主讲人：王翕（清华大学）

　　

　　摘要:

　　Emerging data-intensive applications, such as graph analytics and data mining, exhibit tremendous datasets and irregular memory access patterns. Research has shown that these memory-bound applications are unable to effectively leverage the principles of data locality and regular memory accesses within traditional cache-based memory systems to mitigate the “memory-wall” issue. The expansion of data volume has simultaneously driven a transition from monolithic architectures towards systems integrated with discrete and distributed nodes in large-scale computing systems. As such, multi-layered software infrastructures have become essential to bridge the gap between heterogeneous commodity devices. However, operations across synthesized components with divergent interfaces inevitably lead to redundant software footprints and undesired latency. Therefore, a scalable and unified computing platform, capable of supporting efficient interactions between individual components, is desirable for large-scale data-intensive applications. 

　　In our presentation, we will unveil the Extended Base Global Address Space, or xBGAS, a novel extension for the RISC-V instruction set architecture (ISA), designed for scalable and high-performance computing. The xBGAS extension revolutionizes the landscape by providing native ISA-level support for direct remote shared memory access, achieved through the mapping of remote objects into an extended system address space. Further, we will walk through the performance ramifications of xBGAS through an insightful examination of both software and hardware, employing a wide range of data-intensive workloads.

　　

　　个人简介：

　　王翕博士，清华大学博士后研究员，德克萨斯理工大学客座研究科学家 (Adjunct Research Scientist), 专注于计算机体系结构的科研工作。于2020年在美国德克萨斯理工大学取得计算机科学博士学位， 拥有超过8年RISC-V体系结构设计经验，含括处理器设计，高性能计算，并行计算，编译器，二进制转译，敏捷开发工具链等领域的科研工作。参与/主持多项由美国国家自然科学基金，美国国防部，美国能源部，深圳市科创委， RISC-V国际基金会资助的科研项目。科研成果在 DAC, IPDPS, HPDC, ICPP, TC, TOCS等国际顶级会议和期刊上发表论文，并荣获IPDPS 2021年度最佳论文奖，ISSCC 2023 Code-a-Chip芯片设计奖等国际学术会议奖项。科研成果转化被美国西北太平洋国家实验室 (PNNL),  阿贡国家实验室 (ANL), 劳伦斯伯克利国家实验室(LBNL), RISC-V国际基金会, 美光科技, 大众, 英特尔, 谷歌, 等机构和企业采纳使用。