In Computer Architecture, We Don't Change the Questions, We Change the Answers

Mark D. Hill, Microsoft Azure and University of Wisconsin-Madison

When I was a new professor in the late 1980s, my senior colleague Jim Goodman told me, "On the computer architecture PhD qualifying exam, we don't change the questions, we only change the answers." More generally, I now augment this to say, "In computer architecture, we don't change the questions, application and technology innovations change the answers, and it's our job to recognize those changes." Eternal questions this talk will sample are how best to do the following interacting factors: compute, memory, storage, interconnect/networking, security, power, cooling and one more. The talk will not provide the answers but leave that as an audience exercise. I will dive a little more into compute and memory as in-progress trends provide both challenges and opportunities for creating tremendous value from (large) data.

The slides are available on Mark D. Hill's web page

Mark D. Hill is Partner Hardware Architect with Microsoft Azure (2020-present) where he leads software-hardware pathfinding. He is also the Gene M. Amdahl and John P. Morgridge Professor Emeritus of Computer Sciences at the University of Wisconsin-Madison (https://www.cs.wisc.edu/~markhill/), following his 1988-2020 service in Computer Sciences and Electrical and Computer Engineering. His research interests include parallel-computer system design, memory system design, and computer simulation. Hill's work is highly collaborative with over 160 co-authors. He received the 2019 Eckert-Mauchly Award and is a fellow of AAAS, ACM, and IEEE. He served on the Computing Community Consortium (CCC) 2013-21 including as CCC Chair 2018-20, Computing Research Association (CRA) Board of Directors 2018-20, and Wisconsin Computer Sciences Department Chair 2014-2017. Hill has a PhD in computer science from the University of California, Berkeley.

Fresh Thinking talks

A series of invited talks by younger researchers.

• Accelerating Video Database Systems using Emerging Hardware Technologies

Joy Arulraj (Georgia Institute of Technology)

Over the last decade, advances in deep learning have led to a resurgence of interest in automated analysis of videos at scale. This approach poses many challenges, ranging from the high computational overhead associated with deep learning models to the types of queries that the user may ask. In this talk, I will present EVA, an end-to-end video database system that we are developing at Georgia Tech, for tackling these challenges using novel query optimization and machine learning techniques. I will then discuss about opportunities for the community to help accelerate video database systems using their expertise in leveraging emerging hardware technologies.

Joy Arulraj is an Assistant Professor of Computer Science at the Georgia Institute of Technology. His research focuses on developing systems for efficiently and effortlessly querying video datasets by synthesizing techniques from data systems and machine learning. His research has been recognized with the IEEE TCDE Early Career Award (2022) and the ACM SIGMOD Jim Gray Doctoral Dissertation Award (2019). His group is supported by funding from the NSF, Cisco Research, Alibaba, Adobe, and Intel.

• What the Primacy of Economics Means for Hardware And Software

Viktor Leis (Friedrich-Alexander-Universität Erlangen-Nürnberg)

Using several historical examples, I will argue that both hardware and software is downstream from economics. Economics has been called "the dismal science" and harsh economic realities can prevent technological breakthroughs. At the same time, however, economic thinking can also help overcome seemingly inescapable tradeoffs that we face when building software systems. It may also be our only hope for managing the proliferation of complex heterogeneous hardware, in particular in the cloud.

Viktor Leis is a Professor for Data Management at Friedrich-Alexander University Erlangen-Nürnberg, Germany. His research revolves around designing high-performance data management systems and includes core database topics such as query processing, query optimization, index structures, and storage. Viktor received his doctoral degree in 2016 from the Technical University of Munich, where he worked on the main-memory database system HyPer. He is the recipient of the ACM SIGMOD dissertation award (2018), the IEEE TCDE Rising Star Award, and four best paper awards at ICDE and SIGMOD.