The conference papers and full proceedings are available to registered attendees now and will be available to everyone beginning Wednesday, July 14, 2021. We discuss the design and implementation of TEMERAIRE including strategies for hugepage-aware memory layouts to maximize hugepage coverage and to minimize fragmentation overheads. Message from the Program Co-Chairs. Horcrux-compliant web servers perform offline analysis of all the JavaScript code on any frame they serve to conservatively identify, for every JavaScript function, the union of the page state that the function could access across all loads of that page. These limitations require state-of-the-art systems to distribute training across multiple machines. Our evaluation shows that, compared to existing participant selection mechanisms, Oort improves time-to-accuracy performance by 1.2X-14.1X and final model accuracy by 1.3%-9.8%, while efficiently enforcing developer-specified model testing criteria at the scale of millions of clients. While several new GNN architectures have been proposed, the scale of real-world graphsin many cases billions of nodes and edgesposes challenges during model training. All submissions will be treated as confidential prior to publication on the USENIX OSDI 21 website; rejected submissions will be permanently treated as confidential. Notification of conditional accept/reject for revisions: 3 March 2022. Session Chairs: Dushyanth Narayanan, Microsoft Research, and Gala Yadgar, TechnionIsrael Institute of Technology, Jinhyung Koo, Junsu Im, Jooyoung Song, and Juhyung Park, DGIST; Eunji Lee, Soongsil University; Bryan S. Kim, Syracuse University; Sungjin Lee, DGIST. The hybrid segment recycling chooses a proper block reclaiming policy between segment compaction and threaded logging based on their costs. We present DPF (Dominant Private Block Fairness) a variant of the popular Dominant Resource Fairness (DRF) algorithmthat is geared toward the non-replenishable privacy resource but enjoys similar theoretical properties as DRF. After three years working on web-based collaboration systems at a startup in North Carolina, he joined Sprint's Advanced Technology Lab in Burlingame, California, in 1998, working on cloud computing and network monitoring. CLP's gains come from using a tuned, domain-specific compression and search algorithm that exploits the significant amount of repetition in text logs. By monitoring the status of each job during training, Pollux models how their goodput (a novel metric we introduce that combines system throughput with statistical efficiency) would change by adding or removing resources. Kernel code requires manual memory management and type-unsafe code and must efficiently handle complex, asynchronous events. Professor Veloso earned a Bachelor and Master of Science degrees in Electrical and Computer Engineering from Instituto Superior Tecnico in Lisbon, Portugal, a Master of Arts in Computer Science from Boston University, and Master of Science and PhD in Computer Science from Carnegie Mellon University. Indeed, it is a prime target for powerful adversaries such as nation states. Some recent schedulers choose job resources for users, but do so without awareness of how DL training can be re-optimized to better utilize the provided resources. Under different configurations of TPC-C and TPC-E, Polyjuice can achieve throughput numbers higher than the best of existing algorithms by 15% to 56%. Hence, kernel developers are constantly refining synchronization within OS kernels to improve scalability at the risk of introducing subtle bugs. We present NrOS, a new OS kernel with a safer approach to synchronization that runs many POSIX programs. Manuela M. Veloso is the Head of J.P. Morgan AI Research, which pursues fundamental research in areas of core relevance to financial services, including data mining and cryptography, machine learning, explainability, and human-AI interaction. See the Preview Session page for an overview of the topics covered in the program. ), Program Co-Chairs: Angela Demke Brown, University of Toronto, and Jay Lorch, Microsoft Research. In this paper, we present P3, a system that focuses on scaling GNN model training to large real-world graphs in a distributed setting. High-performance tensor programs are critical for efficiently deploying deep neural network (DNN) models in real-world tasks. Zeph enforces privacy policies cryptographically and ensures that data available to third-party applications complies with users' privacy policies. In this paper, we present Vegito, a distributed in-memory HTAP system that embraces freshness and performance with the following three techniques: (1) a lightweight gossip-style scheme to apply logs on backups consistently; (2) a block-based design for multi-version columnar backups; (3) a two-phase concurrent updating mechanism for the tree-based index of backups. Session Chairs: Ryan Huang, Johns Hopkins University, and Manos Kapritsos, University of Michigan, Jianan Yao, Runzhou Tao, Ronghui Gu, Jason Nieh, Suman Jana, and Gabriel Ryan, Columbia University. Papers must be in PDF format and must be submitted via the submission form. Distributed systems are notoriously hard to implement correctly due to non-determinism. Our evaluation shows that PET outperforms existing systems by up to 2.5, by unlocking previously missed opportunities from partially equivalent transformations. A.H. Hunter, Jane Street Capital; Chris Kennelly, Paul Turner, Darryl Gove, Tipp Moseley, and Parthasarathy Ranganathan, Google. This yielded 6% fewer TLB miss stalls, and 26% reduction in memory wasted due to fragmentation. Of the 26 submitted artifacts: 26 artifacts received the Artifacts Available badge (100%). DistAI generates data by simulating the distributed protocol at different instance sizes and recording states as samples. She also invented the spanning tree algorithm, which transformed Ethernet from a technology that supported a few hundred nodes, to something that can support large networks. MAGE outperforms the OS virtual memory system by up to an order of magnitude, and in many cases, runs SC computations that do not fit in memory at nearly the same speed as if the underlying machines had unbounded physical memory to fit the entire computation. With the help of thousands of Lambda threads, Dorylus scales GNN training to billion-edge graphs. For any further information, please contact the PC chairs: pc-chairs-2022@eurosys.org. Based on this observation, P3 proposes a new approach for distributed GNN training. Prior or concurrent publication in non-peer-reviewed contexts, like arXiv.org, technical reports, talks, and social media posts, is permitted. Responses should be limited to clarifying the submitted work. Professor Veloso has been recognized with a multiple honors, including being a Fellow of the ACM, IEEE, AAAS, and AAAI. Mothy joined the Computer Science Department ETH Zurich in January 2007 and was named Fellow of the ACM in 2013 for contributions to operating systems and networking research. sosp ACM Symposium on Operating Systems Principles. Storm ensures security using a Security Typed ORM that refines the (type) abstractions of each layer of the MVC API with logical assertions that describe the data produced and consumed by the underlying operation and the users allowed access to that data. And yet, they continue to rely on centralized search engines and indexers to help users access the content they seek and navigate the apps. (Jan 2019) Our REPT paper won a best paper at OSDI'18 (Oct 2018) I will serve in the SOSP'19 PC. Memory allocation represents significant compute cost at the warehouse scale and its optimization can yield considerable cost savings. We identify that current systems for learning the embeddings of large-scale graphs are bottlenecked by data movement, which results in poor resource utilization and inefficient training. Password When uploading your OSDI 2021 reviews for your submission to SOSP, you can optionally append a note about how you addressed the reviews and comments. Here, we focus on hugepage coverage. When registering your abstract, you must provide information about conflicts with PC members. Further, Vegito can recover from cascading machine failures by using the columnar backup in less than 60 ms. Authors are also encouraged to contact the program co-chairs, osdi21chairs@usenix.org, if needed to relate their OSDI submissions to relevant submissions of their own that are simultaneously under review or awaiting publication at other venues. Amy Tai, VMware Research; Igor Smolyar, Technion Israel Institute of Technology; Michael Wei, VMware Research; Dan Tsafrir, Technion Israel Institute of Technology and VMware Research. Existing decentralized systems like Steemit, OpenBazaar, and the growing number of blockchain apps provide alternatives to existing services. However, Addra improves message latency in this architecture, which is a key performance metric for voice calls. This paper presents Zeph, a system that enables users to set privacy preferences on how their data can be shared and processed. Moreover, as of October 2020, a review of the 50 most cited empirical papers that list personality as a keyword indicates that all 50 papers were authored by people with insti tutional affiliations in the United States, Canada, Germany, the UK, and New Zealand, and only three papers included samples outside of these regions (see Supplementary Third, GNNAdvisor capitalizes on the GPU memory hierarchy for acceleration by gracefully coordinating the execution of GNNs according to the characteristics of the GPU memory structure and GNN workloads. The papers will be available online to everyone beginning on the first day of the conference, July 14, 2021. We implemented the ZNS+ SSD at an SSD emulator and a real SSD. Professor Veloso is the Past President of AAAI (the Association for the Advancement of Artificial Intelligence), and the co-founder, Trustee, and Past President of RoboCup. Academic and industrial participants present research and experience papers that cover the full range of theory and practice of computer . Weak Links in Authentication Chains: A Large-scale Analysis of Email Sender Spoofing Attacks Call for Papers. She also has made contributions in network security, including scalable data expiration, distributed algorithms despite malicious participants, and DDOS prevention techniques. As has been standard practice in OSDI and SOSP in recent years, we will allow authors to submit quick responses to PC reviews: they will be made available to the PC before the final online discussion and PC meeting. Furthermore, by combining SanRazor with an existing sanitizer reduction tool ASAP, we show synergistic effect by reducing the runtime cost to only 7.0% with a reasonable tradeoff of security. Hence, CLP enables efficient search and analytics on archived logs, something that was impossible without it. Devices employ adaptive interrupt coalescing heuristics that try to balance between these opposing goals. DMons targeted optimizations provide 16.83% speedup on average (up to 53.14%), compared to a baseline that uses the highest level of compiler optimization. Paper Submission Information All submissions must be received by 11:59 PM AoE (UTC-12) on the day of the corresponding deadline. This post is for recording some notes from a few OSDI'21 papers that I got fun. We demonstrate that the hardware thread scheduler is able to lower RPC tail response time by about 5 while enabling the system to sustain 20% higher load, relative to traditional thread scheduling techniques. Jiachen Wang, Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University; Shanghai AI Laboratory; Engineering Research Center for Domain-specific Operating Systems, Ministry of Education, China; Ding Ding, Department of Computer Science, New York University; Huan Wang, Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University; Shanghai AI Laboratory; Engineering Research Center for Domain-specific Operating Systems, Ministry of Education, China; Conrad Christensen, Department of Computer Science, New York University; Zhaoguo Wang and Haibo Chen, Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University; Shanghai AI Laboratory; Engineering Research Center for Domain-specific Operating Systems, Ministry of Education, China; Jinyang Li, Department of Computer Science, New York University. Nico Lehmann and Rose Kunkel, UC San Diego; Jordan Brown, Independent; Jean Yang, Akita Software; Niki Vazou, IMDEA Software Institute; Nadia Polikarpova, Deian Stefan, and Ranjit Jhala, UC San Diego. GoJournal is implemented in Go, and Perennial is implemented in the Coq proof assistant. The novel aspect of the nanoPU is the design of a fast path between the network and applications---bypassing the cache and memory hierarchy, and placing arriving messages directly into the CPU register file. Grand Rapids, Michigan, United States . . She has a PhD in computer science from MIT. Collaboration: You have a collaboration on a project, publication, grant proposal, program co-chairship, or editorship within the past two years (December 2018 through March 2021). AI enables principled representation of knowledge, complex strategy optimization, learning from data, and support to human decision making. The OSDI Symposium emphasizes innovative research as well as quantified or insightful experiences in systems design and implementation. Session Chairs: Deniz Altinbken, Google, and Rashmi Vinayak, Carnegie Mellon University, Tanvir Ahmed Khan and Ian Neal, University of Michigan; Gilles Pokam, Intel Corporation; Barzan Mozafari and Baris Kasikci, University of Michigan. Radia Perlman is a Fellow at Dell Technologies. Upon these two primitives, our system can scale to thousands of concurrent enclaves with high resource utilization and eliminate the high-cost initialization of secure memory using fork-style enclave creation without weakening the security guarantees. PLDI is a premier forum for programming language research, broadly construed, including design, implementation, theory, applications, and performance. In addition, CLP outperforms Elasticsearch and Splunk Enterprise's log ingestion performance by over 13x, and we show CLP scales to petabytes of logs. Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. We will look at various problems and approaches, and for each, see if blockchain would help. The key insight guiding our design is computation separation. We propose Marius, a system for efficient training of graph embeddings that leverages partition caching and buffer-aware data orderings to minimize disk access and interleaves data movement with computation to maximize utilization. Today, privacy controls are enforced by data curators with full access to data in the clear.

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