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      1. ADL97《圖計算》開始報名了

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        2018-10-16

        The CCF Advanced Disciplines Lectures
        中國計算機學會學科前沿講習班
        CCF ADL 第97期
        主題《圖計算》
        2018年11月16-18日 武漢

        微信圖片_20180930112257

        馬上報名

            萬物皆關聯。作為表達和處理關聯關系的最佳方式,圖和圖計算已經成為人們的關注重點和研究熱點,廣泛應用于金融分析、社交分析、智慧交通等諸多領域。作為大數據處理的一種典型模式,圖計算不僅對計算機體系結構提出了嚴峻的挑戰,也對系統軟件、數據管理和處理模式提出了重大挑戰。當前,圖計算還是國家重點研發計劃的資助重點。本期CCF學科前沿講習班邀請了多位學術界和工業界的著名學者,將圍繞大圖處理的系統結構、數據管理、分布式計算、優化算法等方面進行介紹,探討相關技術的未來發展趨勢。本講習班旨在幫助學員了解大數據背景下的圖計算熱點和前沿科學問題,開闊科研視野,增進學術交流和增強實踐能力。

        學術主任:金海 華中科技大學

        主辦單位:中國計算機學會


        日程安排

        2018年11月16日

        15:00-17:00 報到

        地點:湖北武漢華中科技大學東5樓210

        2018年11月17日

        8:00-8:15   開班儀式

        8:15-10:15   學術專題講座1

        題目:圖計算加速器

        講者:金海,華中科技大學

        10:30-12:30   學術專題講座2

        題目:Towards Big Graph Processing: Applications, Challenges, and Advances

        講者:Xuemin Lin,The University of New South Wales

        12:30-13:30   午餐

        13:30-15:30  學術專題講座3

        題目:Graph Computing Technique for AI and Big Data Applications

        講者:Yeh-Ching Chung,香港中文大學深圳分校

        16:00-18:00   學術專題講座4

        題目:圖數據計算系統中的優化策略---兩個案例研究上的思考

        講者:鄒磊,北京大學


        2018年11月18日

        8:00-10:00   學術專題講座5

        題目:Parallel Graph Processing on GPUs

        講者:Bingsheng He,National University of Singapore

        10:15-12:15   學術專題講座6

        題目:圖計算的體系結構設計啟示

        講者:李超,上海交通大學

        12:15-13:00   午餐

        13:30-15:30   學術專題講座7

        題目:圖分析與數據智能技術及在阿里巴巴的應用

        講者:錢正平, 阿里公司

        15:45-17:30   學術討論,合影,結業式

        (如有變動,以現場為準)


        時間:2018年11月17-18日

        地點:湖北省武漢市華中科技大學東5樓210


        特邀講者(按專題講座的時間順序)


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        金海

        華中科技大學

        Bio:金海,華中科技大學教授、博士生導師,長江學者特聘教授,國家杰出青年基金獲得者,中國計算機學會會士,華中科技大學“服務計算技術與系統教育部重點實驗室”主任,“集群與網格計算湖北省重點實驗室”主任,“大數據技術與系統湖北省工程實驗室”主任。國務院特殊津貼專家、國務院學位委員會第六、七屆學科評議組成員、第六、七屆教育部科學技術委員會信息學部委員、副主任委員。973計劃“計算系統虛擬化基礎理論與方法研究”、“云計算安全的基礎理論和方法研究”首席科學家、十三五云計算與大數據國家重點研發計劃專家組副組長、教育部重大專項“中國教育科研網格ChinaGrid”計劃的專家組組長、“十二五”國家高技術研究發展計劃(863計劃)信息技術領域主題專家組專家、“十一五”國家863計劃“高效能計算機及網格服務環境”重大項目專家組成員。中國計算機學會常務理事、湖北省計算機學會理事長、中國計算機學會青工委副主任、中國計算機學會高性能計算專委會副主任委員/普適計算專委會委員/服務計算專委會委員/大數據專委會委員、中國電子學會云計算/物聯網/信息安全專家委員會委員。教育部“長江學者和創新團隊發展計劃”創新團隊學術帶頭人、湖北省自然科學基金創新團隊學術帶頭人。獲國家科技進步二等獎2項、國家發明二等獎1項、國家自然科學四等獎1項、教育部科技進步/技術發明一等獎3項、湖北省科技進步/技術發明一等獎2項。主要研究領域為計算機體系結構、計算系統虛擬化、集群計算和云計算、網絡安全、對等計算、網絡存儲與并行I/O等。金海教授在IEEE/ACM期刊上發表論文80余篇。獲國家發明專利190余項,獲國家軟件著作版權130余項。主持了一批重大科研項目,包括973項目、教育部重大專項、國家杰出青年基金項目、國家自然科學基金重大/重點項目、863重點項目、CNGI項目、霍英東高等院校青年教師基金項目、國際合作項目等。


        講座題目:圖計算加速器


        摘要:隨著大數據產業的深入發展,表達關聯關系的圖數據規模正在爆炸式增長,傳統通用處理架構在新的需求面前逐漸陷入困境,存在著并行效率低、訪存隨機性強以及數據沖突頻度高等突出問題,因此,開展架構創新驅動的圖計算加速器研究有著重要的現實意義。報告系統性地回顧了圖計算加速器的演變發展,探討了我們在圖計算加速器架構設計上的一些探索,并對圖計算加速器及其軟件生態構建中存在的技術挑戰和發展機遇進行了總結展望。

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        Xuemin Lin

        The University of New South Wales

        Bio:Xuemin Lin is a UNSW Scientia Professor , the head of database group in the school of computer science and engineering at UNSW, and a current Professor at ECNU (specially appointed by the Chinese National Thousands Distinguished Professors Program). He is a fellow of IEEE. Xuemin's research interests lie in databases, data mining, algorithms, and complexities. Specifically, he is working in the areas of scalable processing and mining of various data, including graph, spatial-temporal, streaming, text and uncertain data.Xuemin has been very frequently serving as a PC member and area chairs (senior PC members) in SIGMOD, VLDB, ICDE, ICDM, KDD, CIKM, and EDBT. He received the honour of outstanding reviewer in KDD2012. He was an associate editor of ACM TODS (2008-2014) and IEEE TKDE (Feb 2013- Jan 2015), and an associate editor-in-Chief of TKDE (2015-2016), respectively. Currently, he is the editor-in-Chief of TKDE (Jan 2017 - now) and an associate editor of WWW Journal (2013 - now).


        講座題目:Towards Big Graph Processing: Applications, Challenges, and Advances


        摘要:Graph data are key parts of Big Data and widely used for modelling complex structured data with a broad spectrum of applications. Over the last decade, tremendous research efforts have been devoted to many fundamental problems in managing and analyzing graph data. In this talk, I will cover various applications, challenges, and recent advances covering graph matching, cohesive sub-graphs, similarities, and many other graph problems.


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        Yeh-Ching Chung

        香港中文大學深圳分校

        Bio:Yeh-Ching Chung received a B.S. degree in Computer Science from Chung Yuan Christian University in 1983, and the M.S. and Ph.D. degrees in Computer and Information Science from Syracuse University in 1988 and 1992, respectively. From 1992 to 2002, he was with the Department of Information Engineering and Computer Science at Feng Chia University, where he was an associate professor in 1992 and a full professor in 1999. From 1993 to 1997, he served as the director of Computer Network Division of Computer Center. From 1998 to 2001, he was the chairman of the department. From 2002 to 2016, he was with the Department of Computer Science at National Tsing Hua University as a full professor. From 2003 to 2012, he served as the deputy director of Library, where he established the first UHF RFID library system in Taiwan. In 2007, he founded Taiwan Association of Grid Computing (TAGC) that was renamed to Taiwan Association of Cloud Computing (TACC) in 2010. He was the direct general of TAGC/TACC from 2007 to 2011. He has supervised two National Tsing Hua University teams to win the champion of Student Cluster Competition, sponsored by IEEE/ACM SC conference, in 2010 and 2011. He has served as General Chairs, Program Chairs, Keynote Speakers, and Technical Committee Members of many international conferences. He was an awardee of Thousand Talents Plan of China in 2015. In 2016, he joined the Laboratory of Cloud Computing and Disaster Recovery Technology at Research Institute of Tsinghua University in Shenzhen as a deputy director. He is now a professor in School of Science and Engineering of Chinese University of Hong Kong in Shenzhen. His research interests include parallel and distributed processing, cloud computing, big data, and embedded systems. He has published over 200 journal and conference papers and developed many systems in these areas.


        講座題目:Graph Computing Technique for AI and Big Data Applications


        摘要:In this talk, we will show how to apply the graph computing technique to two applications, association rule mining and large-scale software system optimization. In association rule mining, the apriori method is the most commonly used approach to find the maximum frequent k-itemset. When the value of k is large, the apriori method is time consuming and it may not be able to get the result sometimes. We have proposed a hybrid method, ANG, by combining the graph computing and apriori methods for the maximum frequent k-itemset calculation. In ANG, when the value of k is small, the apriori method is used to calculate the maximum frequent k-itemset. When k is over a threshold, the graph computing method is used to calculate the maximum frequent k-itemset. The experimental results show that ANG outperforms the apriori method for all test cases.

        For the large-scale software system optimization, the main goal is to build up a program execution behavior monitoring and analyzing system. The system is an HPC system that integrates Cloud Computing, Distributed Storage, In-Memory Computing, Graph Computing, Compiler, Profiling Tools, and Data Mining techniques. When a large-scale software system is running, with the profiling tools, the execution behaviors of the software system can be recorded in real-time. With the real-time execution behavior records, different big data analytical methods can be used to optimize different desired parameters statically or dynamically. We have applied this system to optimized an LLVM compiler. The experimental results show that the proposed system has 5-10% performance improvement for the LLVM compiler.


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        鄒磊

        北京大學

        Bio:鄒磊,北京大學計算機科學技術研究所教授,國家自然科學基金優秀青年基金項目獲得者,北京大學大數據中心主任助理。鄒磊分別于2003年和2009年畢業于華中科技大學計算機科學與技術學院,獲得工學學士和工學博士學位;2009年9月加入北京大學計算機科學技術研究所。其博士學位論文獲得2009年中國計算機學會優秀博士學位論文提名獎和湖北省優秀博士論文獎。他目前的研究領域包括圖數據庫,RDF知識圖譜,尤其是基于圖的RDF數據管理,已經發表了50余篇國內外學術論文,包括CCF-A類的數據庫領域國際頂級期刊/會議論文(SIGMOD,VLDB等)近30篇;主持研發了面向知識圖譜數據的圖數據庫系統gStore和知識圖譜問答平臺gAnswer。2014年所主持的項目“海量圖結構數據存儲和查詢優化理論研究”,獲得中國計算機學會自然科學二等獎(鄒磊排名第一)。2017年所主持的項目“大規模圖結構數據管理”獲得教育部自然科學二等獎(鄒磊排名第一)。鄒磊承擔了包括國家自然基金、國家重點研發項目等多項國家科研攻關項目;其研究也得到了包括微軟、騰訊、阿里和方正電子等產業界公司的資助。


        講座題目:圖數據計算系統中的優化策略---兩個案例研究上的思考


        摘要:圖由于其表達的靈活性,目前被越來越多的應用選作描述實體之間復雜關系的重要數據結構;因此海量圖數據上的高效計算問題成為這些應用中亟待解決的關鍵技術挑戰。圍繞面向大規模圖數據的通用圖計算系統,我們認為有兩個方面的主要技術問題:其一,優化圖數據的表示方法:傳統的鄰接矩陣和鄰接表在例如高速變化的圖流系統中其空間代價和搜索計算代價都不能滿足圖流計算的需要;因此我們提出了一種圖流系統上的概率數據結構Graph Stream Sketch(GSS),既具有更好的時空性能,同時具有非常高的理論和實踐的圖計算準確度(邊、點和可達性查詢準確度大于99%)。其二,優化圖計算中的基礎算子:我們試圖去尋找圖計算中的一些常用的基礎算子,通過優化這些算子從而提高整體圖計算系統的性能。我們在前期工作中發現“集合交集計算”普遍存在于圖計算任務中并且占據了較大的計算代價,包括極大團發現,圖上的三角形計數,子圖匹配,社區發現等。為此,我們提出在圖計算的環境下,提出基于SIMD指令的算法加速這些圖計算中的集合求交操作。實驗表明我們可以在現有方法(包括不基于SIMD和基于SIMD的圖計算系統)的基礎上提高3-10倍的圖計算性能。

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        Bingsheng He

        National University of Singapore

        Bio:Dr. Bingsheng He is currently an Associate Professor at Department of Computer Science, National University of Singapore. Before that, he was a faculty member in Nanyang Technological University, Singapore (2010-2016), and held a research position in the System Research group of Microsoft Research Asia (2008-2010), where his major research was building high performance cloud computing systems for Microsoft. He got the Bachelor degree in Shanghai Jiao Tong University (1999-2003), and the Ph.D. degree in Hong Kong University of Science & Technology (2003-2008). His current research interests include cloud computing, database systems and high performance computing. His papers are published in prestigious international journals (such as ACM TODS and IEEE TKDE/TPDS/TC) and proceedings (such as ACM SIGMOD, VLDB/PVLDB, ACM/IEEE SuperComputing, ACM HPDC, and ACM SoCC). He has been awarded with the IBM Ph.D. fellowship (2007-2008) and with NVIDIA Academic Partnership (2010-2011). Since 2010, he has (co-)chaired a number of international conferences and workshops, including IEEE CloudCom 2014/2015 and BigData Congress 2018. He has served in editor board of international journals, including IEEE Transactions on Cloud Computing (IEEE TCC), IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS) and Springer Journal of Distributed and Parallel Databases (DAPD).


        講座題目:Parallel Graph Processing on GPUs


        摘要:Graphs are de facto data structures for many data processing applications. Many graph processing tasks are computation intensive and/or memory intensive. Therefore, we have witnessed a significant amount of effort in accelerating graph processing tasks with GPUs to take advantage of their massive parallelism and high memory bandwidth. In this talk, we will first review the literatures of parallel graph processing systems on GPUs. Next, we present our research efforts, and demonstrate the significant performance impact of hardware-conscious designs in parallel graph processing systems. Finally, we outline the research agenda of an on-going project named MetalDB towards an “One Size Fits All” design across heterogeneous hardware. More details about our research can be found at http://www.comp.nus.edu.sg/~hebs/.


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        李超

        上海交通大學

        Bio:李超,上海交通大學Tenure-Track特別研究員,博士生導師,主要從事面向新應用新需求的計算機體系結構設計研究。在包括TC、TPDS、CSUR、ISCA、HPCA、MICRO等高水平學報和會議上發文40余篇,獲得2011年頂級會議HPCA的最佳論文獎和2015年SCI期刊IEEE CAL的最佳論文獎。近五年申請中美專利十余項,已授權5項,相關成果獲得Facebook Fellowship,Yahoo! 重點科技挑戰獎,國家優秀自費留學生獎。2016年入選CCF Intel青年學者提升計劃,2017年入選CCF青年人才發展計劃,2018年在MSRA參與“鑄星計劃”訪問研究。CCF體系結構專委會常委,高性能計算專委會委員,CCF YOCSEF上海副主席,2018年擔任第十二屆中國計算機體系結構學術年會ACA的程序委員會主席。本科畢業于浙江大學,博士畢業于佛羅里達大學。


        講座題目:圖計算的體系結構設計啟示


        摘要:隨著圖計算應用日益受到關注,計算機底層系統結構也需要不斷改進以滿足圖計算對執行效率的需求。此次報告主要著眼于圖數據處理類負載在體系結構設計方面的影響和啟示(implications)。我們首先從硬件架構出發,探討圖計算負載特征對實現高效能圖計算的重要性,綜述近年來的相關研究發現;隨后,我們重點介紹目前圖計算系統面臨的硬件資源管理問題,尤其是對“內存”和“功耗”這兩大體系結構瓶頸進行分析。最后,我們結合時下若干代表性設計思想,對圖計算機底層設計優化的挑戰和機遇做以簡要歸納總結。


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        錢正平

        阿里巴巴

        Bio:錢正平博士是阿里巴巴大數據計算平臺的工程總監。他帶領團隊負責圍繞阿里巴巴內外諸如圖計算、機器學習等新興應用的系統研發和商業解決方案構建。2015年加入阿里之前,他是微軟亞洲研究院的主管研究員。研究興趣是分布式和數據并行計算。錢博士2009年畢業于華南理工大學,獲得博士學位。


        講座題目:圖分析與數據智能技術及在阿里巴巴的應用


        摘要:作為全球最大的電子商務平臺,今天的阿里巴巴深度依賴海量數據的實時分析,由此驅動商業決策。圖數據自然的表達了大規模、多樣化數據間的復雜關聯,易于挖掘隱藏在其中的模式,從而為用戶提供更精準、可靠的信息和發現商業機會。本演講介紹圖分析在阿里巴巴的應用,包括圖數據管理與分析等的關鍵技術與系統抽象。同時結合真實場景的生產實踐,從算法和系統兩個方面,總結重要的研究問題和工程挑戰。

        報名須知:

        1、報名費:CCF會員2500元,非會員3000元。開班現場報名,需繳納報名費4000元(僅支持公務卡,銀行卡,不收取現金)。食宿交通自理。根據交費先后順序,會員優先的原則錄取,額滿為止。
        給予西部五所高校兩個名額,可免費,限CCF會員, 需個人提出書面申請并加蓋院系公章,將電子版發至[email protected], CCF將按照申請順序進行錄取。 (五所高校的名單如下:新疆大學,青海大學,云南大學,貴州大學,寧夏大學。)
        2、報名截止及繳費說明:

        (1)報名截止日期:2018年11月18日。報名請預留不會攔截外部郵件的郵箱。
        (2)CCF會員報名,請務必在報名表中填寫在有效期內的CCF會員號。否則按非會員處理;
        3、聯系:李紅梅  
        郵箱 : [email protected]  電話:18810669757



        时时彩qq计划群2018

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