Uploaded February 2026 | Updated September 2026, 2 weeks ago
Title: Productively Programming Accelerated Computing Systems
Speaker: Rohan Yadav (Stanford)
Date: Tuesday, February 17, 2026
Abstract: Modern accelerated computing systems are increasing in scale, becoming more specialized and diverse, and evolving more quickly. While these changes bring significant performance improvements, they also come with the challenges of productively developing software that targets complex and rapidly changing hardware. For software to keep up with modern hardware, programming systems must also evolve to provide new levels of abstraction, portability and composability. In this talk, I will focus on two pieces of work that advance programming systems along these axes.
First, I will discuss a connection between actor-based and task-based programming models, two popular classes of programming models for distributed and accelerated machines. Task-based models provide high-level abstractions over the underlying hardware that enable composability and portability, while actor-based models expose a lower-level interface that offers the best performance. I will show that these two families of programming models are duals of each other, and then leverage this duality to close the performance gap between the models by compiling task-based programs into efficient actor-based programs.
Second, I will discuss Twill, a system that automatically discovers optimal software pipelining (SWP) and warp specialization (WS) strategies for Tensor Core GPUs. Optimal strategies for SWP and WS continue to change across modern GPU generations and are currently derived through expert intuition and compiler heuristics. We show that these strategies are derivable from first-principles in a machine-parametrizable and heuristic-free manner, and re-discover strategies found by experts.
Bio: Rohan Yadav is a final-year computer science Ph.D. student at Stanford University, advised by Alex Aiken and Fredrik Kjolstad, as well as a part-time researcher at NVIDIA. He is generally interested in programming languages and computer systems, with a focus in systems for parallel and accelerated computing.
This video is closed captioned.
Title: Productively Programming Accelerated Computing Systems
Speaker: Rohan Yadav (Stanford)
Date: Tuesday, February 17, 2026
Abstract: Modern accelerated computing systems are increasing in scale, becoming more specialized and diverse, and evolving more quickly. While these changes bring significant performance improvements, they also come with the challenges of productively developing software that targets complex and rapidly changing hardware. For software to keep up with modern hardware, programming systems must also evolve to provide new levels of abstraction, portability and composability. In this talk, I will focus on two pieces of work that advance programming systems along these axes.
First, I will discuss a connection between actor-based and task-based programming models, two popular classes of programming models for distributed and accelerated machines. Task-based models provide high-level abstractions over the underlying hardware that enable composability and portability, while actor-based models expose a lower-level interface that offers the best performance. I will show that these two families of programming models are duals of each other, and then leverage this duality to close the performance gap between the models by compiling task-based programs into efficient actor-based programs.
Second, I will discuss Twill, a system that automatically discovers optimal software pipelining (SWP) and warp specialization (WS) strategies for Tensor Core GPUs. Optimal strategies for SWP and WS continue to change across modern GPU generations and are currently derived through expert intuition and compiler heuristics. We show that these strategies are derivable from first-principles in a machine-parametrizable and heuristic-free manner, and re-discover strategies found by experts.
Bio: Rohan Yadav is a final-year computer science Ph.D. student at Stanford University, advised by Alex Aiken and Fredrik Kjolstad, as well as a part-time researcher at NVIDIA. He is generally interested in programming languages and computer systems, with a focus in systems for parallel and accelerated computing.
This video is closed captioned.







![[Audio Descriptions] Faculty In Focus: Natasha Jaques
In the inaugural episode of the Allen School’s “Faculty in Focus” series, Assistant Professor Natasha Jaques describes her research in artificial intelligence aimed at building better AI agents. Jaques’ work draws from deep reinforcement learning and game theory to develop new approaches for ensuring that large language models like ChatGPT will be both effective and safe for users to interact with — regardless of the input.
For a version without audio descriptions, visit https://youtu.be/U2Xc3Ab_Has [Audio Descriptions] Faculty In Focus: Natasha Jaques](https://i.ytimg.com/vi/gqb-445BiTw/mqdefault.jpg)
![[Audio Descriptions] I Am CSE: Ather Sharif
Allen School graduate student Ather Sharif of the UW’s ACE Lab and DUB Group describes his work on VoxLens, a tool that makes online data visualizations accessible to people who use screen readers, and explains why UW is the best place to do accessibility research.
This video is closed captioned.
A version of this video without audio descriptions is available here: https://youtu.be/oSBbE5PMKQs. [Audio Descriptions] I Am CSE: Ather Sharif](https://i.ytimg.com/vi/gtMoJAwW-Ms/mqdefault.jpg)

![[Audio Descriptions] Faculty In Focus: Stephanie Wang
In this episode of the Allen School’s “Faculty in Focus” series, Assistant Professor Stephanie Wang talks about her research aimed at designing computer systems that can support advanced machine learning applications. The goal is to build infrastructure that is both scalable and future-proof — while also making it more accessible to a wider variety of people interested in developing and deploying state-of-the-art systems.
For a version without audio descriptions, visit https://youtu.be/6zgmbVEgm9o [Audio Descriptions] Faculty In Focus: Stephanie Wang](https://i.ytimg.com/vi/i6WuWgrUN5M/mqdefault.jpg)