Svetozar Margenov
Professor
Bulgarian Academy of Sciences
演讲标题:Performance Analysis of Modern Supercomputers, Problem-Oriented Metrics, and Applications
摘要:Modern computer technologies provide more and more computing power. At the same time, high-performance computing (HPC) systems are becoming more widespread. Let us note the following example, with an increasing impact on the daily life of each of us. The availability of Big Data together with artificial intelligence (AI) tools and the rapid growth of affordable supercomputing resources have posed new challenges to language and content technologies aimed at building ever more innovative products and services. The second example is a little different. Quantum computing is considered one of the most promising technological directions of strategic importance. Although they are still at an early stage of development, quantum computing systems are already available. The synergy between advanced e-infrastructure and AI in a Big Data environment creates qualitatively new opportunities for science and engineering.
The first part of the talk discusses the current development of the computing power of HPC systems. The LINPACK benchmark has been used for more than 30 years to build the TOP500 list ranking the world's most powerful supercomputers. Although the computational linear algebra forms a backbone of the whole computing, the real life HPC performance is rather different than the one achieved on any single benchmark. This necessitates additional application-oriented approaches and metrics to HPC performance evaluation. Here we analyze data from the last two editions of the TOP500. Trends in the development of HPC architectures from the point of view of AI applications are identified. In addition, the increasingly important topic of energy efficiency is discussed. In this context, the capabilities of the petascale supercomputer HEMUS are also discussed. HEMUS is a part of the Infrastructure Complex for Digital Transformation and Large-scale Computing build in the Institute of Information and Communication Technologies of the Bulgarian Academy of Sciences.
In the second part of the presentation, we look at three case studies illustrating the concept of synergy between HPC performance and the scalability of modern methods and algorithms in a Big Data environment. Our developed BURA (Best Uniform Rational Approximation) method has a fundamental role in a wide range of computational models where the process does not satisfy the Brownian motion hypothesis, thus exhibiting anomalous (fractional) diffusion. BURA has several levels of parallelism that enable efficient use of the hierarchical architecture of modern supercomputers. The second case study discusses a biomedical application showing the necessity of supercomputing performance. The process of radiofrequency hepatic tumor ablation is modelled to optimize the parameters of the electrosurgical equipment. The problem is inherently 3D in space, nonlinear, multiscale and multiphysics, with the computer model reaching O(1012) degrees of freedom. The third but equally important example concerns the improvement of scalability and accuracy of quantum annealing computations. The advantages of the developed algorithms are confirmed by results of numerical experiments on a D-Wave 2000 Q system with more than 5000 qubits. The improvement of quantum algorithms often requires the application of fundamentally new approaches. One may be surprised by the interpretation of the substantially changed concepts of parallelism in the proposed parallel quantum annealing.
At the end, concluding remarks are given, including some open problems.
简历: Svetozar Margenov是保加利亚科学院（BAS）的通讯院士，同时担任计算数学教授。他毕业于索非亚大学“圣克利门特·奥赫里德斯基”的数学与信息学系，并获得了保加利亚科学院的博士学位和科学博士学位。
Svetozar Margenov教授的主要科学成就在信息技术领域，包括计算复杂性、偏微分方程的数值方法、分数阶扩散问题的数值解法、大规模病态线性系统的多层迭代方法、并行方法与算法以及超级计算机应用。他已发表了超过200篇科学论文和两部专著。
Svetozar Margenov教授现任信息与通信技术研究所（IICT-BAS）所长，并领导科学计算部门及其3D数字化与微结构分析实验室。他是美国工业与应用数学学会（SIAM）保加利亚分会的联合创始人，并在前两届担任主席。
Svetozar Margenov教授为现代保加利亚高性能计算（HPC）基础设施的发展作出了重要贡献，并在HPC应用领域拥有卓越的专业知识。他目前领导信息学与信息通信技术卓越中心，该中心于2023年启用了最新的保加利亚千万亿次级超级计算机HEMUS。
Svetozar Margenov教授因其杰出成就被授予保加利亚科学院的最高荣誉——“马林·德里诺夫”绶带荣誉徽章。他还获得了2024年沃尔特·斯科特信息技术奖，并被选为国际信息技术与数量管理学院院士。
Chang-Yun Lin
Professor
National Chung Hsing University
演讲标题:Multiresponse surface methodology for hyperparameter tuning to optimize multiple performance measures of statistical and machine learning algorithms
摘要: Hyperparameter tuning is an important task in machine learning for controlling model complexity and improving prediction performance. Most methods in the literature can only be used to tune hyperparameters to optimize a single performance measure. In practice, participants may want to optimize multiple measures of the model; however, optimizing one measure may worsen another. Therefore, a hyperparameter tuning method is proposed using the multiresponse surface methodology to solve the tradeoff problem between multiple measures. A search algorithm that requires fewer tuning runs is developed to find the optimal hyperparameter settings systematically based on the preferences of participants for multiple measures of the model. An example using the random forest algorithm is provided to demonstrate the application of the proposed method to improve the prediction performance of the model on unbalanced data.
Huawei Huang
Associate Professor
Sun Yat-Sen University, China
ORCID: https://orcid.org/0000-0002-7035-6446
演讲标题:BrokerFi, Broker2Earn, and BlockEmulator: Building the Ecosystem of BrokerChain
摘要: This talk presents the ecosystem built on top of BrokerChain, which is a sharding blockchain developed by Prof. Huawei Huang’s research group (i.e., HuangLab: http://xintelligence.pro/ ).
BrokerChain was originally designed as a cross-shard transaction protocol, and was published in INFOCOM 2022. Starting from BrokerChain protocol, HuangLab members have kept building a sharding blockchain, also named BrokerChain. To keep improving the performance of BrokerChain, we also proposed Broker2Earn protocol (published in INFOCOM 2024), which serves as an incentive mechanism for BrokerChain because it can help recruit enough broker accounts and offer token-liquidity service for other BrokerChain clients. We then developed a DeFi product, named BrokerFi, by integrating the blockchain BrokerChain and the Broker2Earn protocol. Via participating in BrokerFi, brokers can earn native revenues when they stake their idle tokens in the Broker2Earn protocol. On the other hand, Broker2Earn can also benefit the sharded blockchain BrokerChain because it can efficiently spend each staked token on improving BrokerChain’s performance, such as TPS (transaction per second), TCL (transaction confirmation latency), and the queueing length in transaction pools. In the near future, we shall try our best to promote BrokerFi as a real DeFi product in the Hong Kong Web3 market.
BlockEmulator is a blockchain experimental tool open-sourced by HuangLab. Its homepage is https://www.blockemulator.com/. We are currently building a DAO for BlockEmulator on top of BrokerChain, aiming to incentivize blockchain researchers to contribute to and participate into the ecosystem of BlockEmulator.
简历: Huawei Huang 在日本会津大学获得计算机科学与工程博士学位。目前，他是中国中山大学的副教授。他的研究兴趣包括区块链、Web3 和去中心化金融协议（DeFi）。他曾担任日本学术振兴会（JSPS）研究员，香港理工大学的访问学者，以及日本京都大学的助理教授。他是 IEEE 的高级会员。自 2023 年以来，他被评为全球排名前 2% 的科学家。实验室主页：xintelligence.pro
Google scholar: https://scholar.google.com/citations?user=DlKPajcAAAAJ&hl=en
Minxian XU
Associate Professor
Chinese Academy of Sciences
ORCID: https://orcid.org/0000-0002-0046-5153
演讲标题:Cloud-native System Support for Efficient Large Language Models Inference Serving
摘要:Large Language Models (LLMs) are revolutionizing artificial intelligence by enabling advanced natural language processing applications. However, the computational demands of LLM inference require scalable, efficient, and robust system support. This talk investigates cloud-native techniques to optimize LLM inference serving. Specifically, it explores (1) batching inference requests to optimize key-value (KV) cache utilization for enhanced GPU performance and throughput, (2) leveraging containerization for fine-grained resource control to achieve lightweight scheduling and replication, and (3) dynamically balancing workloads through adaptive resource allocation and scaling. Collaborating with industry leaders, this research will integrate first-hand workload data into system design and validation. The outcomes aim to advance system-level support for LLMs, contributing to improved performance and reduced costs for cloud-native AI services.
简历:博士是中国科学院深圳先进技术研究院（SIAT）的副教授。他于2019年在墨尔本大学获得计算与信息系统博士学位。许博士已在多个领先的期刊和会议上发表了70多篇研究论文，包括《CSUR》、《TSC》、《TMC》、《TAAS》和《TOIT》等。他的研究工作已获得超过3,800次引用（谷歌学术），主要集中在分布式和云计算系统的高效管理，特别在云平台的负载均衡、能源效率以及云原生环境下的微服务管理等领域具有丰富的专业知识。许博士的研究成就包括获得2019年IEEE TCSC杰出博士学位论文奖以及2023年IEEE TCSC卓越奖（早期职业奖）。他还被斯坦福大学评为2023年和2024年全球排名前2%的科学家。他是IEEE和CCF的高级会员。
Google scholar:https://scholar.google.com/citations?user=KeJL7gEAAAAJ&hl=zh-CN&oi=ao
Ahmad Ali
Dr.
Shanghai Jiao Tong University
演讲标题:Exploiting Dynamic Spatio-Temporal Graph Convolutional Neural Networks for Citywide Traffic Flow Prediction
摘要:In this talk, I will present recent advancements in using spatio-temporal graph convolutional neural networks for predicting citywide traffic flows. These models capture both spatial dependencies across road networks and temporal patterns of traffic dynamics, enabling accurate forecasting of traffic congestion. I will discuss key challenges, model design, and real-world applications that can significantly improve traffic management in smart cities.
简历:Ahmad Ali在上海交通大学完成了博士学位，目前是深圳大学的博士后研究员。Ali博士为多家知名期刊审阅了超过1150篇文章，这些期刊包括Applied Soft Computing、Information Fusion、Information Sciences、Neural Networks、Applied Intelligence以及IEEE Transactions on Industrial Informatics