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Ziyang Xu will present his FPO "A Fast and Extensible Memory Profiling Framework" on September 27, 2024 at 10am in Friend 125.
by Nicki Mahler 16 Sep '24

16 Sep '24

Ziyang Xu will present his FPO "A Fast and Extensible Memory Profiling Framework" on September 27, 2024 at 10am in Friend 125. The members of his committee are as follows: Examiners: David August (adviser), Aarti Gupta, Amit Levy Readers: Zachary Kincaid and Simone Campanoni (Northwestern University) Abstract: Recent advancements in automatic parallelization offer a promising way to fully leverage modern processors, yet these techniques remain underutilized. To understand this gap, this dissertation presents a field study of the computational usage and needs. The study reveals that although many researchers have access to abundant parallel resources, they often lack the necessary tools or expertise to utilize them effectively, highlighting the need for practical, user-friendly, and efficient solutions. This dissertation explores the practicality of memory profiling, a critical technique for automatic parallelization. Memory profiling captures programs' dynamic memory behavior, assisting in debugging, tuning, and enabling advanced compiler optimizations. Building practical memory profilers often requires extensive compiler expertise, adeptness in program optimization, and significant implementation efforts, leaving a gap in the availability of fast and efficient profilers. To bridge this gap, this dissertation presents PROMPT, a pioneering framework for streamlined development of fast memory profilers. With it, developers only need to specify profiling events and define the core profiling logic, bypassing the complexities of custom instrumentation and intricate memory profiling components and optimizations. By integrating dynamic binary instrumentation alongside LLVM-IR and source instrumentation, PROMPT ensures comprehensive memory access coverage while reducing reliance on binary instrumentation. Two state-of-the-art memory profilers were ported to PROMPT with all features preserved. By focusing on the core profiling logic, the code was reduced by more than 65% and the profiling speed was improved by 5.3x and 7.1x respectively. To further underscore PROMPT's impact, a tailored memory profiling workflow was constructed for a sophisticated compiler optimization client. In just 570 lines of code, this redesigned workflow satisfies the client's memory profiling needs while achieving more than 90% reduction in profiling time and improved robustness compared to the original profilers.

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Reminder: PPH seminar TODAY @ 12 pm, Guillermo Sapiro
by Emily C. Lawrence 12 Sep '24

12 Sep '24

Please join the Princeton Precision Health (PPH) Initiative for a seminar on Friday, September 13, at 12:00 pm, at 252 Nassau Street: Guillermo Sapiro, PhD, Electrical and Computer Engineering, Princeton University A Wearable-Based Biomarker of Aging and Its Association With Disease and Behavior Biomarkers of aging play a vital role in understanding human longevity and have the potential to inform clinical decisions and new interventions. Existing measures of biological age are typically based on blood, saliva, vital signs, or clinical imaging. Wearables, however, can make convenient, frequent, and inexpensive measurements throughout daily life, scaled to an entire population. We propose PPGAge, a biomarker that is non-invasively, passively, and longitudinally measurable by photoplethysmography (PPG) collected from a consumer wearable. With data from the Apple Heart & Movement Study (n=213,593 participants and over 149 million participant-days), we develop a statistical model of healthy aging and study its association with disease and behavior. PPGAge predicts chronological age with mean absolute error (MAE) of 2.43 years (95% CI 2.33--2.53) in a healthy cohort and 3.18 years (95% CI 3.16--3.19) in a general cohort. Among participants with a PPGAge gap (i.e., the deviation between predicted age and chronological age) larger than 6 years, diagnosis rates of heart disease, heart failure, and diabetes are 1.5--5 times the age- and sex-adjusted average. PPGAge is also associated with behavior, including smoking, exercise, and sleep. In longitudinal analyses, PPGAge exhibits a sharp increase during pregnancy and around the time of certain types of cardiac events. With additional evidence, PPGAge may be a useful surrogate for healthy aging in the study of human longevity and the treatment of age-related conditions. Joint work with A. Miller, J. Futoma, S. Abbaspourazad, C. Heinze-Deml, S. Emrani, and I. Shapiro. Lunch will be provided. Please note that getting to the seminar space currently requires that you climb a set of stairs. If an accommodation is needed, please contact PPH in advance at: [ mailto:princetonPPH@princeton.edu | princetonPPH(a)princeton.edu ]

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PPH seminar on Friday @ 12 pm, Guillermo Sapiro
by Emily C. Lawrence 09 Sep '24

09 Sep '24

Please join the Princeton Precision Health (PPH) Initiative for a seminar on Friday, September 13, at 12:00 pm, at 252 Nassau Street: Guillermo Sapiro, PhD, Electrical and Computer Engineering, Princeton University A Wearable-Based Biomarker of Aging and Its Association With Disease and Behavior Biomarkers of aging play a vital role in understanding human longevity and have the potential to inform clinical decisions and new interventions. Existing measures of biological age are typically based on blood, saliva, vital signs, or clinical imaging. Wearables, however, can make convenient, frequent, and inexpensive measurements throughout daily life, scaled to an entire population. We propose PPGAge, a biomarker that is non-invasively, passively, and longitudinally measurable by photoplethysmography (PPG) collected from a consumer wearable. With data from the Apple Heart & Movement Study (n=213,593 participants and over 149 million participant-days), we develop a statistical model of healthy aging and study its association with disease and behavior. PPGAge predicts chronological age with mean absolute error (MAE) of 2.43 years (95% CI 2.33--2.53) in a healthy cohort and 3.18 years (95% CI 3.16--3.19) in a general cohort. Among participants with a PPGAge gap (i.e., the deviation between predicted age and chronological age) larger than 6 years, diagnosis rates of heart disease, heart failure, and diabetes are 1.5--5 times the age- and sex-adjusted average. PPGAge is also associated with behavior, including smoking, exercise, and sleep. In longitudinal analyses, PPGAge exhibits a sharp increase during pregnancy and around the time of certain types of cardiac events. With additional evidence, PPGAge may be a useful surrogate for healthy aging in the study of human longevity and the treatment of age-related conditions. Joint work with A. Miller, J. Futoma, S. Abbaspourazad, C. Heinze-Deml, S. Emrani, and I. Shapiro. Lunch will be provided. Please note that getting to the seminar space currently requires that you climb a set of stairs. If an accommodation is needed, please contact PPH in advance at: [ mailto:princetonPPH@princeton.edu | princetonPPH(a)princeton.edu ]

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DeCenter: upcoming events
by Emily C. Lawrence 05 Sep '24

05 Sep '24

The DeCenter hopes you had a great summer and is excited to welcome you back to campus with some exciting announcements. We are excited to announce our first two events of the year. Our first on-campus seminar of the year will be given by Aviv Zohar, a professor at the Hebrew University of Jerusalem and a Princeton University Sabbatical Visitor. His talk, titled "Speculative Denial-of-Service Attacks in Ethereum," will take place on September 18th, 2024, at 4:30 PM in the Friend Center Convocation Room (113). We are also excited to announce the inaugural Princeton University DeCenter Alumni Mixer. This alumni-only event will take place on October 17th from 6:00 PM to 8:00 PM at the Arlo NoMad Hotel rooftop in NYC and will feature a short talk from the DeCenter faculty directors. The event is free to alums, but space is limited, so please register through the provided Eventbrite link to secure your spot. [ https://www.eventbrite.com/e/1002571938657?aff=oddtdtcreator | https://www.eventbrite.com/e/1002571938657?aff=oddtdtcreator ]

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Kun Woo Cho will present his FPO "Programmable Smart Radio Environments: From Theory to Hardware Implementation" on October 1, 2024 at 3pm in CS 302.
by Gradinfo 25 Aug '24

25 Aug '24

Kun Woo Cho will present his FPO "Programmable Smart Radio Environments: From Theory to Hardware Implementation" on October 1 , 2024 at 3pm in CS 302. Examiner: Prof. Kyle Jamieson (Advisor, CS), Prof. Yasaman Ghasempour (ECE), Prof. Andrew Appel (CS) Reader: Prof. Wyatt Lloyd (CS), Prof. Omid Abari (UCLA), Dr. Ranveer Chandra (MSR) All are welcome to attend. Title: Programmable Smart Radio Environments: From Theory to Hardware Implementation Abstract: Today ’s wireless networks are undergoing a rapid transformation, scaling in traffic vol- ume, spectral efficiency, and radio count as never seen before. This thesis addresses the critical challenges emerging from this evolution in next-generation (NextG) wire- less networks, focusing on realizing three key services: enhanced mobile broadband (Gbps or above), ultra-reliable low latency communication (order of milliseconds), and massive machine type communication (up to one million per squared kilometer). To meet these diverse and demanding requirements, this thesis poses a central question: Can we build a smarter radio environment controlled and learned by soft- ware, configuring itself in real-time to meet different application needs? Current approaches to handle uncontrolled wireless signals are end-to-end. Unfortunately, sending and receiving endpoints are limited in their ability to shape this inherent propagation behavior. For instance, although multiple antennas can shape the beam pattern departing the sender, it cannot control how the resulting signals arrive at the receiver after traversing environmental obstacles. By focusing on changing the envi- ronment itself rather than the communication endpoints, this thesis offers a significant shift in design paradigms for modern wireless networks. First, millimeter-wave (mmWave) technology offers multi-Gbps data rates due to its wide spectral bands. However, the high frequency of mmWave signals makes them vulnerable to blockage by walls, people, and obstacles, significantly limiting their practical applications. This thesis introduces mmWall, a programmable smart surface deployed on buildings to fully control the mmWave radio environment. Com- prising over 4,000 sub-millimeter meta-materials, mmWall can steer signals through the surface or reflect them, bringing outdoor mmWave signals indoors and bypassing obstacles. Also, this thesis proposes Wall-Street, a smart surface installed on vehicles to provide seamless and reliable mmWave connectivity in high-mobility scenarios. Second, satellite networks uses constellations of thousands of satellites to provide low latency communication and global coverage. However, these networks face two significant challenges: outages due to transient blockage, and complications in beam alignment between users and satellites due to the use of different frequency sub-bands in the uplink and downlink directions. Extending the concept of a programmable smart radio to satellite communications, this thesis introduces Wall-E, a dual-band smart surface that mitigates signal blockage and enhances reliability of satellite-to- ground links, and Monolith, a smart surface that boosts inter-satellite link capacity. Third, while these smart surfaces address challenges at the physical layer, we also tackle issues at the link/MAC layer, particularly in massive Internet of Things (IoT) networks. This thesis introduces Cross-Link Channel Prediction (CLCP), a ma- chine learning technique that learns the radio environment and accordingly allocates networking resources to a large number of IoT devices. This AI-driven approach com- plements my programmable surfaces, creating a comprehensive smart radio solution for NextG networks.

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Yue Tan will present her FPO "FAASTEN: An Architecture and implementation for securing cloud applications" on September 5, 2024 at 11am in CS 302.
by Gradinfo 15 Aug '24

15 Aug '24

Yue Tan will present her FPO "FAASTEN: An Architecture and implementation for securing cloud applications" on September 5, 2024 at 11am in CS 302. The members of her committee are as follows: Amit Levy (advisor), Maria Apostolaki, Wyatt Lloyd Readers: Mae Milano and Marcus Peinado (Microsoft Research) All are welcome to attend. The FPO abstract follows below: Modern web applications have evolved into intricate networks of micro-applications, posing challenges in ensuring the security of user data confidentiality and integrity. In the current discretionary security paradigm, developers must enforce policies for every microapplication and all data paths that can arise at run-time. A promising alternative paradigm, decentralized information flow control (DIFC), suggests that the underlying system should enforce policies by tracking information flows. As the cloud has become a standard operating system for developing web applications, this paper proposes the first DIFC design for the cloud. We present Faasten, an architecture and implementation for securing cloud applications. Faasten includes a DIFC model and a FaaS-inspired system interface that implements the DIFC model. We provide a detailed interface security analysis and showcase how three representative cloud applications benefit from Faasten. We also show that Faasten introduces negligible latencies in controlling information flows and policy storage costs.

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Samuel Ginzburg will present his FPO "VectorVisor: A Binary Translation Scheme for Throughput-Oriented GPU Acceleration" on Thursday, August 29, 2024 in CS 302 at 1pm.
by Gradinfo 15 Aug '24

15 Aug '24

Samuel Ginzburg will present his FPO "VectorVisor: A Binary Translation Scheme for Throughput-Oriented GPU Acceleration" on Thursday, August 29, 2024 in CS 302 at 1pm. The members of his FPO committee follows below: Examiners: Michael Freedman (Advisor), Wyatt Lloyd, and Amit Levy Readers: Mae Milano and Mohammad Shahrad (UBC) All are welcome to attend. Please see abstract below. Beyond conventional graphics applications, general-purpose GPU acceleration has had significant impact on machine learning and scientific computing workloads. Yet, it has failed to see widespread use for server-side applications, which we argue is because GPU programming models offer a level of abstraction that is either too low-level (e.g., OpenCL, CUDA) or too high-level (e.g., TensorFlow, Halide), depending on the language. Not all applications fit into either category, resulting in lost opportunities for GPU acceleration. We introduce VectorVisor, a vectorized binary translator that enables new opportunities for GPU acceleration by introducing a novel programming model for GPUs. With VectorVisor, many copies of the same server-side application are run concurrently on the GPU, where VectorVisor mimics the abstractions provided by CPU threads. To achieve this goal, we demonstrate how to (i) provide cross-platform support for system calls and recursion using continuations and (ii) make full use of the excess register file capacity and high memory bandwidth of GPUs. We then demonstrate that our binary translator is able to transparently accelerate certain classes of computebound workloads, gaining significant improvements in throughput-per-dollar of up to 2.9× compared to Intel x86-64 VMs in the cloud, and in some cases match the throughput-per-dollar of native CUDA baselines.

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Kaifeng Lyu will present his FPO "Implicit Bias in Deep Learning Optimization: A Mathematical Examination" on Friday, August 9, 2024 at 10:30 AM in CS 402 and Zoom.
by CS Grad Department 02 Aug '24

02 Aug '24

Kaifeng Lyu will present his FPO " Implicit Bias in Deep Learning Optimization: A Mathematical Examination " on Friday, August 9, 2024 at 10:30 AM in CS 402 and Zoom. Location: Zoom link: https://princeton.zoom.us/j/99448789401 The members of Kaifeng ’s committee are as follows: Examiners: Sanjeev Arora (Adviser), Elad Hazan , Chi Jin Readers: Danqi Chen , Jason Lee A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Deep learning has achieved remarkable success in recent years, yet training neural networks often involves a delicate combination of guesswork and hyperparameter tuning. A critical aspect of this process is the “implicit bias” of optimization methods, where minor changes in the optimization setup—without affecting the small training loss at convergence—can drastically shift the solution to which the model converges, thereby affecting test performance. This dissertation presents a collection of results that mathematically characterize this implicit bias in various training regimes. The first part of this dissertation explores how gradient descent, even without explicit regularization, can converge to solutions that maximize the margin. Previous results have established the first-order optimality of margin for homogeneous neural networks in general, but the global optimality of margin is not guaranteed due to their non-convex nature. This dissertation provides in-depth theoretical analyses when data has simple structures: for linearly separable data, we present both positive and negative results on whether the global optimality of margin can be attained. Furthermore, we show how this margin-based view can be used to explain interesting generalization phenomena in training neural networks with or without explicit regularization, including the simplicity bias and grokking phenomena. The second part of the dissertation presents two results that capture the implicit biases induced by finite learning rate. Many existing analyses, including the margin-based ones in the first part, describe implicit biases that hold even when the learning rate is infinitesimal. However, practical implementations use finite learning rates, which have been empirically observed to benefit generalization. We analyze how full-batch GD with finite learning rates, combined with key training components like normalization layers and weight decay, create a bias towards flatter minima, which are positively correlated with better generalization. Additionally, we study the implicit bias in stochastic optimization and derive rigorous approximations for the dynamics of adaptive gradient methods like Adam and RMSprop via Stochastic Differential Equations (SDEs) to capture the effect of finite learning rates. Based on this, we also derive the square root scaling rule as a practical guideline for adjusting the optimization hyperparameters of adaptive gradient methods when changing batch size.

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Yufei Zheng will present her FPO "Compact Algorithms for Measuring Network Performance" on August 22, 2024 at 12pm in CS 301.
by Gradinfo 25 Jul '24

25 Jul '24

Yufei Zheng will present her FPO "Compact Algorithms for Measuring Network Performance" on August 22, 2024 at 12pm in CS 301. The members of Yufei's committee are as follows: Examiners: Jennifer Rexford (adviser), Maria Apostolaki, and Mark Braverman Readers:Huacheng Yu and David Hay (The Hebrew University of Jerusalem) All are welcome to attend. Please see abstract below. Abstract: For network administrators, performance monitoring provides valuable insights into network quality and security. The emergence of programmable network devices makes it possible to measure performance metrics in the data plane, where packets fly by. Running measurement tasks directly in the data plane improves eciency, preserves user privacy, and enables the possibility of real-time actions based on the analysis. However, programmable data planes have limited memory size and memory accesses. Meanwhile, measuring performance metrics fundamentally requires a large amount of memory resources, as each packet must be processed relative to its predecessor in the same flow. This dissertation tackles the specific challenges that arise from running performance measurements with limited memory. The first part of this dissertation introduces new data-plane algorithms for monitoring two canonical performance metrics related to Transmission Control Protocol (TCP): delay and TCP packet reordering. • In measuring delay distribution, existing algorithms often exhibit bias against larger delays. We present fridges, a novel data structure that allows for the correction of the survivorship bias due to hash collisions. The main idea is to keep track of the probability p that a delay sample is collected, and count it as 1/p samples. Using multiple fridges together, we can further improve the accuracy by computing a weighted average of single-fridge estimators. • We present ecient algorithms for identifying IP prefixes with heavy packet reordering. First, we sample as many flows as possible, regardless of their sizes, but only for a short period at a time. Next, we separately monitor the large flows over long periods, in addition to the flow sampling. Both algorithms measure at the flow level, and aggregate statistics at the prefix level. Existing counter-based algorithms for identifying heavy-hitter flows could also be modified to measure performance metrics. However, many of such algorithms, despite of showing good empirical performance, lack theoretical guarantees. In the second part, we present the first formal analysis for the performance of one such algorithm, the Random Admission Policy (RAP). • We show that, for a highly skewed packet stream with k heavy flows, RAP with memory size k stores O(k) heavy flows with constant probability.

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Linda Cai will present her FPO "Algorithmic Decision Making with Imperfect Information and Practical Irrationality" on August 7th, 2024 at 11am in CS 402.
by Gradinfo 19 Jul '24

19 Jul '24

Linda Cai will present her FPO "Algorithmic Decision Making with Imperfect Information and Practical Irrationality" on August 7th, 2024 at 11am in CS 402. Her FPO committee is as follows: Examiners: Matt Weinberg (adviser), Mark Braverman, and Huacheng Yu Readers: Bernard Chazelle and Brendan Lucier (Microsoft Research) Title and abstract follow below. All are welcome to attend. Title: Algorithmic Decision Making with Imperfect Information and Practical Irrationality Abstract: Market algorithms are ubiquitous in modern life, both online and offline. Historically, markets have been fundamental in matching supply with demand, where designers optimize regulations with global goals and participants optimize strategies with individual goals. Decision theory and mechanism design have evolved to study and prescribe the behavior and structure of these markets. This thesis addresses key questions at the intersection of market design and algorithmic decision-making: How do strategic decision-makers navigate choices, and how do resource producers allocate limited resources among strategic participants? The advent of information technology and increased data availability has revolutionized markets, enabling unprecedented scale, efficiency, and control. This development allows mechanism designers to create or modify market conditions, provided we understand their impact on the designer's objectives. In this thesis, we explore market algorithms in dynamic environments and under practical irrationality, analyzing how deviations from ideal models affect the utility of both designers and participants. We examine environmental changes such as market imbalance, resource augmentation, and competition faced by dominant sellers. For participants, we consider agents who are computationally bounded, behaviorally biased, or using learning algorithms. Our work aims to provide insights into the robustness and adaptability of market algorithms amid practical complexities and diverse participant behaviors.

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