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2023-09-25 16:00:00
2023-09-25 17:00:00
America/New_York
User-Level Differential Privacy With Few Examples Per User
Abstract: Previous work on user-level differential privacy (DP) [Ghazi et al., NeurIPS 2021; Bun et al., STOC 2023] obtained generic algorithms that work for various learning tasks. However, their focus was on the example-rich regime, where the users have so many examples that each user could themselves solve the problem. In this work we consider the example-scarce regime, where each user has only a few examples, and obtain the following results:For approximate-DP, we give a generic transformation of any item-level DP algorithm to a user-level DP algorithm. Roughly speaking, the latter gives a (multiplicative) savings of O_{ε,δ}(√m) in terms of the number of users required for achieving the same utility, where m is the number of examples per user. This algorithm, while recovering most known bounds for specific problems, also gives new bounds, e.g., for PAC learning.For pure-DP, we present a simple technique for adapting the exponential mechanism [McSherry & Talwar, FOCS 2007] to the user-level setting. This gives new bounds for a variety of tasks, such as private PAC learning, hypothesis selection, and distribution learning. For some of these problems, we show that our bounds are near-optimal.
32-G449
Events
September 24, 2023
No events scheduled
September 25, 2023
September 26, 2023
Cancelled
Thesis Defense: "Gluing and Creasing Paper along Curves: Computational Methods for Analysis and Design of Compositions of Developable Surfaces"
Klara Mundilova
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2023-09-26 18:00:00
2023-09-26 19:00:00
America/New_York
Thesis Defense: "Gluing and Creasing Paper along Curves: Computational Methods for Analysis and Design of Compositions of Developable Surfaces"
Curved geometries that can be obtained from flat sheets of material have numerous applications in design and engineering. Inspired by artistic sculptures, we explore shapes formed by joining planar patches of material along their curved boundaries, with a particular focus on curved-crease origami. We discuss the theory behind the computation of shapes made of developable patches and highlight a simplified computational approach for cases where the connected patches are either cylinders or cones. Moreover, we illustrate how to compute a crease connecting a patch to a generalized cylinder or cone. Drawing inspiration from artistic origami, we showcase examples and introduce parametric design tools for curved crease origami design.Committee: Erik Demaine, Justin Solomon, and Tomohiro Tachi
32-G882 (Hewlett) and Zoom https://mit.zoom.us/j/92731567373
September 27, 2023
A Decade of Molecular Cell Atlases
Stephen Quake
Stanford University
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2023-09-27 11:30:00
2023-09-27 13:00:00
America/New_York
A Decade of Molecular Cell Atlases
Zoom link for virtual attendance: https://mit.zoom.us/j/93513735220
32-G575
Thesis Defense: Eric Atkinson. Title: A Language and Logic for Programming and Resoning with Partial Observability
Eric Atkinson
MIT
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2023-09-27 13:00:00
2023-09-27 15:00:00
America/New_York
Thesis Defense: Eric Atkinson. Title: A Language and Logic for Programming and Resoning with Partial Observability
Abstract: Computer systems are increasingly deployed in partially-observable environments, in which the system cannot directly determine the environment’s state but receives partial information from observations. When such a computer system executes, it risks forming an incorrect belief about the true state of the environment. For example, the Mars Polar Lander (MPL) is a lost space probe that crashed because its control software believed it was on the Martian surface when it was actually 40m high, and as a result, it turned off its descent engine too early. Developers need better software development tools to prevent such accidents. In this talk, I will present a new type of programming language and corresponding program logic that enable developers to deliver correct software in partially-observable environments. In particular, I will present belief programming, a language in which developers write a model of the partial observability in the environment. The language produces an executable state estimator, which is a function that maps environmental observations to estimates of the environment’s true state. I have implemented the prototype belief programming language BLIMP, and used BLIMP to implement several example programs including an engine controller for the MPL. I will also present in this talk Epistemic Hoare Logic (EHL), a program logic for belief programs that enables developers to reason about properties of the resulting state estimators. I have used EHL to prove that the BLIMP version of the MPL does not have the error that caused the original MPL to crash. Furthermore, I will present semi-symbolic inference, a technique that provides more efficient implementations of belief programming languages. Across a range of benchmarks, my performance experiments show that a semi-symbolic BLIMP implementation achieves speedups of 515x-58,919x over a naive BLIMP implementation. Together, the contributions of belief programming, EHL, and semi-symbolic inference enable developers to focus on modeling the partial observability in the environment, and provide programming language support for automatically generating efficient state estimators and reasoning about their properties.
Seminar Room D463 (Star)
K9db: Privacy-Compliant Storage For Web Applications By Construction
Kinan Dak Albab
Brown
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2023-09-27 16:00:00
2023-09-27 17:00:00
America/New_York
K9db: Privacy-Compliant Storage For Web Applications By Construction
Data privacy laws like the EU's GDPR grant users new rights, such as the right to request access to and deletion of their data. Manual compliance with these requests is error-prone and imposes costly burdens especially on smaller organizations, as non-compliance risks steep fines.K9db is a new, MySQL-compatible database that complies with privacy laws by construction. The key idea is to make the data ownership and sharing semantics explicit in the storage system. This requires K9db to capture and enforce applications' complex data ownership and sharing semantics, but in exchange simplifies privacy compliance. Using a small set of schema annotations, K9db infers storage organization, generates procedures for data retrieval and deletion, and reports compliance errors if an application risks violating the GDPR.Our K9db prototype successfully expresses the data sharing semantics of real web applications, and guides developers to getting privacy compliance right. K9db also matches or exceeds the performance of existing storage systems, at the cost of a modest increase in state size.
G882
September 29, 2023
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2023-09-29 8:30:00
2023-09-29 16:30:00
America/New_York
IAP-MIT Workshop on the Future of AI and Cloud Computing
You are cordially invited to the MIT Workshop on AI and Cloud Computing. Please see the invitation below our sign-off, and register today if you are able to attend. We hope to see you there!Best regards,Prof. Christina DelimitrouRegister Now: MIT Workshop on AI and Cloud Computing Hear talks by leading experts in academia and industry working in AI and machine learning, hardware acceleration, operating systems, networking, Big Data, security, storage and data management. In addition to faculty from MIT and Harvard, meet and hear from top researchers at Alibaba, Google, Marvell, and Meta.Date: Friday Sept 29, 2023Venue: Kiva Room in Building 32 (Room 32G-449), Stata Center, 32 Vassar St., MIT, Cambridge, MATime: 8:30AM–4:30PM (Badge Pick-up at 8:30AM)Pre-Registration is Required: Please see the Event Page for the Agenda and Registration: https://www.industry-academia.org/mit-2023.htmlCall for Posters: The student poster session during the lunch hour (11:30-12:30PM) is preceded by the Lightning Talk session with graduate students describing their cloud related research projects and findings. Students are encouraged to participate and compete for the $300 cash award for Best Poster. Please indicate your interest on the registration form to receive more info. This event is co-organized by Prof. Christina Delimitrou and the IAPAbout the IAP: The Industry-Academia Partnership (IAP) is in its 11th year hosting events and conducting projects about applications and infrastructure for AI and machine learning, hardware acceleration, networking, security, and storage. For more info, please see www.industry-academia.org
Kiva G-449: Building 32 (Room 32G-449), Stata Center, 32 Vassar St., MIT, Cambridge, MA
PAC Privacy: Automatic Privacy Measurement and Control of Data Processing
Hanshen Xiao
CSAIL MIT
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2023-09-29 10:30:00
2023-09-29 12:00:00
America/New_York
PAC Privacy: Automatic Privacy Measurement and Control of Data Processing
In this talk, I will introduce a new privacy definition, termed Probably Approximately Correct (PAC) Privacy. PAC Privacy characterizes the information-theoretic hardness to recover sensitive data given arbitrary information disclosure/leakage during/after any processing. Unlike the classic cryptographic definition and Differential Privacy (DP), which consider the adversarial (input-independent) worst case, PAC Privacy is a simulatable metric that quantifies the instance-based impossibility of inference. A fully automatic analysis and proof generation framework are proposed: security parameters can be produced with arbitrarily high confidence via Monte-Carlo simulation for any black-box data processing oracle. This appealing automation property enables analysis of complicated data processing, where the worst-case proof in the classic privacy regime could be loose or even intractable. Moreover, we show that the produced PAC Privacy guarantees enjoy simple composition bounds and the automatic analysis framework can be implemented in an online fashion to analyze the composite PAC Privacy loss even under correlated randomness. On the utility side, the magnitude of (necessary) perturbation required in PAC Privacy is not lower bounded by Theta(\sqrt{d}) for a d-dimensional release but could be O(1) for many practical data processing tasks, which is in contrast to the input-independent worst-case information-theoretic lower bound. I will also talk about practical applications to complicated data processing, including end-to-end privacy analysis of deep learning and clustering.
G-882, Hewlett Room
Statistics When n Equals 1
Benjamin Recht
Department EECS, University of California, Berkeley
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2023-09-29 16:00:00
2023-09-29 17:00:00
America/New_York
Statistics When n Equals 1
Abstract: 21st-century medicine embraces a statistical view of effectiveness. This view considers the implications of treatments and diseases as best understood on populations. But such population conclusions tell us little about what to do with any particular person. This talk will first describe some of the shortsightedness of population statistics when it comes to individual decision-making. As an alternative, I will outline how we might design treatments and interventions to help individuals directly. I will present a series of parallel projects that link ideas from optimization, control, and experiment design to create statistics and inform decisions for the individual. Though most recent work has focused on precision, focusing on smaller statistical populations, I will explain why optimization might better guide personalization.Bio: Benjamin Recht is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research has focused on applying mathematical optimization and statistics to problems in data analysis and machine learning. He is currently studying histories, methods, and theories of scientific validity and experimental design.
G449 (Kiva)
October 02, 2023
Quantum's Impact on Security - A Conversation with MIT CSAIL and MIT CQE
Daniela Rus, William Oliver, Peter Shor, Vinod Vaikuntanathan
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2023-10-02 15:30:00
2023-10-02 17:00:00
America/New_York
Quantum's Impact on Security - A Conversation with MIT CSAIL and MIT CQE
On October 2, hear from MIT experts what the current field of quantum computing research looks like, its impact on security, and how the work happening today might impact society in the future. Moderated by CSAIL Director Professor Daniela Rus, Professor Shor will join a panel with the Director of the Center for Quantum Engineering at MIT, Professor William Oliver, and cryptography expert Professor Vinod Vaikuntanathan to explore how quantum will break RSA security, new cryptography measures that could fill the cybersecurity gap, and how those solutions will stand up to the quantum computers of tomorrow.
October 03, 2023
Multiparty Homomorphic Encryption: From Theory to Practice
Christian Mouchet
Swiss Federal Institute of Technology Lausanne
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2023-10-03 16:00:00
2023-10-03 17:00:00
America/New_York
Multiparty Homomorphic Encryption: From Theory to Practice
Multiparty homomorphic encryption (MHE) techniques enable secure multiparty computation (MPC) with low requirements for the input parties. Notably, these protocols have low communication complexity, most of their execution cost can be outsourced, and they can tolerate churn. But, despite being promising in theory, MHE-based MPC solutions have not yet been implemented in any of the 30+ existing MPC frameworks, thus revealing a gap between theory and practice. This presentation summarizes my prior and current work toward closing this gap by proposing contributions to both sides.On the theoretical side, I will present two MHE constructions that extend the new generation of HE schemes to the multiparty setting. On the practical side, I will present the Lattigo library and the Helium system. Lattigo is an open-source Go package that implements the state-of-the-art HE schemes, along with their multiparty extensions. Helium builds on Lattigo and will provide the first open-source end-to-end implementation of an MHE-based MPC protocol.BioChristian Mouchet recently obtained his PhD from EPFL, where he was advised by Prof. Carmela Troncoso and Prof. Jean-Pierre Hubaux. His research focuses on applied cryptography, particularly for secure multiparty computation protocols and their implementation. He is the founder and a maintainer of Lattigo, an open-source library for multiparty homomorphic encryption.
G-449 KIVA/Patil
October 04, 2023
TBA
David Knowles
Columbia University
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2023-10-04 11:30:00
2023-10-04 13:00:00
America/New_York
TBA
Zoom link: https://mit.zoom.us/j/93513735220
October 06, 2023
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2023-10-06 15:15:00
2023-10-06 18:30:00
America/New_York
CBMM10 - A Symposium on Intelligence: Brains, Minds, and Machines
The dream of understanding the mind and the brain and replicating human intelligence in machines was at the core of several new fields created at MIT during the ‘50s and ‘60s, including information theory, cybernetics, and Artificial Intelligence. The same dream was at the core of the NSF-funded, multi-institutional Center for Brains, Minds, and Machines (CBMM) and of its integration in the new Quest for Intelligence, which is bridging faculty across all the Schools of the Massachusetts Institute of Technology.Our symposium will focus on the topic of intelligence – one of the greatest problems in science and engineering and a key to our future as a society. The symposium will look at the past, in particular at the advances achieved by CBMM over the past 10 years. But, it will mainly focus on the future, in particular the future of neuroscience (Brains), the future of cognitive science (Minds), the future of AI (Machines) and their synergies.The goal of the workshop is to celebrate CBMM’s success, and to explore the future of CBMM and Quest, in pursuing the natural science of intelligence and investigating its synergies with AI. Deep learning was inspired by neuroscience and led to a better computational understanding of primate perception. It also led to surprising engineering advances such as AlphaGo, Alphafold, and LLMs. This symposium aims to take stock of what has been scientifically accomplished via that framework, to illuminate what still must be accomplished, and to chart next steps by discussing and debating which of the current approaches are likely to achieve those scientific accomplishments.Pre-registration is required.For more information, including registration, schedule, and speakers, please visit our event page here - https://cbmm.mit.edu/CBMM10
MIT Campus
October 07, 2023
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2023-10-07 9:15:00
2023-10-07 16:15:00
America/New_York
CBMM10 - A Symposium on Intelligence: Brains, Minds, and Machines
The dream of understanding the mind and the brain and replicating human intelligence in machines was at the core of several new fields created at MIT during the ‘50s and ‘60s, including information theory, cybernetics, and Artificial Intelligence. The same dream was at the core of the NSF-funded, multi-institutional Center for Brains, Minds, and Machines (CBMM) and of its integration in the new Quest for Intelligence, which is bridging faculty across all the Schools of the Massachusetts Institute of Technology.Our symposium will focus on the topic of intelligence – one of the greatest problems in science and engineering and a key to our future as a society. The symposium will look at the past, in particular at the advances achieved by CBMM over the past 10 years. But, it will mainly focus on the future, in particular the future of neuroscience (Brains), the future of cognitive science (Minds), the future of AI (Machines) and their synergies.The goal of the workshop is to celebrate CBMM’s success, and to explore the future of CBMM and Quest, in pursuing the natural science of intelligence and investigating its synergies with AI. Deep learning was inspired by neuroscience and led to a better computational understanding of primate perception. It also led to surprising engineering advances such as AlphaGo, Alphafold, and LLMs. This symposium aims to take stock of what has been scientifically accomplished via that framework, to illuminate what still must be accomplished, and to chart next steps by discussing and debating which of the current approaches are likely to achieve those scientific accomplishments.Pre-registration is required.For more information, including registration, schedule, and speakers, please visit our event page here - https://cbmm.mit.edu/CBMM10
MIT Campus
October 11, 2023
TBA
Bogdan Pasaniuc
UCLA
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2023-10-11 11:30:00
2023-10-11 13:00:00
America/New_York
TBA
Zoom Link: https://mit.zoom.us/j/93513735220
October 13, 2023
AI for social impact: Results from deployments for public health
Milind Tambe
Gordon McKay Professor of Computer Science and Director of Center for Research in Computation and Society at Harvard University
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2023-10-13 14:30:00
2023-10-13 15:30:00
America/New_York
AI for social impact: Results from deployments for public health
Abstract:For the past more than 15 years, my team and I have been focused on AI for social impact, deploying end-to-end systems in areas of public health, conservation and public safety. In this talk, I will highlight the results from our deployments for public health, as well as the AI and multiagent systems innovations that were necessary. I will first present our work with the world's two largest mobile health programs for maternal and child care that have served millions of beneficiaries. In one of these, our deployment significantly cut attrition and doubled health information exposure for those with least access. Additionally, I will highlight results from an earlier project on HIV prevention and others. The key challenge in all of these applications is one of optimizing limited intervention resources. To address this challenge, I will discuss advances in restless multi-armed bandits, decision-focused learning in predict-then-optimize systems, and influence maximization in social networks, and also discuss their broader applicability. In pushing this research agenda, our ultimate goal is to facilitate local communities and non-profits to directly benefit from advances in AI tools and techniques.Bio: Milind Tambe is Gordon McKay Professor of Computer Science and Director of Center for Research in Computation and Society at Harvard University; concurrently, he is also Principal Scientist and Director for "AI for Social Good" at Google Research. He is recipient of the IJCAI John McCarthy Award, AAAI Feigenbaum Prize, AAAI Robert S. Engelmore Memorial Lecture Award, AAMAS ACM Autonomous Agents Research Award, INFORMS Wagner prize for excellence in Operations Research practice and MORS Rist Prize. He is a fellow of AAAI and ACM. For his work on AI and public safety, he has received Columbus Fellowship Foundation Homeland security award and commendations and certificates of appreciation from the US Coast Guard, the Federal Air Marshals Service and airport police at the city of Los Angeles.
G449 (Kiva)
October 17, 2023
Distributed Sparse Computing in Python
Stanford University
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2023-10-17 14:00:00
2023-10-17 15:00:00
America/New_York
Distributed Sparse Computing in Python
This is hybrid meeting. The physical room is 32-D463 (Star). The Zoom registration link is https://mit.zoom.us/meeting/register/tJMtdOqgrTwsGNV_k0Nk6JjLMk-G6GFzTRyk.******************IMPORTANT NOTE ABOUT ONLINE REGISTRATION******************- The registration link for 2023 is the same as the link from 2022.- Please save the Zoom link that you receive after you register. This link will stay the same for all subsequent Fast Code seminars.- Zoom does not recognize a second registration, and will not send out the link a second time. The organizers will not be notified of any second registration.- If you have any problems with registration, please contact lindalynch@csail.mit.edu by 12pm on the day of the seminar, so that we can try to resolve it before the seminar begins.*********************************************************************Abstract: The sparse module of the popular SciPy Python library is widely used across applications in scientific computing, data analysis and machine learning. The standard implementation of SciPy is restricted to a single CPU and cannot take advantage of modern distributed and accelerated computing resources. We introduce Legate Sparse, a system that transparently distributes and accelerates unmodified sparse matrix-based SciPy programs across clusters of CPUs and GPUs, and composes with cuNumeric, a distributed NumPy library. Legate Sparse uses a combination of static and dynamic techniques to efficiently compose independently written sparse and dense array programming libraries, providing a unified Python interface for distributed sparse and dense array computations. We show that Legate Sparse is competitive with single-GPU libraries like CuPy and achieves 65% of the performance of PETSc on up to 1280 CPU cores and 192 GPUs of the Summit supercomputer, while offering the productivity benefits of idiomatic SciPy and NumPy.Bio: Rohan Yadav is a fourth-year computer science Ph.D. student at Stanford University, advised by Alex Aiken and Fredrik Kjolstad. He is generally interested in programming languages and computer systems, with a focus in systems for parallel and distributed computing.
32-D463 (Star)
October 18, 2023
Augmenting k-mer sketching for (meta)genomic sequence comparisons
William Yu
Carnegie Mellon University (CMU)
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2023-10-18 11:30:00
2023-10-18 13:00:00
America/New_York
Augmenting k-mer sketching for (meta)genomic sequence comparisons
Over the last decade, k-mer sketching (e.g. minimizers or MinHash) to create succinct summaries of long sequences has proven effective at improving the speed of sequence comparisons. However, rigorously characterizing the accuracy of these techniques has been more difficult. In this talk, I'll touch on three results that showcase some of the modern theoretical developments and practical applications of theory to building faster sequence comparison tools for metagenomics.We begin by rigorously providing average-case guarantees for the popular seed-chain-extend heuristic for pairwise sequence alignment under a random substitution model, showing that it is accurate and runs in close to O(n log n) time for similar sequences. Then, we will turn our focus to metagenomics: our new tool skani computes average nucleotide identity (ANI) using sparse approximate alignments, and is both more accurate and over 20 times faster than the current state-of-the-art FastANI for comparing incomplete, fragmented MAGs (metagenome assembled genomes). This was enabled by Belbasi, et al.'s work showing that minimizers are biased Jaccard estimators, whereas other k-mer sketching does not have that drawback. Finally, we will introduce sylph (unpublished work), which enables fast and accurate database search to find nearest neighbor genomes (in ANI space) of low-coverage sequenced samples by using a combination of k-mer sketching with a zero-inflated Poisson correction (45x faster than MetaPhlAn for screening databases).All of the work in this talk is joint with my brilliant PhD student Jim Shaw.Shaw J, Yu YW. Proving sequence aligners can guarantee accuracy in almost O (m log n) time through an average-case analysis of the seed-chain-extend heuristic. Genome Research (2023) 33 (7), 1175-1187 Shaw J, Yu YW. Fast and robust metagenomic sequence comparison through sparse chaining with skani. Nature Methods (2023). Zoom link: https://mit.zoom.us/j/93513735220
October 25, 2023
TBA
Hoon Cho
Yale
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2023-10-25 11:30:00
2023-10-25 13:00:00
America/New_York
TBA
Zoom link: https://mit.zoom.us/j/93513735220
October 27, 2023
Towards Interpretable and Trustworthy RL for Healthcare
Finale Doshi-Velez
Gordon McKay Professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences at Harvard University
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2023-10-27 12:30:00
2023-10-27 13:30:00
America/New_York
Towards Interpretable and Trustworthy RL for Healthcare
Abstract: Reinforcement learning has the potential to take into the many factors about a patient and identify a personalized treatment strategy that will lead to better long-term outcomes. In this talk, I will focus on the offline, or batch setting: we are giving a large amount prior clinical data, and from those interactions, our goal is to propose a better treatment policy. This setting is common in healthcare, where both safety and compliance concerns make it difficult to train a reinforcement learning agent online. However, when we cannot actually execute our proposed actions, we have to be extra careful that our reinforcement learning agent does not hallucinate bad actions as good ones.Toward this goal, I will first discuss how the limitations of batch data can actually be a feature, when it comes to interpretability. I will share an offline RL algorithm that takes advantage of the fact that we can only make inference about alternative treatments when clinicians have tried many alternatives not only to produce policies that have higher confidence statistically but also are compact enough to inspect by human experts. Next, I will touch on questions of reward design and taking advantage of the fact that our batch of data was produced by experts. Can we expose to the clinicians what their behaviors seem to be optimizing? Can we identify situations in which what a clinician claims is their reward does not match their actions? Can we perform offline RL with all the great qualities above in a way that is robust to reward misspecification? That takes into account that clinicians are in general doing their best? Our work in these areas brings us closer to realizing the potential of RL in healthcare.
D463 (Star)
November 01, 2023
TBA
Brian Hie
Stanford University
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2023-11-01 11:30:00
2023-11-01 13:00:00
America/New_York
TBA
Zoom link: https://mit.zoom.us/j/93513735220
November 08, 2023
TBA
Cory McLean
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2023-11-08 11:30:00
2023-11-08 13:00:00
America/New_York
TBA
Zoom link: https://mit.zoom.us/j/93513735220
November 15, 2023
TBA
Rohit Singh
Duke University
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2023-11-15 11:30:00
2023-11-15 13:00:00
America/New_York
TBA
Zoom link: https://mit.zoom.us/j/93513735220
November 21, 2023
High-Performance GPU Code Generation for Mining Motifs in Temporal Graphs
University of Michigan
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2023-11-21 14:00:00
2023-11-21 15:00:00
America/New_York
High-Performance GPU Code Generation for Mining Motifs in Temporal Graphs
This is hybrid meeting. The physical room is 32-D463 (Star). The Zoom registration link is https://mit.zoom.us/meeting/register/tJMtdOqgrTwsGNV_k0Nk6JjLMk-G6GFzTRyk.******************IMPORTANT NOTE ABOUT ONLINE REGISTRATION******************- The registration link for 2023 is the same as the link from 2022.- Please save the Zoom link that you receive after you register. This link will stay the same for all subsequent Fast Code seminars.- Zoom does not recognize a second registration, and will not send out the link a second time. The organizers will not be notified of any second registration.- If you have any problems with registration, please contact lindalynch@csail.mit.edu by 12pm on the day of the seminar, so that we can try to resolve it before the seminar begins.*********************************************************************Abstract:Temporal motif mining is the task of finding the occurrences of subgraph patterns withina large input temporal graph that obey the specified structural and temporal constraints.Despite its utility in several critical application domains that demand high performance(e.g., detecting fraud in financial transaction graphs), the performance of existing softwareis limited on commercial hard- ware platforms, in that it runs for tens of hours. In this talk,I will present Everest - a system that efficiently maps the workload of mining (supportsboth enumeration and counting) temporal motifs to the highly parallel GPU architecture.Using input temporal graph and a more expressive user-defined temporal motif querydefinition, Everest generates an execution plan and runtime primitives that optimize theworkload execution by exploiting the high compute throughput of a GPU. Everestgenerates motif-specific mining code to reduce long-latency memory accesses andfrequent thread divergence operations. Everest incorporates novel low-cost runtimemechanisms to enable load balancing to improve GPU hardware utilization. To supportlarge graphs that do not fit on GPU memory, Everest also supports multi-GPU executionby intelligently partitioning the edge list that prevents inter-GPU communication. Everesthides the implementation complexity of presented optimizations away from the targetedsystem user for better usability. Our evaluation shows that, using proposed optimizations,Everest improves the performance of a baseline GPU implementation by 19x, on average.Speaker Bio:Nishil Talati is an Assistant Research Scientist (Research Faculty) at the CSE departmentof University of Michigan. He earned his PhD from University of Michigan. Nishil’sresearch interests include computer architecture and systems software design forimproving the performance of modern data-intensive workloads. His research is publishedat several top-tier venues including ISCA, MICRO, HPCA, ASPLOS, and others. Nishil’swork has been recognized as the 2021 HPCA best paper award, 2023 DATE best paperhonorable mention, and 2023 IISWC best paper nominee.Relevant Publication:Y. Yuan, H. Ye, S. Vedula, W. Kaza, and N. Talati, "Everest: GPU-Accelerated System for Mining TemporalMotifs," at International Conference on Very Large Databases (VLDB 2024).
32-D463