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检索条件"任意字段=International Symposium on Databases in Parallel and Distributed Systems"

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A Dynamic distributed Scheduler for Computing on the Edge 1

A Dynamic Distributed Scheduler for Computing on the Edge

引用

1st international symposium on parallel Computing and distributed systems, PCDS 2024

作者： Hu, Fei Mehta, Kunal Mishra, Shivakant Almutawa, Mohammad University of Colorado Dept. of Computer Science United States Kuwait University Dept. of Computer Science Kuwait

ISBN: (纸本)9798350349658

Edge computing is crucial for IoT applications, especially those needing quick, private data handling. However, these applications are resource-intensive, and edge computing resources are limited compared to cloud capabilities. Efficiently using these limited resources is essential to meet all application demands, including latency, privacy, and cost. Due to the dynamic and hybrid nature of IoT environments, static scheduling systems often falls short in meeting these diverse constraints. This paper describes the design, implementation, and evaluation of a dynamic distributed scheduler for the edge. This scheduler schedules the execution of various computing tasks across all computing resources available at the edge in order to satisfy the diverse constraints of an IoT application. The key characteristic of this scheduler is that it constantly monitors the current state of the IoT infrastructure and dynamically adjusts the scheduling of various computing tasks based on the current environmental context as well as the current computing and network conditions. The scheduler utilizes a predictive profiling method to manage the hybrid and variable nature of edge computing resources and tasks. A prototype of this scheduler has been implemented and the paper demonstrates its practicality via an augmented reality application. © 2024 IEEE.

关键词： Augmented Reality Applications De-vice Profile Dynamic distributed Scheduler Edge Computing parallel Com-puting

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DeepThermo: Deep Learning Accelerated parallel Monte Carlo Sampling for Thermodynamics Evaluation of High Entropy Alloys 37

DeepThermo: Deep Learning Accelerated Parallel Monte Carlo S...

引用

37th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Yin, Fungi Wang, Feiyi Shankar, Mallikarjun (Arjun) Oak Ridge Natl Lab Oak Ridge TN 37830 USA

ISBN: (纸本)9798350337662

Since the introduction of Metropolis Monte Carlo (MC) sampling, it and its variants have become standard tools used for thermodynamics evaluations of physical systems. However, a long-standing problem that hinders the effectiveness and efficiency of MC sampling is the lack of a generic method (a.k.a. MC proposal) to update the system configurations. Consequently, current practices are not scalable. Here we propose a parallel MC sampling framework for thermodynamics evaluation-DeepThermo. By using deep learning-based MC proposals that can globally update the system configurations, we show that DeepThermo can effectively evaluate the phase transition behaviors of high entropy alloys, which have an astronomical configuration space. For the first time, we directly evaluate a density of states expanding over a range of similar to e(10,000) for a real material. We also demonstrate DeepThermo's performance and scalability up to 3,000 GPUs on both NVIDIA V100 and AMD MI250X-based supercomputers.

关键词： Monte Carlo Method Deep Learning High Entropy Alloy

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Helios: Efficient distributed Dynamic Graph Sampling for Online GNN Inference 25

Helios: Efficient Distributed Dynamic Graph Sampling for Onl...

引用

30th symposium on Principles and Practice of parallel Programming

作者： Sun, Jie Shi, Zuocheng Su, Li Shen, Wenting Wang, Zeke Li, Yong Yu, Wenyuan Lin, Wei Wu, Fei He, Bingsheng Zhou, Jingren Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Zhejiang Univ Shanghai Inst Adv Study Hangzhou Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798400714436

Online GNN inference has been widely explored by applications such as online recommendation and financial fraud detection systems, where even minor delays can result in significant financial impact. Real-time dynamic graph sampling enables online GNN inference to reflect the latest graph updates in real-world graphs. However, online GNN inference typically demands millisecond-level latency Service Level Objectives (SLOs) as its performance guarantees, which poses great challenges for existing dynamic graph sampling approaches based on graph databases. The issues mainly arise from two aspects: long tail latency due to imbalanced data-dependent sampling and large communication overhead incurred by distributed sampling. To address these issues, we propose Helios, an efficient distributed dynamic graph sampling service to meet the stringent latency SLOs. The key ideas of Helios are 1) pre-sampling the dynamic graph in an event-driven approach, and 2) maintaining a query-aware sample cache to build the complete K-hop sampling results locally for inference requests. Experiments on multiple datasets show that Helios achieves up to 67x higher serving throughput and up to 32x lower P99 query latency compared to baselines.

关键词： Graph Neural Network distributed System Online Inference

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2024 IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2024

2024 IEEE International Parallel and Distributed Processing ...

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2024 IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2024

ISBN: (纸本)9798350364606

The proceedings contain 165 papers. The topics discussed include: understanding multi-dimensional efficiency of fine-tuning large language models using SpeedUp, MemoryUp, and EnergyUp;shared-memory parallel Edmonds blossom algorithm for maximum cardinality matching in general graphs;a reconfigurable architecture of a scalable, ultrafast, ultrasound, delay-and-sum beamformer;scheduling and allocation of disaggregated memory resources in HPC systems;GIM (ghost in the machine): a coarse-grained reconfigurable compute-in-memory platform for exploring machine-learning architectures;further optimizations and analysis of smith-waterman with vector extensions;measurement-based quantum approximate optimization;optimizing forward wavefield storage leveraging high-speed storage media;teaching performance metrics in parallel computing courses;and compiler-driven Swar parallelism for high-performance bitboard algorithms.

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Teaching Performance Metrics in parallel Computing Courses

Teaching Performance Metrics in Parallel Computing Courses

引用

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

作者： Vargas-Perez, Sandino Kalamazoo Coll Dept Comp Sci Kalamazoo MI 49007 USA

ISBN: (纸本)9798350364613;9798350364606

As high-performance computing technologies advance, the significance of parallel programming in various domains is becoming increasingly evident since it allows us to harness the power of heterogeneous computing and solve complex problems more efficiently. However, for students to master this type of computation and be able to apply it in different contexts, it requires understanding how measuring and optimizing parallel code impacts its performance. This paper presents an approach to enhancing students' comprehension of parallel performance metrics through an interactive exercise that complements lectures on parallel performance and improves assessment.

关键词： parallel Computing HPC Undergraduate Education

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A New Exact State Reconstruction Strategy for Conjugate Gradient Methods with Arbitrary Preconditioners

A New Exact State Reconstruction Strategy for Conjugate Grad...

引用

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

作者： Mayer, Viktoria Gansterer, Wilfried N. Univ Vienna Fac Comp Sci UniVie Doctoral Sch Comp Sci DoCS Vienna Austria Univ Vienna Fac Comp Sci Vienna Austria

The likelihood of unanticipated node failures in large-scale parallel computers increases with growing numbers of nodes. Furthermore, global reduction operations become major bottlenecks due to their limited parallel ... 详细信息

ISBN: (纸本)9798350364613;9798350364606

关键词： communication-hiding algorithms pipelined conjugate gradient algorithms extreme-scale parallel computing algorithmic fault tolerance exact state reconstruction

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Codeless PDC Modules for Early Computing Curriculum

Codeless PDC Modules for Early Computing Curriculum

引用

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

作者： Bourke, Chris Firestone, Justin Univ Nebraska Sch Comp Lincoln NE 68588 USA

ISBN: (纸本)9798350364613;9798350364606

PDC at UM, is a series of "codeless" modules consisting of visualizations, simulations, and demonstrations which introduce parallel and distributed Computing (PDC) concepts in early computing courses. These materials are codeless because they do not require students to write or understand code. Instead, students read a short introduction to a PDC concept and Then engage with a web-based visualization and/or (code-based) demonstration reinforcing the concept. The codeless nature of these modules makes them suitable for computing and non computing majors. To test the effectiveness of our modules we introduced them into two CSI courses and designed and administered a pre/posttest. Our results show statistically significant results: those who engaged with our modules substantially improved their knowledge and understanding of PDC concepts. Our modules also improved student attitudes, confidence and self-efficacy with respect to PDC topics. We also provide some qualitative observations of our study and identify common misconceptions students have about PDC.

关键词： parallel computing distributed computing computer science education

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Peachy parallel Assignments (EduPar 2024)

Peachy Parallel Assignments (EduPar 2024)

引用

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

作者： Lazar, Alina Scheelk, Ethan Shoop, Elizabeth Bunde, David P. Youngstown State Univ Youngstown OH 44555 USA Macalester Coll St Paul MN USA Knox Coll Galesburg IL USA

ISBN: (纸本)9798350364613;9798350364606

We present two new assignments in the Peachy parallel Assignments series of assignments for teaching parallel and distributed computing. Submitted assignments must have been successfully used previously and are selected for being easy for other instructors to adopt and for being "cool and inspirational" so that students spend time on them and talk about them with others. The first assignment in this paper familiarizes students with the RAFT library for performing GPU-accelerated computation, pail of the RAPIDS AI ecosystem. Students use this library to accelerate a Radius Nearest Neighbor computation, finding all points within a given distance from a query point. In the second assignment, students parallelize a bird flocking simulation using OpenMP or OpenACC. It is a visual assignment which allows students to readily see the performance improvement.

关键词： Peachy parallel Assignments parallel computing education parallel programming Curriculum development Visualizing parallelism RAPIDS Al ecosystem Radius Nearest Neighbor Boid flocking algorithm

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ArkFS: A distributed File System on Object Storage for Archiving Data in HPC Environment 37

ArkFS: A Distributed File System on Object Storage for Archi...

引用

37th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Cho, Kyu-Jin Kang, Injae Kim, Jin-Soo Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9798350337662

As the burst buffer is being widely deployed in the HPC (High-Performance Computing) systems, the distributed file system layer is taking the role of campaign storage where scalability and cost-effectiveness are of paramount importance. However, the centralized metadata management in the distributed file system layer poses a scalability challenge. The object storage system has emerged as an alternative thanks to its simplified interface and scale-out architecture. Despite this, the HPC communities are used to working with the POSIX interface to organize their files into a global directory hierarchy and control access through access control lists. In this paper, we present ArkFS, a near-POSIX compliant and scalable distributed file system implemented on top of the object storage system. ArkFS achieves high scalability without any centralized metadata servers. Instead, ArkFS lets each client manage a portion of the file system metadata on a perdirectory basis. ArkFS supports any distributed object storage system such as Ceph RADOS or S3-compatible system with an appropriate API translation module. Our experimental results indicate that ArkFS shows significant performance improvement under metadata-intensive workloads while showing near-linear scalability. We also demonstrate that ArkFS is suitable for handling the bursty I/O traffic coming from the burst buffer layer to archive cold data.

关键词： High-performance computing distributed file system Object storage

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Sequre: a high-performance framework for rapid development of secure bioinformatics pipelines 36

Sequre: a high-performance framework for rapid development o...

引用

36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Smajlovic, Haris Shajii, Ariya Berger, Bonnie Cho, Hyunghoon Numanagic, Ibrahim Univ Victoria Victoria BC Canada MIT Cambridge MA 02139 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA

ISBN: (纸本)9781665497473

Genomic data leaks are irreversible. Leaked DNA cannot be changed, stays disclosed indefinitely, and affects the owner's family members as well. The recent large-scale genomic data collections [1], [2] render the traditional privacy protection mechanisms, like the Health Insurance Portability and Accountability Act (HIPAA), inadequate for protection against the novel security attacks [3]. On the other hand, data access restrictions hinder important clinical research that requires large datasets to operate [4]. These concerns can be naturally addressed by the employment of privacy-enhancing technologies, such as a secure multiparty computation (MPC) [5]–[10]. Secure MPC enables computation on data without disclosing the data itself by dividing the data and computation between multiple computing parties in a distributed manner to prevent individual computing parties from accessing raw data. MPC systems are being increasingly adopted in fields that operate on sensitive datasets [11]–[13], such as computational genomics and biomedical research [14]–[22].

关键词： distributed processing Data privacy Pipelines Employment distributed databases Genomics Insurance

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