Balancing Heterogeneity and Programmability Across Computing Scales

Hardware heterogeneity is everywhere, from the high-performance server chips that comprise our data centers to the milliwatt-scale chips on board our biomedical devices. The central thesis of my talk is that hardware heterogeneity breaks through traditional computing abstractions to enable orders of magnitude performance improvements, but that these performance improvements are useful to software developers only when hardware continues to remain easy to program. I will discuss ongoing research in my group on balancing hardware heterogeneity with abstractions/interfaces to enable programmability/flexibility. As exemplars of this question, I will focus on the benefits and challenges of building shared address spaces between general-purpose CPUs and domain-specific hardware accelerators. I will also discuss my work on building flexible neural interfaces driven by a collection of programmable ASICs. My talk will highlight cross-cutting lessons learned and their implications on future accelerator-rich computer systems.

Bio: Abhishek Bhattacharjee is a Professor of Computer Science at Yale University, and is also affiliated with Yale's Wu Tsai Institute for the Brain Sciences as well as Yale's Center for Brain & Mind Health. He is interested in the hardware/software interface. Abhishek's research on address translation has shipped in over one billion AMD Zen CPU cores, over tens of millions of NVIDIA GPUs, over two billion Linux kernel downloads, and has also helped the group tasked with deciding the RISC-V page table format. For these contributions, Abhishek was the recipient of the 2023 ACM SIGARCH Maurice Wilkes Award. Abhishek teaches courses on computer architecture, operating systems, and compilers. In recognition of his teaching and mentoring of undergraduate and graduate students, Abhishek was the recipient of the 2022 Yale Engineering Ackerman Award.

---

To request accommodations for a disability, please contact Emily Lawrence at emilyl@cs.princeton.edu at least one week prior to the event.
Live stream only available to Princeton University students, faculty, and staff.  Webinar registration here.