osdi 2021 accepted papers

For any further information, please contact the PC chairs: pc-chairs-2022@eurosys.org. . 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. One important reason for the high cost is, as we observe in this paper, that many sanitizer checks are redundant the same safety property is repeatedly checked leading to unnecessarily wasted computing resources. In this paper, we show how to address this inefficiency without requiring pages to be rewritten or browsers to be modified. After request completion, an I/O device must decide either to minimize latency by immediately firing an interrupt or to optimize for throughput by delaying the interrupt, anticipating that more requests will complete soon and help amortize the interrupt cost. It then feeds those invariants and the desired safety properties to an SMT solver to check if the conjunction of the invariants and the safety properties is inductive. PET then automatically corrects results to restore full equivalence. NrOS is primarily constructed as a simple, sequential kernel with no concurrency, making it easier to develop and reason about its correctness. Our evaluation on the SPEC benchmarks shows that SanRazor can reduce the overhead of sanitizers significantly, from 73.8% to 28.062.0% for AddressSanitizer, and from 160.1% to 36.6124.4% for UndefinedBehaviorSanitizer (depending on the applied reduction scheme). A significant obstacle to using SC for practical applications is the memory overhead of the underlying cryptography. The key insight guiding our design is computation separation. However, Addra improves message latency in this architecture, which is a key performance metric for voice calls. Please identify yourself as a presenter and include your mailing address in your email. Such centralized engines are in a perfect position to censor content and violate users privacy, undermining some of the key tenets behind decentralization. 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%. Password If in doubt about whether your submission to OSDI 2021 and your upcoming submission to SOSP are the same paper or not, please contact the PC chairs by email. Swapnil Gandhi and Anand Padmanabha Iyer, Microsoft Research. Graph Neural Networks (GNNs) have gained significant attention in the recent past, and become one of the fastest growing subareas in deep learning. Authors must limit their responses to (a) correcting factual errors in the reviews or (b) directly addressing questions posed by reviewers. Only two types of supplementary material are permitted: source code described in the paper and formal proofs sketched in the paper. We present Storm, a web framework that allows developers to build MVC applications with compile-time enforcement of centrally specified data-dependent security policies. 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). In this talk, I'll speculate on how we came to this unfortunate state of affairs, and what might be done to fix it. OSDI'20: 14th USENIX Conference on Operating Systems Design and ImplementationNovember 4 - 6, 2020 ISBN: 978-1-939133-19-9 Published: 04 November 2020 Sponsors: ORACLE, VMware, Google Inc., Amazon, Microsoft Get Alerts for this Conference Save to Binder Export Citation Bibliometrics Citation count 96 Downloads (6 weeks) 317 Downloads (12 months) Compared to a state-of-the-art fuzzer, Fluffy improves the fuzzing throughput by 510 and the code coverage by 2.7 with various optimizations: in-process fuzzing, fuzzing harnesses for Ethereum clients, and semantic-aware mutation that reduces erroneous test cases. Log search and log archiving, despite being critical problems, are mutually exclusive. OSDI brings together professionals from academic and industrial backgrounds in a premier forum for discussing the design, implementation, and implications of systems software. Starting with small invariant formulas and strongest possible invariants avoids large SMT queries, improving SMT solver performance. Evaluations show that Vegito can perform 1.9 million TPC-C NewOrder transactions and 24 TPC-H-equivalent queries per second simultaneously, which retain the excellent performance of specialized OLTP and OLAP counterparts (e.g., DrTM+H and MonetDB). 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. To this end, we propose GNNAdvisor, an adaptive and efficient runtime system to accelerate various GNN workloads on GPU platforms. To enable FL developers to interpret their results in model testing, Oort enforces their requirements on the distribution of participant data while improving the duration of federated testing by cherry-picking clients. The NAL eliminates remote PM accesses to hot items without inducing extra local PM accesses. The co-chairs may then share that paper with the workshops organizers and discuss it with them. Paper Submission Information All submissions must be received by 11:59 PM AoE (UTC-12) on the day of the corresponding deadline. Authors may upload supplementary material in files separate from their submissions. Haojie Wang, Jidong Zhai, Mingyu Gao, Zixuan Ma, Shizhi Tang, and Liyan Zheng, Tsinghua University; Yuanzhi Li, Carnegie Mellon University; Kaiyuan Rong and Yuanyong Chen, Tsinghua University; Zhihao Jia, Carnegie Mellon University and Facebook. With her students, she had led research in AI, with a focus on robotics and machine learning, having concretely researched and developed a variety of autonomous robots, including teams of soccer robots, and mobile service robots. 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 Indeed, it is a prime target for powerful adversaries such as nation states. Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. If you have any questions about conflicts, please contact the program co-chairs. We observe that, due to their intended security guarantees, SC schemes are inherently oblivioustheir memory access patterns are independent of the input data. PLDI seeks outstanding research that extends and/or applies programming-language concepts to advance the field of computing. 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. 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. Leveraging these information, Pollux dynamically (re-)assigns resources to improve cluster-wide goodput, while respecting fairness and continually optimizing each DL job to better utilize those resources. 23 artifacts received the Artifacts Functional badge (88%). We implemented the ZNS+ SSD at an SSD emulator and a real SSD. The blockchain community considers this hard fork the greatest challenge since the infamous 2016 DAO hack. We have made Fluffy publicly available at https://github.com/snuspl/fluffy to contribute to the security of Ethereum. Existing systems that hide voice call metadata either require trusted intermediaries in the network or scale to only tens of users. Although SSDs can be simplified under the current ZNS interface, its counterpart LFS must bear segment compaction overhead. 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. We propose a new framework for computing the embeddings of large-scale graphs on a single machine. Submission of a response is optional. We present TEMERAIRE, a hugepage-aware enhancement of TCMALLOC to reduce CPU overheads in the applications code. Academic and industrial participants present research and experience papers that cover the full range of theory and practice of computer . OSDI brings together professionals from academic and industrial backgrounds in what has become a premier forum for discussing the design, implementation, and implications of systems software. Foreshadow was chosen as an IEEE Micro Top Pick. Finding the inductive invariant of the distributed protocol is a critical step in verifying the correctness of distributed systems, but takes a long time to do even for simple protocols. Sam Kumar, David E. Culler, and Raluca Ada Popa, University of California, Berkeley. We built an FPGA prototype of the nanoPU fast path by modifying an open-source RISC-V CPU, and evaluated its performance using cycle-accurate simulations on AWS FPGAs. OSDI'21 accepted 31 papers and 26 papers participated in the AE, a significant increase in the participate ratio: 84%, compared to OSDI'20 (70%) and SOSP'19 (61%). Yet, existing efforts randomly select FL participants, which leads to poor model and system efficiency. This post is for recording some notes from a few OSDI'21 papers that I got fun. Here, we focus on hugepage coverage. While verifying GoJournal, we found one serious concurrency bug, even though GoJournal has many unit tests. The full program will be available in May 2021. At a high level, Addra follows a template in which callers and callees deposit and retrieve messages from private mailboxes hosted at an untrusted server. Notification of conditional accept/reject for revisions: 3 March 2022. (Jan 2019) Our REPT paper won a best paper at OSDI'18 (Oct 2018) I will serve in the SOSP'19 PC. Furthermore, to enable automatic runtime optimization, GNNAdvisor incorporates a lightweight analytical model for an effective design parameter search. Poor data locality hurts an application's performance. We focus on NVMe storage devices and show that it is natural to express these semantics in the kernel and the application and only requires a modest two-bit change to the device interface. First, GNNAdvisor explores and identifies several performance-relevant features from both the GNN model and the input graph, and use them as a new driving force for GNN acceleration. For realistic workloads, KEVIN improves throughput by 68% on average. These limitations require state-of-the-art systems to distribute training across multiple machines. Call for Papers. A graph embedding is a fixed length vector representation for each node (and/or edge-type) in a graph and has emerged as the de-facto approach to apply modern machine learning on graphs. Authors may use this for content that may be of interest to some readers but is peripheral to the main technical contributions of the paper. We propose PET, the first DNN framework that optimizes tensor programs with partially equivalent transformations and automated corrections. CLP's gains come from using a tuned, domain-specific compression and search algorithm that exploits the significant amount of repetition in text logs. 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. 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. 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. Lukas Burkhalter, Nicolas Kchler, Alexander Viand, Hossein Shafagh, and Anwar Hithnawi, ETH Zrich. Metadata from voice calls, such as the knowledge of who is communicating with whom, contains rich information about peoples lives. Each new model trained with DP increases the bound on data leakage and can be seen as consuming part of a global privacy budget that should not be exceeded. 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. Therefore, developers typically find data locality issues via dynamic profiling and repair them manually. 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. This budget is a scarce resource that must be carefully managed to maximize the number of successfully trained models. Session Chairs: Sebastian Angel, University of Pennsylvania, and Malte Schwarzkopf, Brown University, Ishtiyaque Ahmad, Yuntian Yang, Divyakant Agrawal, Amr El Abbadi, and Trinabh Gupta, University of California Santa Barbara. Instead, we propose addressing the root cause of the heuristics problem by allowing software to explicitly specify to the device if submitted requests are latency-sensitive. Authors of each accepted paper must ensure that at least one author registers for the conference, and that their paper is presented in-person at the conference. Consensus bugs are bugs that make Ethereum clients transition to incorrect blockchain states and fail to reach consensus with other clients. Main conference program: 5-8 April 2022. Prior or concurrent publication in non-peer-reviewed contexts, like arXiv.org, technical reports, talks, and social media posts, is permitted. We convert five state-of-the-art PM indexes using Nap. PET discovers and applies program transformations that improve computation efficiency but only maintain partial functional equivalence. Alas, existing profiling techniques incur high overhead when used to identify data locality problems and cannot be deployed in production, where programs may exhibit previously-unseen performance problems. Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning, Oort: Efficient Federated Learning via Guided Participant Selection, PET: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections, Modernizing File System through In-Storage Indexing, Nap: A Black-Box Approach to NUMA-Aware Persistent Memory Indexes, Rearchitecting Linux Storage Stack for s Latency and High Throughput, Optimizing Storage Performance with Calibrated Interrupts, ZNS+: Advanced Zoned Namespace Interface for Supporting In-Storage Zone Compaction, DMon: Efficient Detection and Correction of Data Locality Problems Using Selective Profiling, CLP: Efficient and Scalable Search on Compressed Text Logs, Polyjuice: High-Performance Transactions via Learned Concurrency Control, Retrofitting High Availability Mechanism to Tame Hybrid Transaction/Analytical Processing, The nanoPU: A Nanosecond Network Stack for Datacenters, Beyond malloc efficiency to fleet efficiency: a hugepage-aware memory allocator, Scalable Memory Protection in the PENGLAI Enclave, NrOS: Effective Replication and Sharing in an Operating System, Addra: Metadata-private voice communication over fully untrusted infrastructure, Bringing Decentralized Search to Decentralized Services, Finding Consensus Bugs in Ethereum via Multi-transaction Differential Fuzzing, MAGE: Nearly Zero-Cost Virtual Memory for Secure Computation, Zeph: Cryptographic Enforcement of End-to-End Data Privacy, It's Time for Operating Systems to Rediscover Hardware, DistAI: Data-Driven Automated Invariant Learning for Distributed Protocols, GoJournal: a verified, concurrent, crash-safe journaling system, STORM: Refinement Types for Secure Web Applications, Horcrux: Automatic JavaScript Parallelism for Resource-Efficient Web Computation, SANRAZOR: Reducing Redundant Sanitizer Checks in C/C++ Programs, Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads, GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs, Marius: Learning Massive Graph Embeddings on a Single Machine, P3: Distributed Deep Graph Learning at Scale.

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