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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yan Jia Yuqing Cheng Kele Xu Yong Dou Peng Qiao Zhouyu He National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network’s ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI.

关键词： Training Ultrasonic imaging Tongue Self-supervised learning Data augmentation Data models Cognition Data mining Noise measurement Signal to noise ratio

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Performance Trade-offs of High Order Meshless Approximation on distributed Memory systems

arXiv

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arXiv 2025年

作者： Vehovar, Jon Rot, Miha Kosec, Gregor Jozef Stefan International Postgraduate School Jamova cesta 39 Ljubljana1000 Slovenia Institut Jožef Stefan Parallel and Distributed Systems Laboratory Jamova cesta 39 Ljubljana1000 Slovenia

Meshless methods approximate operators in a specific node as a weighted sum of values in its neighbours. Higher order approximations of derivatives provide more accurate solutions with better convergence characteristics, but they come at the cost of including more neighbours. On the accuracy-per-compute time basis we know that increasing the approximation order is beneficial for a shared memory computer, but there is additional communication overhead when problems become too large and we have to resort to distributed memory systems. Meshless nodes are divided between systems in spatially coherent subdomains with approximations at their edges requiring neighbouring value exchange. Performance optimization is then a balancing act between minimizing the required number of communicated neighbours by lowering the approximation order or increasing it to enable faster convergence. We use the radial basis function-generated finite difference method (RBF-FD) to approximate the derivatives that we use to solve the Poisson equation with an explicit iterative scheme. Inter-system communication is provided by Open MPI, while OpenMP is used for intra-system parallelisation. We perform the analysis on a homogenous CPU-based cluster where we examine the behaviour and attempt to determine the optimal parameterisation with the goal of minimizing the computational time to reach a desired accuracy. © 2025, CC BY.

关键词： Poisson equation

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CTXNL: A Software-Hardware Co-designed Solution for Efficient CXL-Based Transaction Processing 25

CTXNL: A Software-Hardware Co-designed Solution for Efficien...

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30th ACM International Conference on Architectural Support for Programming Languages and Operating systems, ASPLOS 2025

作者： Wang, Zhao Chen, Yiqi Li, Cong Guan, Yijin Niu, Dimin Guan, Tianchan Du, Zhaoyang Wei, Xingda Sun, Guangyu School of Integrated Circuits School of Computer Science Peking University Beijing China Peking University School of Integrated Circuits Beijing China Damo Academy Alibaba Group Hupan Lab Hangzhou China Shanghai Jiao Tong University Institute of Parallel and Distributed Systems Seiee Shanghai China School of Integrated Circuits Beijing Advanced Innovation Center for Integrated Circuits Peking University Beijing China

ISBN: (纸本)9798400710797

Transaction processing systems are the crux for modern data-center applications, yet current multi-node systems are slow due to network overheads. This paper advocates for Compute Express Link (CXL) as a network alternative, which enables low-latency and cache-coherent shared memory accesses. However, directly adopting standard CXL primitives leads to performance degradation due to the high cost of maintaining cross-node cache coherence. To address the CXL challenges, this paper introduces CTXNL, a software-hardware co-designed system that implements a novel hybrid coherence primitive tailored to the loosely coherent nature of transactional data. The core innovation of CTXNL is empowering transaction system developers with the ability to selectively achieve data coherence. Our evaluations on OLTP workloads demonstrate that CTXNL enhances performance, outperforming current network-based systems and achieves up to 2.08x greater throughput than vanilla CXL memory sharing architectures across universal transaction processing policies. © 2025 ACM.

关键词： Cache memory

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Liquid-state drive: a case for DNA block device for enormous data 25

Liquid-state drive: a case for DNA block device for enormous...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Jiahao Zhou Mingkai Dong Fei Wang Jingyao Zeng Lei Zhao Chunhai Fan Haibo Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University School of Chemistry and Chemical Engineering New Cornerstone Science Laboratory Frontiers Science Center for Transformative Molecules National Center for Translational Medicine Shanghai Jiao Tong University

ISBN: (纸本)9781939133458

The rapid development of DNA synthesis and sequencing technologies is making the ultra-high-density storage medium DNA to meet the rising demand for enormous data storage. The block storage interface, which is massively employed in storage systems, is the critical abstraction to integrate DNA storage into silicon-based computer systems. In this paper, we explore building block devices on DNA and identify the challenges of petabyte-scale metadata management and high DNA access costs. We propose a holistic DNA block device design called LIQUID-STATE DRIVE to provide low-cost block access to exabyte-scale data with the help of small yet fast SSDs. We adopt the dual-layer translation table to leverage SSDs to decrease the metadata updating cost. We introduce symbiotic metadata and delayed invalidation to reduce the cost of garbage collection and block updating. Our evaluation demonstrates that in microbenchmarks and real-world traces, the write cost reduces up to seven orders of magnitude and 2,927×, and the read cost reduces up to 6,206× and 7×, respectively. We expect our exploration and experience in building DNA block devices to be useful in expediting the advancement of DNA storage and bridging the gap between information technology and biotechnology.

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City models: past, present and future prospects

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Frontiers of Urban and Rural Planning 2025年第1期3卷 1-24页

作者： Ritter, Helge Herzog, Otthein Rothermel, Kurt Cohn, Anthony G. Wu, Zhiqiang Faculty of Technology Bielefeld University Bielefeld Germany College of Architecture and Urban Planning Tongji University Shanghai People’s Republic of China FB 03 University of Bremen Bremen Germany Institute of Parallel and Distributed Systems University of Stuttgart Stuttgart Germany School of Computer Science University of Leeds Leeds UK The Alan Turing Institute London UK

This paper attempts to take a comprehensive look at the challenges of representing the spatio-temporal structures and dynamic processes that define a city’s overall characteristics. For the task of urban planning and urban operation, we take the stance that even if the necessary representations of these structures and processes can be achieved, the most important representation of the relevant mindsets of the citizens are, unfortunately, mostly neglected. After a review of major “traditional” urban models of structures behind urban scale, form, and dynamics, we turn to major recent modeling approaches triggered by recent advances in AI that enable multimodal generative models. Some of these models can create representations of geometries, networks and images, and reason flexibly at a human-compatible semantic level. They provide huge amounts of knowledge extracted from huge collections of text and image documents and cover the required rich representation spectrum including geographic knowledge by different knowledge sources, degrees of granularity and scales. We then discuss what these new opportunities mean for coping with the modeling challenges posed by cities, in particular with regard to the role and impact of citizens and their interactions within the city infrastructure. We propose to integrate these possibilities with existing approaches, such as agent-based models, which opens up new modeling spaces including rich citizen models which are able to also represent social interactions. Finally, we put forward some thoughts about a vision of a “social AI in a city ecosystem” that adds relevant citizen models to state-of-the-art structural and process models. This extended city representation will enable urban planners to establish citizen-oriented planning of city infrastructures, to make them into inviting environments that reconcile and foster human culture, city resilience and sustainability.

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Optimistic Transaction Processing in Deterministic Database

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Journal of Computer Science & Technology 2020年第2期35卷 382-394页

作者： Zhi-Yuan Dong Chu-Zhe Tang Jia-Chen Wang Zhao-Guo Wang Hai-Bo Chen Bin-Yu Zang Institute of Parallel and Distributed Systems Shanghai Jiao Tong UniversityShanghai 200240China

Deterministic databases can improve the performance of distributed workload by eliminating the distributed commit protocol and reducing the contention ***,the current deterministic scheme does not consider the performance scalability within a single *** this paper,we describe a scalable deterministic concurrency control,Deterministic and Optimistic Concurrency Control(DOCC),which is able to scale the performance both within a single node and across multiple *** performance improvement comes from enforcing the determinism lazily and avoiding read-only transaction blocking the *** evaluation shows that DOCC achieves 8x performance improvement than the popular deterministic database system,Calvin.

关键词： deterministic database concurrency control scalability

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SYNC or ASYNC: Time to fuse for distributed graph-parallel computation 2015

SYNC or ASYNC: Time to fuse for distributed graph-parallel c...

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20th ACM SIGPLAN Symposium on Principles and Practice of parallel Programming, PPoPP 2015

作者： Xie, Chenning Chen, Rong Guan, Haibing Zang, Binyu Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science Shanghai Jiao Tong University China

ISBN: (纸本)9781450332057

Large-scale graph-structured computation usually exhibits iterative and convergence-oriented computing nature, where input data is computed iteratively until a convergence condition is reached. Such features have led to the development of two different computation modes for graph-structured programs, namely synchronous (Sync) and asynchronous (Async) modes. Unfortunately, there is currently no in-depth study on their execution properties and thus programmers have to manually choose a mode, either requiring a deep understanding of underlying graph engines, or suffering from suboptimal performance. This paper makes the first comprehensive characterization on the performance of the two modes on a set of typical graph-parallel applications. Our study shows that the performance of the two modes varies significantly with different graph algorithms, partitioning methods, execution stages, input graphs and cluster scales, and no single mode consistently outperforms the other. To this end, this paper proposes Hsync, a hybrid graph computation mode that adaptively switches a graph-parallel program between the two modes for optimal performance. Hsync constantly collects execution statistics on-the-fly and leverages a set of heuristics to predict future performance and determine when a mode switch could be profitable. We have built online sampling and offline profiling approaches combined with a set of heuristics to accurately predicting future performance in the two modes. A prototype called PowerSwitch has been built based on PowerGraph, a state-of-the-art distributed graph-parallel system, to support adaptive execution of graph algorithms. On a 48-node EC2-like cluster, PowerSwitch consistently outperforms the best of both modes, with a speedup ranging from 9% to 73% due to timely switch between two modes. Copyright 2015 ACM.

关键词： Graphic methods

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Addressing Activation Outliers in LLMs: A Systematic Review of Post-Training Quantization Techniques

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IEEE Access 2025年 13卷 81917-81932页

作者： Patrik Czakó Gábor Kertész Sándor Szénási Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Laboratory of Parallel and Distributed Systems Institute for Computer Science and Control (SZTAKI) Hungarian Research Network (HUN-REN) Budapest Hungary Faculty of Economics and Informatics J. Selye University Komárno Slovakia

Large Language Models (LLMs) have transformed natural language processing, yet their deployment remains challenging due to substantial computational, memory, and energy demands. Post-training quantization has emerged as a key strategy for enabling efficient inference, particularly in resource-constrained settings. This systematic review focuses on weight-activation quantization, with a unique emphasis on the emergent outlier phenomenon in LLM activations. This work evaluates recent techniques that mitigate activation outliers and improve quantization efficiency, distinguishing itself from prior reviews. Using the PRISMA methodology, we examine 52 recent studies to uncover key trends and evaluate the effectiveness of different approaches. By synthesizing insights from these works, this review presents a diverse set of techniques and their implications for activation quantization, laying the groundwork for future research and practical advancements in LLM deployment.

关键词： Quantization (signal) Computational modeling Throughput Arithmetic Decoding Adaptation models Transformers Systematics Systematic literature review Large language models

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Revisiting Persistent Indexing Structures on Intel Optane DCPersistent Memory

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Journal of Computer Science & Technology 2021年第1期36卷 140-157页

作者： Heng Bu Ming-Kai Dong Ji-Fei Yi Bin-Yu Zang Hai-Bo Chen Institute of Parallel and Distributed Systems Shanghai Jiao Tong UniversityShanghai 200240China

Persistent indexing structures are proposed in response to emerging non-volatile memory(NVM)to provide high performance yet durable ***,due to the lack of real NVM hardware,many prior persistent indexing structures were evaluated via emulation,which varies a lot across different setups and differs from the real ***,Intel has released its Optane DC Persistent Memory Module(PMM),which is the first production-ready *** this paper,we revisit popular persistent indexing structures on PMM and conduct comprehensive evaluations to study the performance differences among persistent indexing structures,including persistent hash tables and persistent *** to the evaluation results,we find that Cacheline-Conscious Extendible Hashing(CCEH)achieves the best performance among all evaluated persistent hash tables,and Failure-Atomic ShifT B+-Tree(FAST)and Write Optimal Radix Tree(WORT)perform better than other ***,we find that the insertion performance of hash tables is heavily influenced by data locality,while the insertion latency of trees is dominated by the flush *** also uncover that no existing emulation methods accurately simulate PMM for all the studied data ***,we provide three suggestions on how to fully utilize PMM for better performance,including using clflushopt/clwb with sfence instead of clflush,flushing continuous data in a batch,and avoiding data access immediately after it is flushed to PMM.

关键词： persistent memory data structure performance Intel Optane DC persistent memory module(PMM)

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HCloud:A Trusted JointCloud Serverless Platform for IoT systems with Blockchain

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China Communications 2020年第9期17卷 1-10页

作者： Zheng Huang Zeyu Mi Zhichao Hua Institute of Parallel and Distributed Systems Shanghai Jiao Tong UniversityShanghai 200240China

Cloud computing has been exploited in managing large-scale IoT *** cloud servers usually handle a large number of requests from various IoT *** to the fluctuant and heavy workload,the servers require the cloud to provide high scalability,stable performance,low price and necessary ***,traditional clouds usually offer computing service with the abstraction of virtual machine(VM),which can hardly meet these ***,different cloud vendors provide different performance stabilities and price models,which fluctuate according to the dynamic workload.A single cloud cannot satisfy all the requirements of the IoT scenario *** JointCloud computing model empowers the cooperation among multiple public ***,it is still difficult to dynamically schedule the workload on different clouds based on the VM *** paper introduces HCloud,a trusted JointCloud platform for IoT systems using serverless computing *** allows an IoT server to be implemented with multiple serverless functions and schedules these functions on different clouds based on a schedule *** policy is specified by the client and includes the required functionalities,execution resources,latency,price and so *** collects the status of each cloud and dispatches serverless functions to the most suitable cloud based on the schedule *** leveraging the blockchain technology,we further enforce that our system can neither fake the cloud status nor wrongly dispatch the target *** have implemented a prototype of HCloud and evaluated it by simulating multiple cloud *** evaluation results show that HCloud can greatly improve the performance of serverless workloads with negligible costs.

关键词： IoT blockchain serverless jointcloud

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