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CATC: A task scheduling based on computing-aware and task classification in Pervasive Edge computing

CATC: A task scheduling based on computing-aware and task cl...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： JiaHui Yang Yang Zhang ShuKui Zhang Mingyu Zhu YingYing Wang School of Computer Science and Technology Soochow University Suzhou China Global Institute of Software Technology Suzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With no infrastructure, the battlefield environment has characteristics such as device heterogeneity, uneven resource distribution, and dynamic node movement. Additionally, the sensing tasks produced by devices are complex and delay-sensitive, making it challenging to complete the tasks within the time limit if a centralized unified scheduling strategy is employed. In order to fully utilize the resources in the battlefield environment, optimize the load situation in the battlefield scenario, and guarantee the stability of task offloading, this paper proposes a scheduling strategy based on computing-aware and task classification (CATC). We first design a distributed computing-aware mechanism to ensure the detection of surrounding resources and load conditions by the generating task devices. Based on this, we classify the battlefield tasks into multi-attribute tasks according to the actual situation, and develop an offloading strategy for different task types, taking into account computation, storage, transmission, and load situation. The simulation results demonstrate that the proposed CATC strategy has a high task completion rate and can satisfy the definite delay requirements of delay-sensitive tasks.

关键词： Processor scheduling Simulation Dynamic scheduling Stability analysis Delays Sensors Resource management Edge computing

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Time and Space Optimal Massively parallel Algorithm for the 2-Ruling Set Problem 37

Time and Space Optimal Massively Parallel Algorithm for the ...

引用

37th international symposium on distributed computing, DISC 2023

作者： Cambus, Mélanie Kuhn, Fabian Pai, Shreyas Uitto, Jara Aalto University Finland University of Freiburg Germany

ISBN: (纸本)9783959773010

In this work, we present a constant-round algorithm for the 2-ruling set problem in the Congested Clique model. As a direct consequence, we obtain a constant round algorithm in the MPC model with linear space-per-machine and optimal total space. Our results improve on the O(log log log n)-round algorithm by [HPS, DISC'14] and the O(log log ∆)-round algorithm by [GGKMR, PODC'18]. Our techniques can also be applied to the semi-streaming model to obtain an O(1)-pass algorithm. Our main technical contribution is a novel sampling procedure that returns a small subgraph such that almost all nodes in the input graph are adjacent to the sampled subgraph. An MIS on the sampled subgraph provides a 2-ruling set for a large fraction of the input graph. As a technical challenge, we must handle the remaining part of the graph, which might still be relatively large. We overcome this challenge by showing useful structural properties of the remaining graph and show that running our process twice yields a 2-ruling set of the original input graph with high probability. © Mélanie Cambus, Fabian Kuhn, Shreyas Pai, and Jara Uitto;licensed under Creative Commons License CC-BY 4.0.

关键词： parallel algorithms

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An Investigative Study of WebAssembly Performance in Cloud-to-Edge

An Investigative Study of WebAssembly Performance in Cloud-t...

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Sangeeta Kakati Mats Brorsson SnT University of Luxembourg Luxembourg

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Developing an application architecture that can fully utilize the resources available is essential for developers as cloud-to-edge computing keeps evolving. With its ability to operate on multiple architectures without interfering with the development process, WebAssembly has become a highly significant solution. This makes it easy for programmers to spread applications across various computer systems. To make the WebAssembly binaries work, a runtime is required. In this paper, the tests are run using the existing Wasmtime runtime and the SightGlass benchmark suite. We compare the cycle counts for the benchmarks' compilation, instantiation, and execution phases using performance counters during the evaluation stage. These tests have shown that WebAssembly is a highly effective tool that can help developers create applications that are both efficient and effective. Using WebAssembly, developers can create applications that can adapt to changing computing environments, making it possible to take full advantage of cloud-to-edge computing. Our experimental study investigated the performance of the wasmtime runtime on X86, ARM, and RISC-V architectures. Through the evaluation, we gained a valuable analysis of Wasm's performance characteristics. By examining architecture-specific outcomes, we demonstrated its capacity to fulfill the demands of a cross-architecture cloud-to-edge application framework.

关键词： Performance evaluation Runtime Program processors Focusing Data visualization Computer architecture Benchmark testing parallel processing Standards Optimization

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The GraphBLAS 3.0 Project

The GraphBLAS 3.0 Project

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Raye Kimmerer Timothy G. Mattson Scott McMillan Benjamin Brock Erik Welch Michel Pelletier José E. Moreira JuliaLab Massachusetts Institute of Technology Cambridge MA Human Learning Group Ocean Park WA Software Engineering Institute Carnegie Mellon University Parallel Computing Lab Intel Santa Clara CA NVIDIA Corporation Austin TX Graphegon Portland OR IBM Thomas J. Watson Research Center Yorktown Heights NY

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

The GraphBLAS C API is mature with an updated specification (version 2.1) and a compliant implementation (SuiteSparse GraphBLAS). We are now focused on GraphBLAS 3.0; the next major GraphBLAS revision. Potential changes include: (1) a separate math spec to make new language bindings easier to write, (2) better support for user-defined types, rank promotion, and enhanced non-blocking execution, (3) expanded scope of GraphBLAS to address a wider range of applications, and (4) support for complex heterogeneous and distributed systems.

关键词： distributed processing Conferences Oral communication Libraries

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Introducing parallel and distributed computing concepts through the use of Flashcards and a Card Game

Introducing Parallel and Distributed Computing concepts thro...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Mary L. Smith Srishti Srivastava Computer Science Hawaii Pacific University Honolulu HI USA Computer Science University of Southern Indiana Evansville IN USA

This paper describes two active learning strategies to teach and review fundamental PDC concepts in early computer science courses. Questions were created based on eight PDC concept categories. In the first phase, flashcards were created for students to review the concepts. In the second phase, a card game called PDC Quest was created to allow groups of students to engage collaboratively in learning and reviewing the concepts.

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Task Tree Partition and Subtree Allocation for Heterogeneous Multiprocessors 19

Task Tree Partition and Subtree Allocation for Heterogeneous...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： He, Suna Wu, Jigang Wei, Bing Wu, Jiaxin Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China

ISBN: (纸本)9781665435741

Tree-shaped task graphs become a paradigm to be utilized in distributed platform for various computational domains, such as the electronic structure calculations and the factorization of sparse matrices. However, the scheduling of the tree-shaped task graph has been rarely studied for the more realistic heterogeneous multiprocessor platform (HEMP). This paper proposes an efficient algorithm named Partition-Allocation (PA) for parallel computing on HEMP with limited memory. Algorithm PA consists of two stages: partitioning and allocation. In the partitioning stage, a task tree is split into several subtrees. In the allocation stage, these subtrees are assigned to different processors for execution. Our algorithm PA can reduce makespan by prioritizing subtrees on the critical path, both in the partitioning and in the allocation. Based on randomly generated trees and real-world dataset, experimental results show that the proposed PA is significantly better than the latest work in terms of average makespan. The proposed algorithm can successfully reduce the average makespan by up to 67.01% on real-world dataset, and 52.35% on randomly generated trees.

关键词： parallel computing task scheduling tree partitioning heterogeneous multiprocessor platform critical path

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distributed-Memory k-mer Counting on GPUs 35

Distributed-Memory <i>k</i>-mer Counting on GPUs

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Nisa, Israt Pandey, Prashant Ellis, Marquita Oliker, Leonid Buluc, Aydin Yelick, Katherine Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA

ISBN: (纸本)9781665440660

A fundamental step in many bioinformatics computations is to count the frequency of fixed-length sequences, called k-mers, a problem that has received considerable attention as an important target for shared memory parallelization. With datasets growing at an exponential rate, distributed memory parallelization is becoming increasingly critical. Existing distributed memory k-mer counters do not take advantage of GPUs for accelerating computations. Additionally, they do not employ domain-specific optimizations to reduce communication volume in a distributed environment. In this paper, we present the first GPU-accelerated distributed-memory parallel k-mer counter. We evaluate the communication volume as the major bottleneck in scaling k-mer counting to multiple GPU-equipped compute nodes and implement a supermer-based optimization to reduce the communication volume and to enhance scalability. Our empirical analysis examines the balance of communication to computation on a state-of-the-art system, the Summit supercomputer at Oak Ridge National Lab. Results show overall speedups of up to two orders of magnitude with GPU optimization over CPU-based k-mer counters. Furthermore, we show an additional 1.5 x speedup using the supermer-based communication optimization.

关键词： GPU k-mer counter distributed memory

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Practice and Experience in using parallel and Scalable Machine Learning with Heterogenous Modular Supercomputing Architectures

Practice and Experience in using Parallel and Scalable Machi...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Riedel, Morris Sedona, Rocco Barakat, Chadi Einarsson, Petur Hassanian, Reza Cavallaro, Gabriele Book, Matthias Neukirchen, Helmut Lintermann, Andreas Univ Iceland Dept Comp Sci Reykjavik Iceland Forschungszentrum Julich Julich Supercomp Ctr Julich Germany

ISBN: (纸本)9781665435772

We observe a continuously increased use of Deep Learning (DL) as a specific type of Machine Learning (ML) for data-intensive problems (i.e., 'big data') that requires powerful computing resources with equally increasing performance. Consequently, innovative heterogeneous High-Performance computing (HPC) systems based on multi-core CPUs and many-core GPUs require an architectural design that addresses end user communities' requirements that take advantage of ML and DL. Still the workloads of end user communities of the simulation sciences (e.g., using numerical methods based on known physical laws) needs to be equally supported in those architectures. This paper offers insights into the Modular Supercomputer Architecture (MSA) developed in the Dynamic Exascale Entry Platform (DEEP) series of projects to address the requirements of both simulation sciences and data-intensive sciences such as High Performance Data Analytics (HPDA). It shares insights into implementing the MSA in the Julich Supercomputing Centre (JSC) hosting Europe No. 1 Supercomputer Julich Wizard for European Leadership Science (JUWELS). We augment the technical findings with experience and lessons learned from two application communities case studies (i.e., remote sensing and health sciences) using the MSA with JUWELS and the DEEP systems in practice. Thus, the paper provides details into specific MSA design elements that enable significant performance improvements of ML and DL algorithms. While this paper focuses on MSA-based HPC systems and application experience, we are not losing sight of advances in Cloud computing (CC) and Quantum computing (QC) relevant for ML and DL.

关键词： High performance computing cloud computing quantum computing machine learning deep learning parallel and distributed algorithms remote sensing health sciences modular supercomputer architecture

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A Game Theoretic Approach to Task Offloading for Multi-Data-Source Tasks in Mobile Edge computing 19

A Game Theoretic Approach to Task Offloading for Multi-Data-...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Chen, Shuyu Sun, Shiyong Chen, Haopeng Ruan, Jinteng Wang, Ziming Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China China Elect Technol Grp Corp Res Inst 54 Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781665435741

Mobile edge computing(MEC) is becoming an appealing paradigm due to its low-latency and low-overhead computing services offered to users. In MEC scenarios, most user data are usually stored on multiple edge nodes for their privacy, large scale, and unstructured nature instead of being centralized stored in the cloud datacenter. Consequently, we focus on the multi-data-source tasks and propose a game-theoretic approach to task offloading for task accomplishment. We formulate the multi-data-source task offloading as a unified task offloading optimization problem. The traditional centralized optimal solutions have the issues of high complexity, data privacy, and poor performance. To solve these issues, we decompose the global optimization problem into sub-optimization problems which each edge node can solve. Furthermore, we construct a non-cooperative game-theoretic offloading framework and prove the existence of Nash equilibrium. We then design a distributed task offloading strategy to find the Nash equilibrium, which combines an optimal subtask partial offloading algorithm for each node. We conduct simulation experiments and demonstrate that our proposed approach has better convergence, lower algorithm overhead, and superior performance in task offloading than other typical algorithms.

关键词： Multi-data-source task Task offloading Game theory Nash equilibrium Mobile edge computing

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Deep Reinforcement Agent for Scheduling in HPC 35

Deep Reinforcement Agent for Scheduling in HPC

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Fan, Yuping Lan, Zhiling Childers, Taylor Rich, Paul Allcock, William Papka, Michael E. IIT Chicago IL 60616 USA Argonne Natl Lab Lemont IL USA Northern Illinois Univ De Kalb IL USA

ISBN: (纸本)9781665440660

Cluster scheduler is crucial in high-performance computing (HPC). It determines when and which user jobs should be allocated to available system resources. Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads. However, the increasing complexity of computing systems and the highly dynamic nature of application workloads have placed tremendous burden on manually designed and tuned scheduling heuristics. More aggressive optimization and automation are needed for cluster scheduling in HPC. In this work, we present an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for Scheduling) by leveraging deep reinforcement learning. DRAS is built on a novel, hierarchical neural network incorporating special HPC scheduling features such as resource reservation and hacklilling. A unique training strategy is presented to enable DRAS to rapidly learn the target environment. Once being provided a specific scheduling objective given by system manager, DRAS automatically learns to improve its policy through interaction with the scheduling environment and dynamically adjusts its policy as workload changes. The experiments with different production workloads demonstrate that DRAS outperforms the existing heuristic and optimization approaches by up to 45%.

关键词： cluster scheduling high-performance computing deep reinforcement learning job starvation backfilling resource reservation

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