Metadata from voice calls, such as the knowledge of who is communicating with whom, contains rich information about peoples lives. With an aim to improve time-to-accuracy performance in model training, Oort prioritizes the use of those clients who have both data that offers the greatest utility in improving model accuracy and the capability to run training quickly. Only two types of supplementary material are permitted: source code described in the paper and formal proofs sketched in the paper. 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. Abstract registrations that do not provide sufficient information to understand the topic and contribution (e.g., empty abstracts, placeholder abstracts, or trivial abstracts) will be rejected, thereby precluding paper submission. We also show that Marius can scale training to datasets an order of magnitude beyond a single machine's GPU and CPU memory capacity, enabling training of configurations with more than a billion edges and 550 GB of total parameters on a single machine with 16 GB of GPU memory and 64 GB of CPU memory. Instead of choosing among a small number of known algorithms, our approach searches in a "policy space" of fine-grained actions, resulting in novel algorithms that can outperform existing algorithms by specializing to a given workload. JEL codes: Q18, Q28, Q57 . Because DistAI starts with the strongest possible invariants, if the SMT solver fails, DistAI does not need to discard failed invariants, but knows to monotonically weaken them and try again with the solver, repeating the process until it eventually succeeds. This talk will discuss several examples with very different solutions. The NVMe zoned namespace (ZNS) is emerging as a new storage interface, where the logical address space is divided into fixed-sized zones, and each zone must be written sequentially for flash-memory-friendly access. P3 exposes a simple API that captures many different classes of GNN architectures for generality. ), Program Co-Chairs: Angela Demke Brown, University of Toronto, and Jay Lorch, Microsoft Research. The experimental results show that Penglai can support 1,000s enclave instances running concurrently and scale up to 512GB secure memory with both encryption and integrity protection. Paper abstracts and proceedings front matter are available to everyone now. A glance at this year's OSDI program shows that Operating Systems are a small niche topic for this conference, not even meriting their own full session. For conference information, see: . Used Zotero to organize papers about the stress and diffusion between anode and electrolyte and made a summary . Although SSDs can be simplified under the current ZNS interface, its counterpart LFS must bear segment compaction overhead. Concretely, Dorylus is 1.22 faster and 4.83 cheaper than GPU servers for massive sparse graphs. Authors may upload supplementary material in files separate from their submissions. Pollux simultaneously considers both aspects. PET discovers and applies program transformations that improve computation efficiency but only maintain partial functional equivalence. DeSearch then introduces a witness mechanism to make sure the completed tasks can be reused across different pipelines, and to make the final search results verifiable by end users. Jaehyun Hwang and Midhul Vuppalapati, Cornell University; Simon Peter, UT Austin; Rachit Agarwal, Cornell University. USENIX new Date().getFullYear()>document.write(new Date().getFullYear()); Grants for Black Computer Science Students Application, Title Page, Copyright Page, and List of Organizers, OSDI '21 Proceedings Interior (PDF, best for mobile devices). Session Chairs: Gennady Pekhimenko, University of Toronto / Vector Institute, and Shivaram Venkataraman, University of WisconsinMadison, Aurick Qiao, Petuum, Inc. and Carnegie Mellon University; Sang Keun Choe and Suhas Jayaram Subramanya, Carnegie Mellon University; Willie Neiswanger, Petuum, Inc. and Carnegie Mellon University; Qirong Ho, Petuum, Inc.; Hao Zhang, Petuum, Inc. and UC Berkeley; Gregory R. Ganger, Carnegie Mellon University; Eric P. Xing, MBZUAI, Petuum, Inc., and Carnegie Mellon University. Most existing schedulers expect users to specify the number of resources for each job, often leading to inefficient resource use. 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. Additionally, there is no assurance that data processing and handling comply with the claimed privacy policies. We built a functional NFSv3 server, called GoNFS, to use GoJournal. 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. The 20th ACM Workshop on Hot Topics in Networks (HotNets 2021) will bring together researchers in computer networks and systems to engage in a lively debate on the theory and practice of computer networking. In this paper, we propose Oort to improve the performance of federated training and testing with guided participant selection. Submitted November 12, 2021 Accepted January 20, 2022. The OSDI Symposium emphasizes innovative research as well as quantified or insightful experiences in systems design and implementation. Fluffy found two new consensus bugs in the most popular Geth Ethereum client which were exploitable on the live Ethereum mainnet. OSDI will provide an opportunity for authors to respond to reviews prior to final consideration of the papers at the program committee meeting. Despite their extensive use for debugging and vulnerability discovery, sanitizer checks often induce a high runtime cost. Responses should be limited to clarifying the submitted work. For example, traditional compute resources are replenishable while privacy is not: a CPU can be regained after a model finishes execution while privacy budget cannot. However, a plethora of recent data breaches show that even widely trusted service providers can be compromised. The blockchain community considers this hard fork the greatest challenge since the infamous 2016 DAO hack. All deadline times are 23:59 hrs UTC. 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. Second, Fluffy uses multiple existing Ethereum clients that independently implement the specification as cross-referencing oracles. 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. 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. 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. 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. For general conference information, see https://www.usenix.org/conference/osdi22. The chairs will review paper conflicts to ensure the integrity of the reviewing process, adding or removing conflicts if necessary. We implement and evaluate a suite of applications, including MICA, Raft and Set Algebra for document retrieval; and we demonstrate that the nanoPU can be used as a high performance, programmable alternative for one-sided RDMA operations. Research Impact Score 9.24. . Differential privacy (DP) enables model training with a guaranteed bound on this leakage. A scientific paper consists of a constellation of artifacts that extend beyond the document itself: software, hardware, evaluation data and documentation, raw survey results, mechanized proofs, models, test suites, benchmarks, and so on. The file system performance of the proposed ZNS+ storage system was 1.33--2.91 times better than that of the normal ZNS-based storage system. DistAI: Data-Driven Automated Invariant Learning for Distributed Protocols Jianan Yao, Runzhou Tao, Ronghui Gu, Jason Nieh . This yielded 6% fewer TLB miss stalls, and 26% reduction in memory wasted due to fragmentation. This approach misses possible optimization opportunities as transformations that only preserve equivalence on subsets of the output tensors are excluded. Welcome to the 2021 USENIX Annual Technical Conference (ATC '21) submissions site! Tej Chajed, MIT CSAIL; Joseph Tassarotti, Boston College; Mark Theng, MIT CSAIL; Ralf Jung, MPI-SWS; M. Frans Kaashoek and Nickolai Zeldovich, MIT CSAIL. First, Fluffy mutates and executes multi-transaction test cases to find consensus bugs which cannot be found using existing fuzzers for Ethereum. Submissions violating the detailed formatting and anonymization rules will not be considered for review. In particular, responses must not include new experiments or data, describe additional work completed since submission, or promise additional work to follow. Timothy Roscoe is a Full Professor in the Systems Group of the Computer Science Department at ETH Zurich, where he works on operating systems, networks, and distributed systems, and is currently head of department. . We develop rigorous theoretical foundations to simplify equivalence examination and correction for partially equivalent transformations, and design an efficient search algorithm to quickly discover highly optimized programs by combining fully and partially equivalent optimizations at the tensor, operator, and graph levels. Authors are required to register abstracts by 3:00 p.m. PST on December 3, 2020, and to submit full papers by 3:00 p.m. PST on December 10, 2020. Even the little publishable OS work that is not based on Linux still assumes the same simplistic hardware model (essentially a multiprocessor VAX) that bears little resemblance to modern reality. Main conference program: 5-8 April 2022. For any further information, please contact the PC chairs: pc-chairs-2022@eurosys.org. 2019 - Present. In particular, I'll argue for re-engaging with what computer hardware really is today and give two suggestions (among many) about how the OS research community can usefully do this, and exploit what is actually a tremendous opportunity. We discuss the design and implementation of TEMERAIRE including strategies for hugepage-aware memory layouts to maximize hugepage coverage and to minimize fragmentation overheads. Many application domains can benefit from hybrid transaction/analytical processing (HTAP) by executing queries on real-time datasets produced by concurrent transactions. Professor Veloso is on leave from Carnegie Mellon University as the Herbert A. Simon University Professor in the School of Computer Science, and the past Head of the Machine Learning Department. Lifting predicates and crash framing make the specification easy to use for developers, and logically atomic crash specifications allow for modular reasoning in GoJournal, making the proof tractable despite complex concurrency and crash interleavings. 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. Advisor: You have a past or present association as thesis advisor or advisee. Machine learning (ML) models trained on personal data have been shown to leak information about users. Youngseok Yang, Seoul National University; Taesoo Kim, Georgia Institute of Technology; Byung-Gon Chun, Seoul National University and FriendliAI. We present the nanoPU, a new NIC-CPU co-design to accelerate an increasingly pervasive class of datacenter applications: those that utilize many small Remote Procedure Calls (RPCs) with very short (s-scale) processing times. PC members are not required to read supplementary material when reviewing the paper, so each paper should stand alone without it. However, with the increasingly speedy transactions and queries thanks to large memory and fast interconnect, commodity HTAP systems have to make a tradeoff between data freshness and performance degradation. 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 Our further evaluation on 38 CVEs from 10 commonly-used programs shows that SanRazor reduced checks suffice to detect at least 33 out of the 38 CVEs. Hence, kernel developers are constantly refining synchronization within OS kernels to improve scalability at the risk of introducing subtle bugs. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. KEVIN combines a fast, lightweight, and POSIX compliant file system with a key-value storage device that performs in-storage indexing. 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. For realistic workloads, KEVIN improves throughput by 68% on average. While compiler-based techniques have been proposed to improve data locality, they depend on heuristics, which can sometimes hurt performance. Horcruxs JavaScript scheduler then uses this information to judiciously parallelize JavaScript execution on the client-side so that the end-state is identical to that of a serial execution, while minimizing coordination and offloading overheads. Yet, existing efforts randomly select FL participants, which leads to poor model and system efficiency. VLDB 2021: Venue Tivoli Hotel & Congress Center Arni Magnussons Gade 2 1577 Copenhagen, Denmark +45 3268 4300 In-person attendees can purchase tickets for the park / gardens with a 15% discount, which is a special offer by Tivoli Hotel & Congress Center to VLDB 2021 attendees. 64 papers accepted out of 341 submitted. Our approach effectively eliminates high communication and partitioning overheads, and couples it with a new pipelined push-pull parallelism based execution strategy for fast model training. However, Addra improves message latency in this architecture, which is a key performance metric for voice calls. We present selective profiling, a technique that locates data locality problems with low-enough overhead that is suitable for production use. In contrast, CLP achieves significantly higher compression ratio than all commonly used compressors, yet delivers fast search performance that is comparable or even better than Elasticsearch and Splunk Enterprise. Grand Rapids, Michigan, United States . Across a wide range of pages, phones, and mobile networks covering web workloads in both developed and emerging regions, Horcrux reduces median browser computation delays by 31-44% and page load times by 18-37%. . DeSearch uses trusted hardware to build a network of workers that execute a pipeline of small search engine tasks (crawl, index, aggregate, rank, query). The copyback-aware block allocation considers different copy costs at different copy paths within the SSD. Poor data locality hurts an application's performance. Despite having the same end goals as traditional ML, FL executions differ significantly in scale, spanning thousands to millions of participating devices. NrOS replicates kernel state on each NUMA node and uses operation logs to maintain strong consistency between replicas. Ankit Bhardwaj and Chinmay Kulkarni, University of Utah; Reto Achermann, University of British Columbia; Irina Calciu, VMware Research; Sanidhya Kashyap, EPFL; Ryan Stutsman, University of Utah; Amy Tai and Gerd Zellweger, VMware Research. PLDI seeks outstanding research that extends and/or applies programming-language concepts to advance the field of computing. Authors may submit a response to those reviews until Friday, March 5, 2021. In the Ethereum network, decentralized Ethereum clients reach consensus through transitioning to the same blockchain states according to the Ethereum specification. 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. Currently, for large graphs, CPU servers offer the best performance-per-dollar over GPU servers. This fast path contains programmable hardware support for low latency transport and congestion control as well as hardware support for efficient load balancing of RPCs to cores. The paper review process is double-blind. 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. Typically, monolithic kernels share state across cores and rely on one-off synchronization patterns that are specialized for each kernel structure or subsystem. (Registered attendees: Sign in to your USENIX account to download these files. 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. 1 Acknowledgements: Paper prepared for the post-conference workshop on Food for Thought: Economic Analysis in Anticipation of the Next Farm Bill at the Agricultural and Applied Economics Association annual meeting, Austin, TX . Forgot your password? There is no explicit limit to the response, but authors are strongly encouraged to keep it under 500 words; reviewers are neither required nor expected to read excessively long responses. CLP's gains come from using a tuned, domain-specific compression and search algorithm that exploits the significant amount of repetition in text logs. The ZNS+ also allows each zone to be overwritten with sparse sequential write requests, which enables the LFS to use threaded logging-based block reclamation instead of segment compaction. Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. Furthermore, to enable automatic runtime optimization, GNNAdvisor incorporates a lightweight analytical model for an effective design parameter search. When further combined with a simple caching strategy, our evaluation shows that P3 is able to outperform existing state-of-the-art distributed GNN frameworks by up to 7. Using this property, MAGE calculates the memory access pattern ahead of time and uses it to produce a memory management plan. Session Chairs: Moshe Gabel, University of Toronto, and Joseph Gonzalez, University of California, Berkeley, John Thorpe, Yifan Qiao, Jonathan Eyolfson, and Shen Teng, UCLA; Guanzhou Hu, UCLA and University of Wisconsin, Madison; Zhihao Jia, CMU; Jinliang Wei, Google Brain; Keval Vora, Simon Fraser; Ravi Netravali, Princeton University; Miryung Kim and Guoqing Harry Xu, UCLA.

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