hai Jiang;Keqin Li","doi":"10.1109/TPDS.2025.3620384","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3620384","url":null,"abstract":"Field Programmable Gate Arrays (FPGAs) are widely adopted in datacenters, where each FPGA is exclusively assigned to a task. This strategy results in significant resource waste and increased task rejections. To address this issue, placement algorithms adjust the locations and shapes of tasks based on Dynamic Partial Reconfiguration, which partitions an FPGA into multiple rectangular areas for sharing. However, existing schemes are designed for static task sets without adjustable shapes, incapable of optimizing the placement problem in datacenters. In this paper, FEditor is proposed as the first consecutive task placement scheme with adjustable shapes. It expands the planar FPGA models into three-dimensional ones with timestamps to accommodate consecutive tasks. To reduce the complexity of three-dimensional resource management, <i>State Frames</i> (<i>SFs</i>) are designed to compress the models losslessly. Three metrics and a nested heuristic algorithm are used for task placement. Experimental results demonstrate that FEditor has improved resource utilization by at least 19.8% and acceptance rate by at least 10% compared to the referenced algorithms. <i>SFs</i> and the nested algorithm accelerate the task placement by up to <inline-formula><tex-math>$10.26times$</tex-math></inline-formula>. The suitability of FEditor in datacenter environments is verified by its time efficiency trends.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"37 1","pages":"1-14"},"PeriodicalIF":6.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}