- talks - lists.cs.princeton.edu

Aninda Manocha will present her FPO "Optimizing Data Supply and Memory Management for Graph Applications in Post-Moore Hardware-Software Systems" on Monday, May 8th, 2023 at 3pm in CS 105
by Nicki Mahler 07 Apr '23

07 Apr '23

Aninda Manocha will present her FPO "Optimizing Data Supply and Memory Management for Graph Applications in Post-Moore Hardware-Software Systems" on Monday, May 8th, 2023 at 3pm in CS 105. The members of her committee are as follows: Margaret Martonosi (Adviser); Readers: Kai Li and Luca Carloni (Columbia University); Examiners: David Wentzlaff and David August Everyone is invited to attend her talk. Title: "Optimizing Data Supply and Memory Management for Graph Applications in Post-Moore Hardware-Software Systems" Abstract: Graph structures naturally and efficiently capture relationships between entities, such as individuals in a social network, pages in the World Wide Web, and amino acids in protein molecules. However, despite the advances in computing over the past few decades, graph processing algorithms continue to pose data supply challenges for current hardware. Modern networks are sparse and evergrowing, and therefore many graph applications are characterized by irregular memory access patterns that trouble data supply and memory management schemes. As a result, these applications are severely bottlenecked by several long-latency memory accesses to off-chip memory. My dissertation aims to tackle these bottlenecks with three main thrusts. First, this dissertation offers a hardware-software co-design that pairs automated compiler techniques to slice programs along bottleneck memory accesses with modest architectural modifications. This approach, GraphAttack, achieves a 2.9x speedup and 8.6x gain in energy efficiency over modern latency tolerance approaches. Second, this dissertation proposes tailored cache management policies supported with flexible hardware to improve memory bandwidth pressure and minimize off-chip memory accesses. This domain-specific memory hierarchy approach, Graphfire, yields a 1.3x speedup over state-of-the-art cache management policies and 63.3x speedup when scaling up to 64 cores. Third, this dissertation introduces architectural support for operating system (OS) techniques to efficiently identify data worth backing with huge pages and alleviate significant address translation overheads that occur in irregular applications. This results in 1.2-1.4x speedups over base pages alone when backing only 1-4% of the application footprint with huge pages. The end of Moore’s Law has pushed computer architects to design and develop hardware and software to accelerate compute and aim for aggressive power and performance targets. Despite these advances, data supply remains a challenge, particularly for memory-bound graph applications. Embracing hardware-software co-designs, my dissertation attacks the memory bottlenecks in graph processing workloads from multiple perspectives of the hardware-software stack to enable optimal performance. The techniques presented demonstrate the need to design properly equipped in-order technology and provide a path forward for tailored memory management for irregular applications in order to target a world where energy savings are critical for performance and scalability.

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Decenter Seminar: Joachim Matthias Neu, Monday, March 27 at 3:30
by Emily C. Lawrence 07 Apr '23

07 Apr '23

Decenter Seminar Speaker: Joachim Matthias Neu, Stanford University Day: Monday, March 27, 2023 Time: 3:30-4:30pm Location: CS 105 Host: Pramod Viswanath Title: Internet-Scale Consensus In The Blockchain Era Abstract: Blockchains have ignited interest in Internet-scale consensus as a fundamental building block for decentralized applications and services, which in turn promise more egalitarian access and improved robustness to faults and abuse. While consensus has been studied in distributed systems for decades, Internet-scale consensus requires a fundamental rethinking of models, desiderata, and protocols. Participants are no longer a handful of computers in one company's data centers, but numerous mistrustful entities distributed across the Internet. I will discuss two examples of challenges and solutions for this new setting: (1) Ethereum, the second largest blockchain, aims to strengthen consensus liveness under open participation where parties come and go at will, and to strengthen safety to enable accountability in case of any safety violation. However, we show that no traditional single-ledger protocol can satisfy both strengthened properties simultaneously. To resolve this dilemma, we develop the multi-ledger consensus paradigm that is now the security design-specification for Ethereum. A by-product of this work are attacks on Ethereum's consensus protocol that prompted design changes. (2) Traditional network models do not capture rate constraints on communication and processing, leaving popular "provably secure" protocols vulnerable to attack. We show via a new queuing-based model how to schedule message handling securely. Our policies are simple enough to be forward-deployed at Internet service providers via a system that can also protect traffic of applications beyond blockchains. Bio: Joachim Neu is a PhD candidate at Stanford University with David Tse. His research focuses on the science and engineering of Internet-scale consensus as a fundamental building block for decentralized systems, using tools from distributed systems, applied probability, networking and communications, and applied cryptography. While a Masters student at Technical University of Munich, he worked in information and coding theory. Joachim has received the Protocol Labs PhD Fellowship and the Stanford Graduate Fellowship.

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CS Colloquium Speaker Mae Milano TODAY at 12:30pm
by Emily C. Lawrence 06 Apr '23

06 Apr '23

Speaker: Mae Milano, University of California, Berkeley Date: Thursday, April 6 Time: 12:30pm EST Location: CS 105 Host: Andrew Appel Event page: https://www.cs.princeton.edu/events/26344 Title: Programming Distributed Systems Abstract: Our interconnected world is increasingly reliant on distributed systems of unprecedented scale, serving applications which must share state across the globe. And, despite decades of research, we're still not sure how to program them! In this talk, I'll show how to use ideas from programming languages to make programming at scale easier, without sacrificing performance, correctness, or expressive power in the process. We'll see how slight tweaks to modern imperative programming languages can provably eliminate common errors due to replica consistency or concurrency---with little to no programmer effort. We'll see how new language designs can unlock new systems designs, yielding both more comprehensible protocols and better performance. And we'll conclude by imagining together the role that a new cloud-centric programming language could play in the next generation of distributed programs. Bio: Mae Milano is a postdoctoral scholar at UC Berkeley working at the intersection of Programming Languages, Distributed Systems, and Databases. Her work has appeared at top-tier venues including PLDI, OOPSLA, POPL, VLDB, and TOCS, and has attracted the attention of the Swift language team. She is a recipient of the NDSEG Fellowship, has won several awards for her writing and service, and is a founding member of the Computing Connections Fellowship's selection committee (https://computingconnections.org/).

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Yuting Yang will present her FPO "Exploiting Program Representation with Shader Applications" on Wednesday, May 3, 2023 at 3:00 PM in CS 402.
by CS Grad Department 05 Apr '23

05 Apr '23

Yuting Yang will present her FPO "Exploiting Program Representation with Shader Applications" on Wednesday, May 3, 2023 at 3:00 PM in CS 402. Location: CS 402 The members of Yuting’s committee are as follows: Examiners: Adam Finkelstein (Adviser), Szymon Rusinkiewicz, Jia Deng Readers: Felix Heide, Connelly Barnes (Adobe Research) A copy of her thesis will be available upon request, two weeks before the FPO. Please email [ mailto:gradinfo@cs.princeton.edu | gradinfo(a)cs.princeton.edu ] if you would like a copy of the thesis. Everyone is invited to attend her talk. Abstract follows below: Programs are widely used in content creation. For example, artists design shader programs to procedurally render scenes and textures, while musicians construct “synth” programs to generate electronic sound. While the generated content is typically the focus of attention, the programs themselves offer hidden potential for transformations that can support untapped applications. In this thesis, we will discuss four projects that exploit the program structure to automatically apply machine learning or math transformations as if they were manually designed by domain experts. First, we propose a compiler framework to automatically approximate the convolution of an arbitrary program with a Gaussian kernel in order to smooth the program for visual antialiasing. Second, we explore the benefit of program representation in deep-learning tasks by proposing to learn from program traces of procedural fragment shaders – programs that generate images. We next describe a compiler-based framework with novel math rules to extend reverse mode automatic differentiation so as to provide accurate gradients for arbitrary discontinuous programs. Finally, we extend the differentiation framework to audio “synth” programs so as to match the acoustic properties of a provided sound clip. In each of these settings, we demonstrate the benefit of exploiting the program structure to generalize hand-crafted techniques to arbitrary programs.

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Princeton Robotics Seminar: Scott Kuindersma, Friday April 7
by Emily C. Lawrence 04 Apr '23

04 Apr '23

Speaker: Scott Kuindersma, Senior Director of Robotics Research, Boston Dynamics Date: Friday, April 7, 2023 Time: 11am ET Location: Computer Science 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Taskable Agility: Making Useful Dynamic Behavior Easier to Create Abstract: In this talk, I will provide some insights and observations from our recent work on Atlas, the world's most dynamic humanoid robot. I'll cover the core technical ideas that have made an impact for us over the past few years and share my thoughts about the future for robots like Atlas. Bio: Scott Kuindersma is the Senior Director of Robotics Research at Boston Dynamics where he leads behavior research on Atlas. Prior to joining Boston Dynamics, he was an Assistant Professor of Engineering and Computer Science at Harvard. Scott’s research explores intersections of machine learning and model-based control to improve the capabilities of humanoids and other dynamic mobile manipulators.

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ECE Seminar: Kuan Fang TODAY at 4:30pm
by Emily C. Lawrence 04 Apr '23

04 Apr '23

ECE SEMINAR Speaker: Kuan Fang, University of California, Berkeley Title: Scalable Robot Intelligence: Self-Supervised Learning through Generation Day: Tuesday, April 4, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Chi Jin Abstract: Rapid advances in deep learning have resulted in promising techniques for robots to boost their capabilities to perceive, reason, and act by leveraging large models and massive datasets. However, the scalability of existing robot learning methods is severely limited by the manual labor and domain knowledge that humans can provide. To acquire general-purpose skills for solving a broad range of tasks, intelligent robots need scalable methods to collect and learn from rich data without extensive human supervision. In this talk, I will present my research on scaling up robot learning through the autonomous generation of environments, goals, and tasks. I will start by describing how to leverage procedural content generation for learning robust skills that can handle the variety and uncertainty of the real world. Then I will present algorithms that train robots to effectively reuse skills learned from prior experiences for novel sequential tasks by learning to generate reachable subgoals. Finally, I will demonstrate how to enable robots to discover a repertoire of novel skills by adaptively generating tasks during training. The acquired skills can be used for solving a variety of complex tasks such as tool use and sequential manipulation based on raw sensory inputs. Bio: Kuan Fang is a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, working with Sergey Levine. He received his Ph.D. degree in Electrical Engineering from Stanford University, advised by Fei-Fei Li and Silvio Savarese. His research interests lie at the intersection of robotics, computer vision, and machine learning, with a focus on developing data-driven methods to enable intelligent robots to operate in unstructured environments. He is a recipient of the Stanford Graduate Fellowship and the Computing Innovation Fellowship.

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Maxine Perroni-Scharf will present her MSE thesis "Constructing Printable Surfaces with View-dependent Appearance" on Wednesday, April 12, 2023 at 10am in CS 402.
by Nicki Mahler 03 Apr '23

03 Apr '23

Maxine Perroni-Scharf will present her MSE thesis "Constructing Printable Surfaces with View-dependent Appearance" on Wednesday, April 12, 2023 at 10am in CS 402. The members of her committee are as follows: Szymon Rusinkiewicz (Adviser) and Olga Russakovsky (Reader). The title and abstract are as follows: "Constructing Printable Surfaces with View-dependent Appearance" We present a method for the digital fabrication of surfaces whose appearance varies based on viewing direction. The surfaces are constructed from a mesh of bars arranged in a self-occluding colored heightfield which creates the desired view-dependent effects. At the heart of our method is a novel and simple differentiable rendering algorithm specifically designed to render colored 3D heightfields and enable efficient calculation of the gradient of appearance with respect to heights and colors. This algorithm forms the basis of a coarse-to-fine ML-based optimization process that adjusts the heights and colors of the strips to minimize the loss between the desired and real surface appearance from each viewpoint, deriving meshes that can then be fabricated using a 3D printer. Using our method, we demonstrate both synthetic and real-world fabricated results with view-dependent appearance.

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ECE Seminar: Daniel Seita TODAY at 4:30pm
by Emily C. Lawrence 03 Apr '23

03 Apr '23

ECE SEMINAR Speaker: Daniel Seita, Carnegie Mellon University Title: Representations in Robot Manipulation: Learning to Manipulate Cables, Fabrics, Bags, Liquids, and Plants Day: Monday, April 3, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Jaime Fernández Fisac Abstract: The robotics community has seen significant progress in applying machine learning for robot manipulation. However, much manipulation research focuses on rigid objects instead of highly deformable objects such as cables, fabrics, bags, liquids, and plants, which pose challenges due to their complex configuration spaces, dynamics, and self-occlusions. To achieve greater progress in robot manipulation of such diverse deformable objects, I advocate for an increased focus on learning and developing appropriate representations for robot manipulation. In this talk, I show how novel action-centric representations can lead to better imitation learning for manipulation of diverse deformable objects. I will show how such representations can be learned from color images, depth images, or point cloud observational data. My research demonstrates how novel representations can lead to an exciting new era for robot manipulation of complex objects. Bio: Daniel Seita is a postdoctoral researcher at Carnegie Mellon University's Robotics Institute, advised by David Held. His research interests are in computer vision and machine learning for robot manipulation, with a focus on using and developing novel observation and action representations to improve manipulation of challenging deformable objects. Daniel holds a PhD in computer science from the University of California, Berkeley, advised by John Canny and Ken Goldberg. He received undergraduate degrees in math and computer science from Williams College. Daniel's research has been supported by a six-year Graduate Fellowship for STEM Diversity and by a two-year Berkeley Fellowship. He has the Honorable Mention for Best Paper award at UAI 2017, was an RSS 2022 Pioneer, and has presented his work at premier robotics conferences such as ICRA, IROS, RSS, and CoRL.

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CS Colloquium Speaker Venkat Arun TODAY at 12:30pm
by Emily C. Lawrence 03 Apr '23

03 Apr '23

Speaker: Venkat Arun, Massachusetts Institute of Technology Date: Monday, April 3 Time: 12:30pm EST Location: CS 105 Host: Ravi Netravali Event page: https://www.cs.princeton.edu/events/26346 Title: Designing Provably Performant Networked Systems Abstract: As networked systems become critical infrastructure, their design must reflect their new societal role. Today, we build systems with hundreds of heuristics but often do not understand their inherent and emergent behaviors. I will present a set of tools and techniques to prove performance properties of heuristics running in real-world conditions. Rigorous proofs can not only inspire confidence in our designs, but also give counter-intuitive insights about their performance. A key theme in our approach is to model uncertainty in systems using non-random, non-deterministic objects that cover a wide range of possible behaviors under a single abstraction. Such models allow us to analyze complex system behaviors using automated reasoning techniques. I will present automated tools to analyze congestion control and process scheduling algorithms. These tools prove performance properties and find counter-examples where widely deployed heuristics fail. I will also show that current end-to-end congestion control algorithms that bound delay cannot avoid starvation and present a method to beamform wireless signals using thousands of antennas. Bio: Venkat Arun is a PhD candidate at MIT working with Hari Balakrishnan and Mohammad Alizadeh. His work spans internet congestion control, video streaming, privacy-preserving computation, wireless networks, and mobile systems. Across these areas, a unifying theme of his work is to bridge between heuristics that systems use in practice and proofs of how well they work. He believes that rigorous proof combined with automated reasoning will enable us to make networked systems more robust and performant. He has won two ACM SIGCOMM best paper awards and the president of India gold medal.

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Quantum Seminar - Gokul Subramanian Ravi: Thursday, April 6 at 4:30pm
by Emily C. Lawrence 31 Mar '23

31 Mar '23

Speaker: Gokul Subramanian Ravi, University of Chicago Date: Thursday, April 6 Time: 4:30pm EST Location: Engineering Quadrangle B205 Host: Hakan Türeci Event page: https://www.cs.princeton.edu/events/26361 Title: A Hybrid Computing Ecosystem for Practical Quantum Advantage Abstract: As quantum computing transforms from lab curiosity to technical reality, we must unlock its full potential to enable meaningful benefits on real-world applications with imperfect quantum technology. Achieving this vision requires computer architects to play a key role, leveraging classical computing principles to build and facilitate a hybrid computing ecosystem for practical quantum advantage. First, I will introduce my four research thrusts toward building this hybrid ecosystem: Classical Application Transformation, Adaptive Noise Mitigation, Scalable Error Correction and Efficient Resource Management. Second, from the Classical Application Transformation thrust, I will present "CAFQA: A classical simulation bootstrap for variational quantum algorithms", which enables accurate classical initialization for VQAs by searching efficiently through the classically simulable portion of the quantum space with Bayesian Optimization. CAFQA recovers as much as 99.99% of the accuracy lost in prior state-of-the-art classical initialization, with mean improvements of 56x. Third, from the Scalable Error Correction thrust, I will present "Clique: Better than worst-case decoding for quantum error correction", which proposes the Clique QEC decoder for cryogenic quantum systems. Clique is a lightweight cryo-decoder for decoding and correcting common trivial errors, so that only the rare complex errors are handled outside the cryo-refrigerator. Clique eliminates 90-99+% of the cryo-refrigerator I/O decoding bandwidth, while supporting more than a million physical qubits. Finally, I will conclude with an overview of other prior and ongoing work, along with my future research vision toward practical quantum advantage. Bio: Gokul Subramanian Ravi is a 2020 NSF CI Fellows postdoctoral scholar at the University of Chicago, mentored by Prof. Fred Chong. His research targets quantum computing architecture and systems, primarily on themes at the intersection of quantum and classical computing. He received his PhD in computer architecture from UW-Madison in 2020 and was advised by Prof. Mikko Lipasti. He was awarded the 2020 Best ECE Dissertation Award from UW-Madison and named a 2019 Rising Star in Computer Architecture. His quantum and classical computing research have resulted in publications at top computer architecture, systems, and engineering venues (such as ASPLOS, ISCA, MICRO, HPCA, TACO, ISLPED, QCE, IISWC), as well as two granted and three pending patents. His co-authored work was recognized as the Best Paper at HPCA 2022 and as a 2023 IEEE Micro Top Picks Honorable Mention.

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