報(bào)告題目：On Optimizing Mobile Memory and Storage
報(bào)告時間：2024年5月22日14:00-15:00
報(bào)告地點(diǎn)：437bwin必贏國際官網(wǎng)大樓B405
報(bào)告人：薛春
報(bào)告人國籍：中國
報(bào)告人單位：穆罕默德·本·扎耶德人工智能大學(xué)
報(bào)告人簡介：Prof. Chun Jason Xue is a professor at the Department of Computer Science, MBZUAI, Abu Dhabi. He received Ph.D. in Computer Science from the University of Texas at Dallas in 2007 and joined the City University of Hong Kong in the same year. He is currently an associate editor of ACM Transactions on Embedded Computing Systems, ACM Transactions on Storage, and ACM Transactions on CPS.
He has served/serves as General Chair, Program Chair, and Program Committee Member on a number of technical conferences and workshops. He is currently the Steering Committee Chair of ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES) since 2020.
His research interest includes system software for memory and storage optimizations, considering mobile and embedded platforms, with a focus on memory technologies such as non-volatile memories and flash memories.
薛春教授是穆罕默德·本·扎耶德人工智能大學(xué)（MBZUAI）計(jì)算機(jī)科學(xué)系的教授。他于2007年獲得德克薩斯大學(xué)達(dá)拉斯分校的計(jì)算機(jī)科學(xué)博士學(xué)位，并于同年加入香港城市大學(xué)。他目前是ACM Transactions on Embedded Computing Systems、ACM Transactions on Storage和ACM Transactions on CPS的副編輯。他曾擔(dān)任多個技術(shù)會議和研討會的大會主席、程序主席和程序委員會成員。自2020年以來，他一直擔(dān)任ACM SIGPLAN/SIGBED會議LCTES的指導(dǎo)委員會主席。他的研究興趣包括面向移動和嵌入式平臺的內(nèi)存和存儲優(yōu)化系統(tǒng)軟件，重點(diǎn)關(guān)注非易失性內(nèi)存和閃存等內(nèi)存技術(shù)。
報(bào)告摘要：Current mobile operating systems, such as Android, inherit the Linux kernel. As a result, system software designs that were targeted for servers are now applied in mobile devices. In this series of work, through analyzing mobile application characteristics on files, memory, and storage usage, we found that mobile applications have their own unique characteristics which differ from applications on servers. These differences present new optimization opportunities in mobile memory and storage management. In this talk, I will present several mobile memory and storage management-related works that improve user experience on mobile devices based on mobile application characterization.
邀請人：李清安、袁夢霆
報(bào)告題目：Enabling Efficient and Scalable Parallelization for Data-Intensive Computations
報(bào)告時間：2024年5月22日15:00-16:00
報(bào)告地點(diǎn)：437bwin必贏國際官網(wǎng)大樓B405
報(bào)告人：邱俊喬
報(bào)告人國籍：中國
報(bào)告人單位：香港城市大學(xué)
報(bào)告人簡介：Dr. Junqiao QIU is an Assistant Professor in the Department of Computer Science at City University of Hong Kong. Prior to joining CityU, he was a tenure-track assistant professor at Michigan Technological University and earned his Ph.D. from the University of California Riverside. His research interests span the areas of compilers and systems, with a focus on enabling efficient parallel computing for data-intensive applications and those with irregular data access patterns. He is a recipient of the ACM SIGPLAN PAC Award, the NSF CRII Award, and the Best Paper Award at ASPLOS 2020.
邱俊喬博士是香港城市大學(xué)計(jì)算機(jī)科學(xué)系的助理教授。在加入城市大學(xué)之前，他曾在密歇根理工大學(xué)擔(dān)任助理教授，并在加利福尼亞大學(xué)河濱分校獲得博士學(xué)位。他的研究興趣涵蓋編譯器和系統(tǒng)領(lǐng)域，重點(diǎn)關(guān)注為數(shù)據(jù)密集型應(yīng)用和具有不規(guī)則數(shù)據(jù)訪問模式的應(yīng)用實(shí)現(xiàn)高效的并行計(jì)算。他曾獲得ACM SIGPLAN PAC獎、NSF CRII獎和ASPLOS 2020最佳論文獎。
報(bào)告摘要：Exploiting parallelism is crucial for achieving high-performance data processing on modern processors. However, many data processing routines still run serially due to the sequential nature of their underlying computation models. In this presentation, I will demonstrate how to effectively break inherent data dependencies and enable scalable and efficient data-parallel processing.
I will begin by introducing our previous work on using speculation to auto-parallelize bitstream processing applications. Following this, I will discuss our ongoing projects that push the boundaries of speculative parallelization. These include leveraging non-SIMD vector instructions to accelerate speculative parallelization, integrating speculation into pattern-aware graph mining applications, and enabling efficient concurrent GPU-based inferences.
Finally, I will conclude the talk by sharing my ideas on parallelizing more general applications, aiming to broaden the applicability of these techniques.
邀請人：李清安、袁夢霆
