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NEO-DNND: Communication-Optimized Distributed Nearest Neighbor Graph Construction

NEO-DNND: Communication-Optimized Distributed Nearest Neighb...

2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Iwabuchi, Keita Steil, Trevor Priest, Benjamin W. Pearce, Roger Sanders, Geoffrey Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350355543

Graph-based approximate nearest neighbor algorithms have shown high neighbor structure representation quality. NN-Descent is a widely known graph-based approximate nearest neighbor (ANN) algorithm. However, graph-based approaches are memory- and *** address the drawbacks, we develop a scalable distributed NN-Descent. Our NEO-DNND (neighbor-checking efficiency optimized distributed NN-Descent) is built on top of MPI and designed to utilize network bandwidth efficiently. NEO-DNND reduces duplicate elements, increases intra-node data sharing, and leverages available DRAM to replicate data that may be sent ***-DNND showed remarkable scalability up to 256 nodes and was able to construct neighborhood graphs from billion-scale datasets. Compared to a leading shared-memory ANN library, NEO-DNND achieved competitive performance even on a single node and exhibited 41.7X better performance by scaling up to 32 nodes. Furthermore, NEO-DNND outperformed a state-of-the-art distributed NN-Descent implementation, achieving up to a 6.0X speedup. © 2024 IEEE.

关键词： approximate nearest neighbor distributed computing

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Disentangling Disparate Communication Streams and Recovering Underlying Structure 11

Disentangling Disparate Communication Streams and Recovering...

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11th IEEE International Conference on Social Networks Analysis, Management and Security, SNAMS 2024

作者： Nathan, Eisha Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States

ISBN: (纸本)9798331518349

In this paper we present various approaches for the tasks of disentanglement (or automatic forum identification) and structure recovery (or prediction of message replies) in online discussion forums such as Reddit and Stack Exchange. For the disentanglement task we present a fine-Tuned BERT model. By leveraging transfer learning from a pre-Trained BERT (Bidi-rectional Encoder Representations from Transformers) model, given the message content, our approach achieves accuracies of 83-92% in predicting where a message originated. For the task of structure recovery, we propose a novel neural network architecture that leverages deep learning techniques to model the sequential relationship between messages to predict if one message follows another with accuracies over 80%. Additionally we present few-shot learning strategies for both tasks of disentanglement and structure recovery. Given the sparse availability of limited samples for training in real data, few-shot learning, or the ability to train without needed large amounts of labeled data, is necessary. Experimental results demonstrate the effectiveness of our proposed architecture and methods in capturing contextual dependencies and improving prediction accuracy in such diverse online forums for both tasks and our few-shot architectures achieve comparable accuracies to the case when large training data is available. © 2024 IEEE.

关键词： Zero-shot learning

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Linearly Solvable General-Sum Markov Games 60

Linearly Solvable General-Sum Markov Games

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60th Annual Allerton Conference on Communication, Control, and computing, Allerton 2024

作者： Soper, Braden C. Miller, Caleb J. Merl, Dan M. Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States

ISBN: (纸本)9798331541033

Linearly solvable Markov decision processes (LSMDPs) are a special class of Markov decision processes (MDPs) in which the optimal value function under an exponential transformation satisfies a linear equation. This model was previously extended to a class of two-player, zero-sum Markov games in which the game's equilibrium value function can similarly be derived from a linear equation. In this work, a new class of linearly solvable n-player, general-sum Markov games is proposed. We show that games in this class have a unique Nash equilibrium, and the equilibrium value functions can be derived from a single system of linear equations. We demonstrate how to approximate discrete-state, discrete-action Markov games outside this class through an embedding process analogous to the way LSMDPs approximate standard MDPs. An off-policy reinforcement learning algorithm for general-sum Markov games, which we call Nash-Z learning, is presented. We empirically demonstrate that Nash-Z learning finds equilibrium policies with low regret and that it finds these solutions orders of magnitude faster than the classic Nash-Q learning algorithm. © 2024 IEEE.

关键词： Nash equilibrium

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Explainable machine learning in materials science

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npj Computational Materials 2022年第1期8卷 1951-1969页

作者： Xiaoting Zhong Brian Gallagher Shusen Liu Bhavya Kailkhura Anna Hiszpanski T.Yong-Jin Han Materials Science Division Lawrence Livermore National LaboratoryLivermoreCAUSA Center for Applied Scientific Computing Lawrence Livermore National LaboratoryLivermoreCAUSA

Machine learning models are increasingly used in materials studies because of their exceptional ***,the most accurate machine learning models are usually difficult to *** to this problem lie in explainable artificial intelligence(XAI),an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks(DNNs).This article attempts to provide an entry point to XAI for materials *** are defined to clarify what explain means in the context of materials *** works are reviewed to show how XAI helps materials science *** and opportunities are also discussed.

关键词： explain learning plain

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Exploring Multiscale Navigation of Homogeneous and Dense Objects with Progressive Refinement in Virtual Reality 32

Exploring Multiscale Navigation of Homogeneous and Dense Obj...

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32nd IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2025

作者： Pavanatto, Leonardo Giovannelli, Alexander Giera, Brian Bremer, Timo Miao, Haichao Bowman, Doug A. Virginia Tech Center for Human-Computer Interaction Department of Computer Science United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798331536459

Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding. © 2025 IEEE.

关键词： Virtual reality

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Testing the Unknown: A Framework for OpenMP Testing via Random Program Generation

Testing the Unknown: A Framework for OpenMP Testing via Rand...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Laguna, Ignacio Chapman, Patrick Parasyris, Konstantinos Georgakoudis, Giorgis Rubio-Gonzalez, Cindy Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States University of California Department of Computer Science Davis United States

ISBN: (纸本)9798350355543

We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly generate thousands of tests, exposing OpenMP implementations to a wide range of program behaviors. We represent the space of possible random OpenMP tests using a grammar and implement our method as an extension of the Varity program generator. By generating 1,800 OpenMP tests, we find various performance anomalies and correctness issues when we apply them to three OpenMP implementations: GCC, Clang, and Intel. We also present several case studies that analyze the anomalies and give more details about the classes of tests that our approach creates. © 2024 IEEE.

关键词： differential testing OpenMP random program generation software testing

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On the Scalability of computing Genomic Diversity Using SparkLeBLAST: A Feasibility Study

On the Scalability of Computing Genomic Diversity Using Spar...

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2024 IEEE High Performance Extreme computing Conference, HPEC 2024

作者： Prabhu, Ritvik Moussad, Bernard Youssef, Karim Vatai, Emil Feng, Wu-Chun Virginia Tech Department of Computer Science United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Riken Center for Computational Science Japan

ISBN: (纸本)9798350387131

Studying the genomic diversity of viruses can help us understand how viruses evolve and how that evolution can impact human health. Rather than use a laborious and tedious wet-lab approach to conduct a genomic diversity study, we take a computational approach, using the ubiquitous NCBI BLAST and our parallel and distributed SparkLeBLAST, across 53 patients (40,000,000 query sequences) on Fugaku, the world's fastest homogeneous supercomputer with 158,976 nodes, where each code contains a 48-core A64FX processor and 32 GB RAM. To project how long BLAST and SparkLeBLAST would take to complete a genomic diversity study of COVID-19, we first perform a feasibility study on a subset of 50 query sequences from a single COVID-19 patient to identify bottlenecks in sequence alignment processing. We then create a model using Amdahl's law to project the run times of NCBI BLAST and SparkLeBLAST on supercomputing systems like Fugaku. Based on the data from this 50-sequence feasibility study, our model predicts that NCBI BLAST, when running on all the cores of the Fugaku supercomputer, would take approximately 26.7 years to complete the full-scale study. In contrast, SparkLeBLAST, using both our query and database segmentation, would reduce the execution time to 0.026 years (i.e., 22.9 hours) - resulting in more than a 10,000× speedup over using the ubiquitous NCBI BLAST. © 2024 IEEE.

关键词： COVID-19

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NEO-DNND: Communication-Optimized Distributed Nearest Neighbor Graph Construction 24

NEO-DNND: Communication-Optimized Distributed Nearest Neighb...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Keita Iwabuchi Trevor Steil Benjamin W. Priest Roger Pearce Geoffrey Sanders Center for Applied Scientific Computing Lawrence Livermore National Laboratory

ISBN: (纸本)9798350355543

关键词： approximate nearest neighbor

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A CLOSER LOOK AT MODEL ADAPTATION USING FEATURE DISTORTION AND SIMPLICITY BIAS 11

A CLOSER LOOK AT MODEL ADAPTATION USING FEATURE DISTORTION A...

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11th International Conference on Learning Representations, ICLR 2023

作者： Trivedi, Puja Koutra, Danai Thiagarajan, Jayaraman J. CSE Department University of Michigan United States Center of Applied Scientific Computing Lawrence Livermore Natl.Laboratory United States

Advances in the expressivity of pretrained models have increased interest in the design of adaptation protocols which enable safe and effective transfer *** beyond conventional linear probing (LP) and fine tuning (FT) strategies, protocols that can effectively control feature distortion, i.e., the failure to update features orthogonal to the in-distribution, have been found to achieve improved out-of-distribution generalization (OOD).In order to limit this distortion, the LP+FT protocol, which first learns a linear probe and then uses this initialization for subsequent FT, was ***, in this paper, we find when adaptation protocols (LP, FT, LP+FT) are also evaluated on a variety of safety objectives (e.g., calibration, robustness, etc.), a complementary perspective to feature distortion is helpful to explain protocol *** this end, we study the susceptibility of protocols to simplicity bias (SB), *** well-known propensity of deep neural networks to rely upon simple features, as SB has recently been shown to underlie several problems in robust *** a synthetic dataset, we demonstrate the susceptibility of existing protocols to *** the strong effectiveness of LP+FT, we then propose modified linear probes that help mitigate SB, and lead to better initializations for subsequent *** verify the effectiveness of the proposed LP+FT variants for decreasing SB in a controlled setting, and their ability to improve OOD generalization and safety on three adaptation datasets. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Probes

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NEO-DNND: Communication-Optimized Distributed Nearest Neighbor Graph Construction

NEO-DNND: Communication-Optimized Distributed Nearest Neighb...

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High Performance computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Keita Iwabuchi Trevor Steil Benjamin W. Priest Roger Pearce Geoffrey Sanders Center for Applied Scientific Computing Lawrence Livermore National Laboratory

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

关键词： Scalability High performance computing Conferences Random access memory Focusing Artificial neural networks Approximation algorithms Vectors Libraries Optimization

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