CS Colloquium Series

Speaker: Pat Hanrahan, Stanford University
Date: Monday, November 14
Time: 4:30pm
Location: Friend Center, room 101
Host: Adam Finkelstein
Event page: https://www.cs.princeton.edu/events/26268

Title: Shading Languages and the Emergence of Programmable Graphics Systems

Abstract: A major challenge in using computer graphics for movies and games is to create a rendering system that can create realistic pictures of a virtual world. The system must handle the variety and complexity of the shapes, materials, and lighting that combine to create what we see every day. The images must also be free of artifacts, emulate cameras to create depth of field and motion blur, and compose seamlessly with photographs of live action.
Pixar's RenderMan was created for this purpose, and has been widely used in feature film production. A key innovation in the system is to use a shading language to procedurally describe appearance. Shading languages were subsequently extended to run in real-time on graphics processing units (GPUs), and now shading languages are widely used in game engines. The final step was the realization that the GPU is a data-parallel computer, and the the shading language could be extended into a general-purpose data-parallel programming language. This enabled a wide variety of applications in high performance computing, such as physical simulation and machine learning, to be run on GPUs. Nowadays, GPUs are the fastest computers in the world. This talk will review the history of shading languages and GPUs, and discuss the broader implications for computing.

Bio: Pat Hanrahan is the Canon Professor of Computer Science and Electrical Engineering in the Computer Graphics Laboratory at Stanford University. His research focuses on rendering algorithms, graphics systems, and visualization.

Hanrahan received a Ph.D. in biophysics from the University of Wisconsin-Madison in 1985. As a founding employee at Pixar Animation Studios in the 1980s, Hanrahan led the design of the RenderMan Interface Specification and the RenderMan Shading Language. In 1989, he joined the faculty of Princeton University. In 1995, he moved to Stanford University. More recently, Hanrahan served as a co-founder and CTO of Tableau Software.  He has received three Academy Awards for Science and Technology, the SIGGRAPH Computer Graphics Achievement Award, the SIGGRAPH Stephen A. Coons Award, and the IEEE Visualization Career Award. He is a member of the National Academy of Engineering and the American Academy of Arts and Sciences. In 2019, he received the ACM A. M. Turing Award.

Speaker: Richard L. Sites
Date: Tuesday, November 15
Time: 12:30pm
Location: CS 105
Host: Brian Kernighan
Event page: https://www.cs.princeton.edu/events/26269 

Title: Making the Invisible Visible: Observing Complex Software Dynamics

Abstract: From mobile and cloud apps to video games to driverless vehicle control, more and more software is time-constrained: it must deliver reliable results seamlessly, consistently, and virtually instantaneously. If it doesn't, customers are unhappy--and sometimes lives are put at risk. When complex software underperforms or fails, identifying the root causes is difficult and, historically, few tools have been available to help, leaving application developers to guess what might be happening. How can we do better?
The key is to have low-overhead observation tools that can show exactly where all the  elapsed time goes in both normal responses and in delayed responses. Doing so makes visible each of the seven possible reasons for such delays, as we show.

Bio: Richard L. Sites wrote his first computer program in 1959 and has spent most of his career at the boundary between hardware and software, with a particular interest in CPU/software performance interactions. His past work includes VAX microcode, DEC Alpha co-architect, and inventing the performance counters found in nearly all processors today. He has done low-overhead microcode and software tracing at DEC, Adobe, Google, and Tesla. Dr. Sites earned his PhD at Stanford in 1974; he holds 66 patents and is a member of the US National Academy of Engineering.

Speaker: Luke Zettlemoyer, University of Washington
Date: Thursday, November 17
Time: 12:30pm
Location: Friend Center Convocation room
Host: Danqi Chen

Title: Large Language Models: Will they keep getting bigger? And, how will we use them if they do?

Abstract: The trend of building ever larger language models has dominated much research in NLP over the last few years. In this talk, I will discuss our recent efforts to (at least partially) answer two key questions in this area: Will we be able to keep scaling? And, how will we actually use the models, if we do? I will cover our recent efforts on learning new types of sparse mixtures of experts (MoEs) models. Unlike model-parallel algorithms for learning dense models, which are very difficult to further scale with existing hardware, our sparse approaches have significantly reduced cross-node communication costs and could possibly provide the next big leap in performance, although finding a version that scales well in practice remains an open challenge. I will also present our recent work on prompting language models that better controls for surface form variation, to improve performance of models that are so big we can only afford to do inference, with little to no task-specific fine tuning. Finally, time permitting, I will discuss work on new forms of supervision for language model training, including learning from the hypertext and multi-modal structure of web pages to provide new signals for both learning and prompting the model. Together, these methods present our best guesses for how to keep the scaling trend alive as we move forward to the next generation of NLP models. 
This talk describes work done at the University of Washington and Meta, primarily led by Armen Aghajanyan, Suchin Gururangan, Ari Holtzmann, Mike Lewis, Margaret Li, Sewon Min, and Peter West. 

Bio: Luke Zettlemoyer is a Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, and a Research Director at Meta. His research focuses on empirical methods for natural language semantics, and involves designing machine learning algorithms, introducing new tasks and datasets, and, most recently, studying how to best develop self-supervision signals for pre-training. His honors include being named an ACL Fellow as well as winning a PECASE award, an Allen Distinguished Investigator award, and multiple best paper awards. Luke received his PhD from MIT and was a postdoc at the University of Edinburgh.