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A parallel Novelty Search Metaheuristic Applied to a Wildfire Prediction System 36

A Parallel Novelty Search Metaheuristic Applied to a Wildfir...

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Strappa, Jan Caymes-Scutari, Paola Bianchini, German Consejo Nacl Invest Cient & Tecn CONICET Mendoza Argentina Univ Tecnol Nacl Fac Reg Mendoza Buenos Aires DF Argentina

ISBN: (纸本)9781665497473

Wildfires are a highly prevalent multi-causal environmental phenomenon. The impact of this phenomenon includes human losses, environmental damage and high economic costs. To mitigate these effects, several computer simulation systems have been developed in order to predict fire behavior based on a set of input parameters, also called a scenario (wind speed and direction;temperature;etc.). However, the results of a simulation usually have a high degree of error due to the uncertainty in the values of some variables, because they are not known, or because their measurement may be imprecise, erroneous, or impossible to perform in real time. Previous works have proposed the combination of multiple results in order to reduce this uncertainty. State-of-the-art methods are based on parallel optimization strategies that use a fitness function to guide the search among all possible scenarios. Although these methods have shown improvements in the quality of predictions, they have some limitations related to the algorithms used for the selection of scenarios. To overcome these limitations, in this work we propose to apply the Novelty Search paradigm, which replaces the objective function by a measure of the novelty of the solutions found, which allows the search to continuously generate solutions with behaviors that differ from one another. This approach avoids local optima and may be able to find useful solutions that would be difficult or impossible to find by other algorithms. As with existing methods, this proposal may also be adapted to other propagation models (floods, avalanches or landslides).

关键词： wildfire propagation prediction evolutionary algorithms novelty search uncertainty reduction

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Accuracy vs. Cost in parallel Fixed-Precision Low-Rank Approximations of Sparse Matrices 36

Accuracy vs. Cost in Parallel Fixed-Precision Low-Rank Appro...

引用

36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Ernstbrunner, Robert Mayer, Viktoria Gansterer, Wilfried Univ Vienna Fac Comp Sci Vienna Austria

ISBN: (纸本)9781665481069

We study a randomized and a deterministic algorithm for the fixed-precision low-rank approximation problem of large sparse matrices. The Randomized QB Factorization (RandQB_EI) constructs a reduced and dense representation of the originally sparse matrix based on randomization. The representation resulting from the deterministic Truncated LU Factorization with Column and Row Tournament Pivoting (LU_CRTP) is sparse, but fill-in introduced in the factorization process can affect sparsity and performance. We therefore attempt to mitigate fill-in with an incomplete LU_CRTP variant with thresholding (ILUT_CRTP). We analyze this approach and identify potential problems that may arise in practice. We design parallel implementations of RandQB_EI, LU_CRTP and ILUT_CRTP. We experimentally evaluate strong scaling properties for different problems and the runtime required for achieving a given approximation quality. Our results show that LU_CRTP tends to be particularly competitive for low approximation quality. However, when a lot of fill-in occurs, LU_CRTP is outperformed by RandQB_EI especially for higher approximation quality. ILUT_CRTP outperforms both LU_CRTP and RandQB_EI and can achieve speedups up to 40 over LU_CRTP, depending on the amount of fill-in.

关键词： Randomized low-rank approximations Deterministic low-rank approximations Randomized QB Factorization Truncated LU Factorization with Tournament Pivoting

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Shared -Memory parallel Edmonds Blossom Algorithm for Maximum Cardinality Matching in General Graphs

Shared -Memory Parallel Edmonds Blossom Algorithm for Maximu...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Schwing, Gregory Grosu, Daniel Schwiebert, Loren Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)9798350364613;9798350364606

The Edmonds Blossom algorithm is implemented here using depth-first search, which is intrinsically serial. By streamlining the code, our serial implementation is consistently three to five times faster than the previously fastest general graph matching code. By extracting parallelism across iterations of the algorithm, with coarse -grain locking, we are able to further reduce the run lime on random regular graphs fourfold and obtain a two-fold reduction of run time on real-world graphs with similar topology. Solving very sparse graphs (average degree less than four) exhibiting comnwnity structure with eight threads led to a slow down of three-fold, but this slow down is replaced by marginal speed up once the average degree is greater than four. We conclude that our parallel coarse -grain locking implementation performs well when extracting parallelism from this augmenting-path-based algorithm and may work well for similar algorithms.

关键词： matching general graphs Edmonds Blossom parallel

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Degree-Aware Kernels for Computing Jaccard Weights on GPUs 36

Degree-Aware Kernels for Computing Jaccard Weights on GPUs

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Aljundi, Amro Alabsi Akyildiz, Taha Atahan Kaya, Kamer Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey

ISBN: (纸本)9781665481069

Graphs provide the ability to extract valuable metrics from the structural properties of the underlying data they represent. One such metric is the Jaccard Weight of an edge, which is the ratio of the number of common neighbors of the edge's endpoints to the union of the endpoints' neighborhood. A naive implementation of Jaccard Weights computation has a complexity that scales with the number of edges in the graph times the square of the maximum degree. Recently, GPU-based parallel algorithms have been proposed for this problem. However, these algorithms cannot overcome the structural variance within a graph, i.e., the sparsity pattern and degree imbalance, which directly translates to unbalanced work distribution across threads. In this work, we propose an optimized GPU-based algorithm with an ML-based work distribution model that mitigates the unbalanced work distribution. Our algorithm is shown to be up to 35x and on average 12x faster than the state of the art in practice while using less memory. In fact, we show that by manually tweaking the load distribution, a state-of-the-art implementation can be 5x faster. In addition, we propose a multi-core, shared-memory algorithm that applies a traditional but effective technique to improve the computation asymptotically and perform comparably to the GPU algorithms. Our code is available at https://***/SU- HPC/Jaccard-ML

关键词： Jaccard Weights parallel graph algorithms GPU work distribution

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A Massive Open Online Course (MOOC) on High-Performance parallel Computing for *** Web-learning Platform 33

A Massive Open Online Course (MOOC) on High-Performance Para...

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33rd Euromicro international Conference on parallel, distributed, and Network-Based processing, PDP 2025

作者： Laccetti, Giuliano Lapegna, Marco Merciai, Ilaria Univ. of Naples Federico II dept. of Mathematics and Applications Naples Italy Univ. of Naples Fedrico II Federica Web Learning Naples Italy

ISBN: (纸本)9798331524937

Massive Open Online Courses (MOOCs) represent an accessible and user-friendly tool for disseminating innovative and cutting-edge topics to broad segments of civil society via online learning platforms, enabling users to learn at their own pace and on their own schedule. In this contribution, we describe the design and the implementation of a Massive Open Online Course on parallel Computing and High-Performance Computing, developed for Federica Web Learning: the University Center for innovation, experimentation, and dissemination of multimedia teaching at the University of Naples Federico II. © 2025 ieee.

关键词： Curricula

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Semi-supervised Learning in distributed Split Learning Architecture and IoT applications 15

Semi-supervised Learning in Distributed Split Learning Archi...

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ieee 15th international symposium on Autonomous Decentralized Systems (ISADS)

作者： Barhoush, Mahdi Ayad, Ahmad Schmeink, Anke Rhein Westfal TH Aachen Chair Informat Theory & Data Analyt Aachen Germany

ISBN: (纸本)9781665464512

In the era of big data, new learning techniques are emerging to solve the difficulties of data collection, storage, scalability, and privacy. To overcome these challenges, we propose a distributed learning system that merges the hybrid edge-cloud split-learning architecture with the semi-supervised learning scheme. The proposed system based on three semi-supervised learning algorithms (FixMatch, Virtual Adversarial Training, and MeanTeacher) is compared to the supervised learning scheme and trained on different datasets and data distributions (IID and non-IID) and with a variable number of clients. The new system could efficiently utilize the local unlabeled samples on the client side and gave a performance encouragement that exceeds 30% in most cases even with small percentage of labelled data. Additionally, certain Split-SSL algorithms showed performance that was on par with or occasionally even better than more resource-intensive algorithms, although requiring less processing power and convergence time.

关键词： Algorithm Comparison Big Data distributed Learning Semi-supervised learning Split Learning

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PINT: parallel INTerval-Based Race Detector 36

PINT: Parallel INTerval-Based Race Detector

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Xu, Yifan Zhou, Anchengcheng Agrawal, Kunal Lee, I-Ting Angelina Washington Univ St Louis MO 63110 USA

ISBN: (纸本)9781665481069

A race detector for task-parallel code typically consists of two main components- a reachability analysis component that checks whether two instructions are logically in parallel and an access history component that keeps track of memory locations accessed by previous instructions. Race detectors from prior work typically utilize a hashmap to maintain the access history, which provides asymptotically optimal overhead per operation but can incur significant overhead in practice, since the detector needs to insert into and query the hashmap for every memory access. An exception is STINT by Xu et al., which race detects task-parallel code by coalescing memory accesses into intervals, or continuous memory locations accessed within a sequence of instructions without any parallel construct. STINT utilizes a treap to manage access history that allows for insertions and queries of non-overlapping intervals. While a treap incurs higher asymptotic overhead per operation, this strategy works well in practice as the race detector performs operation on the access history with much lower frequency compared to the strategy that utilizes a hashmap. STINT only executes task-parallel code sequentially, however, due to the unique design of their treap that ensures no overlapping intervals exist in the tree. parallelizing STINT efficiently is non-trivial, as it would require a concurrent treap that ensures no overlapping interval, which is challenging to design and likely incurs high synchronization overhead. This work proposes PINT, a race detector that, like STINT, race detects task-parallel code at the interval granularity and utilizes the same treap design to maintain access history. PINT executes the computation in parallel, however, while keeping the parallelization / synchronization overhead low. A key insight is that, PINT separates out operations needed for race detection into the core part (e.g., reachability maintenance) and the access history part. Doing so allows PINT to p

关键词： distributed processing Codes Detectors Maintenance engineering History Synchronization Task analysis

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A parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery

A Parallel Workflow for Polar Sea-Ice Classification using A...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Iqrah, Jurdana Masuma Wang, Wei Xie, Hongjie Prasad, Sushil K. Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA Univ Texas San Antonio Dept Earth & Planetary Sci San Antonio TX USA

ISBN: (纸本)9798350364613;9798350364606

The observation of the advancing and retreating pattern of polar sea ice cover stands as a vital indicator of global warming. This research aims to develop a robust, effective, and scalable system for classifying polar sea ice as thick/snow-covered, young/thin, or open water using Sentinel-2 (S2) images. Since the 52 satellite is actively capturing high-resolution imagery over the earth's surface, there are lots of images that need to be classified. One major obstacle is the absence of labeled 52 training data (images) to act as the ground truth. We demonstrate a scalable and accurate method for segmenting and automatically labeling S2 images using carefully determined color thresholds. We employ a parallel workflow using PySpark to scale and achieve 9-fold data loading and 16-fold map-reduce speedup on auto-labeling S2 images based on thin cloud and shadow filtered color-based segmentation to generate label data. The auto-labeled data generated from this process are then employed to train a U-Net machine learning model, resulting in good classification accuracy. As training the U-Net classification model is computationally heavy and time-consuming, we distribute the U-Net model training to scale it over 8 GPLJs using the Horovod framework over a DGX cluster with a 7.2 lx speedup without affecting the accuracy of the model. Using the Antarctic's Ross Sea region as an example, the U-Net model trained on autolabeled data achieves a classification accuracy of 98.97% for auto-labeled training datasets when the thin clouds and shadows from the S2 images are filtered out.

关键词： Polar Sea Ice Sentinel-2 Sea Ice Classification Auto-labeling parallel processing distributed Deep Learning Synchronous Data parallel

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The Universal Gossip Fighter 36

The Universal Gossip Fighter

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Gorbunova, Anastasiia Guerraoui, Rachid Kermarrec, Anne-Marie Kucherenko, Anastasiia Pinot, Rafael Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781665481069

The notion of adversary is a staple of distributed computing. An adversary typically models "hostile" assumptions about the underlying distributed environment, e.g., a network that can drop messages, an operating system that can delay processes or an attacker that can hack machines. So far, the goal of distributed computing researchers has mainly been to develop a distributed algorithm that can face a given adversary, the abstraction characterizing worst-case scenarios. This paper initiates the study of the somehow opposite approach. Given a distributed algorithm, the adversary is the abstraction we seek to implement. More specifically, we consider the problem of controlling the spread of messages in a large-scale system, conveying the practical motivation of limiting the dissemination of fake news or viruses. Essentially, we assume a general class of gossip protocols, called all-to-all gossip protocols, and devise a practical method to hinder the dissemination. We present the Universal Gossip Fighter (UGF). Just like classical adversaries in distributed computing, UGF can observe the status of a dissemination and decide to stop some processes or delay some messages. The originality of UGF lies in the fact that it is universal, i.e., it applies to any all-to-all gossip protocol. We show that any gossip protocol attacked by UGF ends up exhibiting a quadratic message complexity (in the total number of processes) if it achieves sublinear time of dissemination. We also show that if a gossip protocol aims to achieve a message complexity ff times smaller than quadratic, then the time complexity rises exponentially in relation to ff. We convey the practical relevance of our theoretical findings by implementing UGF and conducting a set of empirical experiments that confirm some of our results.

关键词： distributed Computing Gossip Protocols Adaptive Adversaries

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A Deep Dive into Task-Based parallelism in Python

A Deep Dive into Task-Based Parallelism in Python

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Ruys, William Lee, Hochan You, Bozhi Talati, Shreya Park, Jaeyoung Almgren-Bell, James Yan, Yineng Fernando, Milinda Biros, George Erez, Mattan Burtscher, Martin Rossbach, Christopher J. Pingali, Keshav Gligoric, Milos Univ Texas Austin Austin TX 78712 USA Texas State Univ San Marcos TX USA

ISBN: (纸本)9798350364613;9798350364606

Modern Python programs in high-performance computing call into compiled libraries and kernels for performance critical tasks. However, effectively parallelizing these finer-grained, and often dynamic, kernels across modern heterogeneous platforms remains a challenge. First, we perform an experimental study to examine the impact of Python's Global Interpreter Lock (GIL), and potential speedups under a GIL-less PEP703 future, to guide runtime design. Using our optimized runtime, we explore scheduling tasks with constraints that require resources across multiple, potentially diverse, devices through the introduction of new programming abstractions and runtime mechanisms. We extend an existing Python tasking library, Parla, to augment its performance and add support for such multi -device tasks. Our experimental analysis, using tasks graphs from synthetic and real applications, shows at least a 3x (and up to 6x) performance improvement over its predecessor in scenarios with high GIL contention. When scheduling multi-GPU tasks, we observe an Ax reduction in per-task launching overhead compared to a multi-process system.

关键词： task runtime parallel programming Python heterogeneous computing

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