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A Framework for distributed Quantum Queries in the CONGEST Model 22

A Framework for Distributed Quantum Queries in the CONGEST M...

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acm symposium on principles of distributed computing (PODC)

作者： van Apeldoorn, Joran de Vos, Tijn Univ Amsterdam QuSoft Amsterdam Netherlands Univ Amsterdam IViR Amsterdam Netherlands Univ Salzburg Salzburg Austria

ISBN: (纸本)9781450392624

The Quantum CONGEST model is a variant of the CONGEST model, where messages consist of O(log(n)) qubits. In this paper, we give a general framework for implementing quantum query algorithms efficiently in a Quantum CONGEST network, using the concept of parallel-query quantum algorithms. We apply our framework for distributed quantum queries in two settings: problems where data is distributed over the network, and graph theoretical problems where the network defines the input. The first setting is slightly unusual in CONGEST but here our results follow almost directly from the quantum query setting. The second setting is more traditional for the CONGEST model but here our framework requires also some classical CONGEST steps to apply. In the setting with distributed data, we show how a network can pick one of k dates for a meeting such that a maximum number of nodes is available, using (O) over tilde(root kD + D) rounds, with D the network diameter. The classical complexity is linear in k. We also give an efficient algorithm for element distinctness: if all nodes together holds a list of k numbers, we show that the nodes can determine whether there are any duplicates in (O) over tilde (k(2/3) D-1/3 + D) rounds. Classically this problem requires (Omega) over tilde (k + D) rounds. We also generalize the protocol for the distributed Deutsch-Jozsa problem from the two-party setting considered by Buhrman, Cleve, and Wigderson [4] to general networks. This gives a novel separation between exact classical and exact quantum protocols in the CONGEST model. In the setting where the input is the network structure itself, our framework almost directly allows us to recover the O(root nD) round diameter computation algorithm of Le Gall and Magniez [21]. We extend this approach to also compute the radius in the same number of rounds, and to give an epsilon-additive approximation of the average eccentricity in (O) over tilde (D-3/2/epsilon) rounds. Finally, we give the first quantum

关键词： Quantum CONGEST element distinctness distributed Deutsch-Jozsa cycle detection girth

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MobiWac 2023 - proceedings of the Internatinal acm symposium on Mobility Management and Wireless Access

MobiWac 2023 - Proceedings of the Internatinal ACM Symposium...

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21st acm International symposium on Mobility Management and Wireless Access, MobiWac 2023

ISBN: (纸本)9798400703676

The proceedings contain 17 papers. The topics discussed include: quantifying network level improvement due to beamforming on the performance of large-scale dense urban IoT networks;spatial user clustering and power control for downlink MIMO-NOMA systems;user-centric AP clustering with deep reinforcement learning for cell-free massive MIMO;new machine learning hybrid models to lower position errors for Bluetooth-based indoor localizations;application-aware energy attack mitigation in the battery-less internet of things;spatial anti-void querying in IoT-enabled wireless sensor networks;Median-Krum: a joint distance-statistical based byzantine-robust algorithm in federated learning;distributed collaborative learning in wireless mobile communication;online dependency-aware task offloading in cloudlet-based edge computing networks;and gated recurrent units for blockage mitigation in mmWave wireless.

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UGACHE: A Unified GPU Cache for Embedding-based Deep Learning 23

UGACHE: A Unified GPU Cache for Embedding-based Deep Learnin...

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29th acm symposium on Operating Systems principles (SOSP)

作者： Song, Xiaoniu Zhang, Yiwen Chen, Rong Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798400702297

This paper presents UGache, a unified multi-GPU cache system for embedding-based deep learning (EmbDL). UGache is primarily motivated by the unique characteristics of EmbDL applications, namely read-only, batched, skewed, and predictable embedding accesses. UGache introduces a novel factored extraction mechanism that avoids bandwidth congestion to fully exploit high-speed cross-GPU interconnects (e.g., NVLink and NVSwitch). Based on a new hotness metric, UGache also provides a near-optimal cache policy that balances local and remote access to minimize the extraction time. We have implemented UGache and integrated it into two representative frameworks, TensorFlow and PyTorch. Evaluation using two typical types of EmbDL applications, namely graph neural network training and deep learning recommendation inference, shows that UGache outperforms state-of-the-art replication and partition designs by an average of 1.93x and 1.63x (up to 5.25x and 3.45x), respectively.

关键词： GPU cache Embedding GPU interconnect

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Skadi: Building a distributed Runtime for Data Systems in Disaggregated Data Centers 23

Skadi: Building a Distributed Runtime for Data Systems in Di...

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19th acm Workshop on Hot Topics in Operating Systems (HotOS)

作者： Hu, Cunchen Wang, Chenxi Wang, Sa Sun, Ninghui Bao, Yungang Zhao, Jieru Kashyap, Sanidhya Zuo, Pengfei Chen, Xusheng Xu, Liangliang Zhang, Qin Feng, Hao Shan, Yizhou Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci ICT State Key Lab Proc Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Ecole Polytech Fed Lausanne Lausanne Switzerland Huawei Cloud Dublin Ireland

ISBN: (纸本)9798400701955

Data-intensive systems are the backbone of today's computing and are responsible for shaping data centers. Over the years, cloud providers have relied on three principles to maintain cost-effective data systems: use disaggregation to decouple scaling, use domain-specific computing to battle waning laws, and use serverless to lower costs. Although they work well individually, they fail to work in harmony: an issue amplified by emerging data system workloads. In this paper, we envision a distributed runtime to mitigate current shortcomings. The distributed runtime has a tiered access layer exposing declarative APIs, underpinned by a stateful serverless runtime with a distributed task execution model. It will be the narrow waist between data systems and hardware. Users are oblivious to data location, concurrency, disaggregation style, or even the hardware to do the computing. The underlying stateful serverless runtime transparently evolves with novel data-center architectures, such as disaggregation and tightly-coupled clusters. We prototype Skadi to showcase that the distributed runtime is practical.

关键词： Cost effectiveness

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Brief Announcement: Asymmetric Mutual Exclusion for RDMA 36

Brief Announcement: Asymmetric Mutual Exclusion for RDMA

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36th International symposium on distributed computing, DISC 2022

作者： Nelson-Slivon, Jacob Tseng, Lewis Palmieri, Roberto Lehigh University BethlehemPA United States Boston College MA United States

ISBN: (纸本)9783959772556

In this brief announcement, we define operation asymmetry, which captures how processes may interact with an object differently, and discuss its implications in the context of a popular network communication technology, remote direct memory access (RDMA). Then, we present a novel approach to mutual exclusion for RDMA-based distributed synchronization under operation asymmetry. Our approach avoids RDMA loopback for local processes and guarantees starvation-freedom and fairness. © Jacob Nelson-Slivon, Lewis Tseng, and Roberto Palmieri.

关键词： distributed computer systems

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DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems 51

DS-GL: Advancing Graph Learning via Harnessing Nature's Powe...

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acm/IEEE 51st Annual International symposium on Computer Architecture (ISCA)

作者： Song, Ruibing Wu, Chunshu Liu, Chuan Li, Ang Huang, Michael Geng, Tony Tong Univ Rochester Rochester NY 14642 USA Pacific Northwest Natl Lab Richland WA 99352 USA

ISBN: (纸本)9798350326598;9798350326581

With the rapid digitization of the world, an increasing number of real-world applications are turning to non-Euclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamical systems fabricated with CMOS technologies, have emerged as a new approach that harnesses the inherent power of nature within dynamical systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are binary and thus ill-suited for real-valued problems;(ii) their expensive all-to-all coupling network that guarantees generality for optimization problems poses daunting scalability concerns. To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamical system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system's original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamical system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and community structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamical system while maintaining hi

关键词： Dynamical System Graph Learning Nature-Powered computing

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ByteMQ: A Cloud-native Streaming Data Layer in ByteDance 15

ByteMQ: A Cloud-native Streaming Data Layer in ByteDance

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15th Annual acm symposium on Cloud computing, SoCC 2024

作者： Mao, Yancan Yin, Ruohang Lei, Liyuan Ye, Peng Zou, Shengfu Tang, Shizheng Guo, Yunzhe Yuan, Ye Yu, Xiaochen Wan, Bo Gong, Yunfei Gao, Changli Zhang, Guanghui Shen, Jian Shi, Rui Ma, Richard T. B. ByteDance Inc Singapore National University of Singapore Singapore China

ISBN: (纸本)9798400712869

Real-time streaming data is generated in high volumes and consumed for statistical and analytical purposes, requiring efficient and effective management by Message Queuing Systems (MQS) that ensure high throughput and low latency. ByteDance relies extensively on MQS to handle its massive streaming data across various applications. However, existing MQS solutions often fall short of meeting ByteDance's high-volume, diverse requirements. To address these challenges, we propose ByteMQ (BMQ), a cloud-native streaming data layer designed to manage ByteDance's extensive streaming data needs efficiently in the cloud. BMQ features three key designs: 1) separation of messaging and storage, utilizing ByteDance's Federated distributed File System (DFS) for high-performance data storage;2) adaptive resource scheduling to balance workloads and redistribute resources across multiple availability zones;and 3) historical data restructuring to support offline applications with efficient structured data management. ByteDance has migrated 99.76% of its Kafka clusters to BMQ infrastructure, achieving about a 70% reduction in resource costs. This paper shares our journey of designing and implementing BMQ, providing insights that may benefit other organizations facing similar challenges. © 2024 acm.

关键词： Data Management distributed System Resource Scheduling

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BAGPIPE: Accelerating Deep Recommendation Model Training 23

BAGPIPE: Accelerating Deep Recommendation Model Training

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29th acm symposium on Operating Systems principles (SOSP)

作者： Agarwal, Saurabh Yan, Chengpo Zhang, Ziyi Venkataraman, Shivaram Univ Wisconsin Madison Madison WI 53706 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9798400702297

Deep learning based recommendation models (DLRM) are widely used in several business critical applications. Training such recommendation models efficiently is challenging because they contain billions of embedding-based parameters, leading to significant overheads from embedding access. By profiling existing systems for DLRM training, we observe that around 75% of the iteration time is spent on embedding access and model synchronization. Our key insight in this paper is that embedding access has a specific structure which can be used to accelerate training. We observe that embedding accesses are heavily skewed, with around 1% of embeddings representing more than 92% of total accesses. Further, we also observe that during offline training we can lookahead at future batches to determine which embeddings will be needed at what iteration in the future. Based on these insights, we develop Bagpipe, a system for training deep recommendation models that uses caching and prefetching to overlap remote embedding accesses with the computation. We design an Oracle Cacher, a new component that uses a lookahead algorithm to generate optimal cache update decisions while providing strong consistency guarantees against staleness. We also design a logically replicated, physically partitioned cache and show that our design can reduce synchronization overheads in a distributed setting. Finally, we propose a disaggregated system architecture and show that our design can enable low-overhead fault tolerance. Our experiments using three datasets and four models show that Bagpipe provides a speed up of up to 5.6x compared to state of the art baselines, while providing the same convergence and reproducibility guarantees as synchronous training.

关键词： distributed Training Recommendation Models

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Enhancing University Curricula with Integrated AI Ethics Education: A Comprehensive Approach 2025

Enhancing University Curricula with Integrated AI Ethics Edu...

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56th Technical symposium on Computer Science Education

作者： Deb, Debzani Taylor, Greg Betz, Scott Maddux, Bao Anh T. Ebert, C. Edward Richardson, Flourice W. Couto, Jeanine Lino S. Jarrett, Michael S. Madjd-Sadjadi, Zagros Winston Salem State Univ Dept Comp Sci Winston Salem NC 27110 USA Winston Salem State Univ Dept Business Adm Winston Salem NC USA Winston Salem State Univ Dept Art Visual Studies Winston Salem NC USA Winston Salem State Univ Dept Math Winston Salem NC USA West Virginia Sch Osteopath Med Dept Biomed Sci Lewisburg WV USA NC A&T State Univ Dept English Greensboro NC USA Winston Salem State Univ Dept World Languages & Cultures Winston Salem NC USA Winston Salem State Univ Dept Exercise Physiol Winston Salem NC USA Winston Salem State Univ Dept Accounting & Econ Winston Salem NC USA

ISBN: (纸本)9798400705311

As AI technologies become more prevalent, it is crucial for students to develop responsible, ethical, and proactive AI engagement skills. Recent educational initiatives have focused on enhancing CS and engineering students' AI ethics education but have largely overlooked integrating these concepts across other disciplines. This paper presents and assesses a pioneering initiative that integrates AI ethics into university curricula through a collaborative framework between CS and domain educators. We introduced 1-3-week AI ethics modules in seven diverse courses from Art to Chemistry, incorporating case studies and hands-on activities using chat- or image-based Large Language Models (LLMs). Student surveys indicated significant gains in confidence regarding AI ethics discussions, application of principles, and reasoning skills. Our approach advocates for utilizing structured frameworks and faculty collaboration in embedding AI ethics into university curricula, enhancing students' practical skills and ethical understanding across diverse professional settings.

关键词： AI Ethics computing education Ethics education Inclusive computing curricula and pedagogy Non-majors

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distributed Edge Coloring in Time Polylogarithmic in Δ 22

Distributed Edge Coloring in Time Polylogarithmic in Δ

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acm symposium on principles of distributed computing (PODC)

作者： Balliu, Alkida Brandt, Sebastian Kuhn, Fabian Olivetti, Dennis Gran Sasso Sci Inst Laquila Italy CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Univ Freiburg Freiburg Germany

ISBN: (纸本)9781450392624

We provide new deterministic algorithms for the edge coloring problem, which is one of the classic and highly studied distributed local symmetry breaking problems. As our main result, we show that a (2 Delta - 1)-edge coloring can be computed in time poly log Delta + O(log* n) in the LOCAL model. This improves a result of Balliu, Kuhn, and Olivetti [PODC '20], who gave an algorithm with a quasi-polylogarithmic dependency on Delta. We further show that in the CONGEST model, an (8 + epsilon)Delta-edge coloring can be computed in poly log Delta + O(log* n) rounds. The best previous O(Delta)-edge coloring algorithm that can be implemented in the CONGEST model is by Barenboim and Elkin [PODC '11] and it computes a 2(O(1/epsilon)) Delta-edge coloring in time O(Delta(epsilon) + log* n) for any epsilon is an element of(0, 1].

关键词： edge coloring LOCAL model CONGEST model token dropping

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