报告题目：Enabling Efficient and Scalable Parallelization for Data-Intensive Computations

报告时间：2024年5月22日15:00-16:00

报告地点：新葡萄8883官网AMG大楼B405

报告人：邱俊乔

报告人国籍：中国

报告人单位：香港城市大学

报告人简介：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.

邱俊乔博士是香港城市大学计算机科学系的助理教授。在加入城市大学之前，他曾在密歇根理工大学担任助理教授，并在加利福尼亚大学河滨分校获得博士学位。他的研究兴趣涵盖编译器和系统领域，重点关注为数据密集型应用和具有不规则数据访问模式的应用实现高效的并行计算。他曾获得ACM SIGPLAN PAC奖、NSF CRII奖和ASPLOS 2020最佳论文奖。

报告摘要：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.

邀请人：李清安、袁梦霆