Lukas Burkhalter, Nicolas Kchler, Alexander Viand, Hossein Shafagh, and Anwar Hithnawi, ETH Zrich. Radia Perlman is a Fellow at Dell Technologies. As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings). 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. Today, privacy controls are enforced by data curators with full access to data in the clear. Professor Veloso has been recognized with a multiple honors, including being a Fellow of the ACM, IEEE, AAAS, and AAAI. A graph neural network (GNN) enables deep learning on structured graph data. All submissions will be treated as confidential prior to publication on the USENIX OSDI 21 website; rejected submissions will be permanently treated as confidential. There are two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which are expensive to purchase and maintain, and 2) limited memory on GPUs cannot scale to today's billion-edge graphs. (Oct 2018) Awarded an Intel Faculty Grant for Research on automated performance optimization (Sep. 2018) Our paper on Foreshadow is accepted to appear at USENIX Security. In some cases, the quality of these artifacts is as important as that of the document itself. This paper describes the design, implementation, and evaluation of Addra, the first system for voice communication that hides metadata over fully untrusted infrastructure and scales to tens of thousands of users. Just using Lambdas on top of CPU servers offers up to 2.75 more performance-per-dollar than training only with CPU servers. HotCRP.com signin Sign in using your HotCRP.com account. OSDI '21 Technical Sessions All the times listed below are in Pacific Daylight Time (PDT). They collectively make the backup fresh, columnar, and fault-tolerant, even facing millions of concurrent transactions per second. Jiang Zhang, University of Southern California; Shuai Wang, HKUST; Manuel Rigger, Pinjia He, and Zhendong Su, ETH Zurich. Our evaluation shows that NrOS scales to 96 cores with performance that nearly always dominates Linux at scale, in some cases by orders of magnitude, while retaining much of the simplicity of a sequential kernel. Sep 2021 - Present 1 year 7 months. Authors may submit a response to those reviews until Friday, March 5, 2021. Perennial 2.0 makes this possible by introducing several techniques to formalize GoJournals specification and to manage the complexity in the proof of GoJournals implementation. (Visa applications can take at least 30 working days to process.) All the times listed below are in Pacific Daylight Time (PDT). Prior or concurrent publication in non-peer-reviewed contexts, like arXiv.org, technical reports, talks, and social media posts, is permitted. We present DistAI, a data-driven automated system for learning inductive invariants for distributed protocols. We demonstrate the above using design, implementation and evaluation of blk-switch, a new Linux kernel storage stack architecture. 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. This is the first OSDI in an odd year as OSDI moves to a yearly cadence. Hence, CLP enables efficient search and analytics on archived logs, something that was impossible without it. The key to our solution, Horcrux, is to account for the non-determinism intrinsic to web page loads and the constraints placed by the browsers API for parallelism. PET then automatically corrects results to restore full equivalence. 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. Sijie Shen, Rong Chen, Haibo Chen, and Binyu Zang, Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University; Shanghai Artificial Intelligence Laboratory; Engineering Research Center for Domain-specific Operating Systems, Ministry of Education, China. Widely used log-search tools like Elasticsearch and Splunk Enterprise index the logs to provide fast search performance, yet the size of the index is within the same order of magnitude as the raw log size. 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. 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. Swapnil Gandhi and Anand Padmanabha Iyer, Microsoft Research. Petuum Awarded OSDI 2021 Best Paper for Goodput-Optimized Deep Learning Research Petuum CASL research and engineering team's Pollux technical paper on adaptive scheduling for optimized. 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. 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 conference information, . We argue that a key-value interface between a file system and an SSD is superior to the legacy block interface by presenting KEVIN. The NAL eliminates remote PM accesses to hot items without inducing extra local PM accesses. We propose a new framework for computing the embeddings of large-scale graphs on a single machine. In experiments with real DL jobs and with trace-driven simulations, Pollux reduces average job completion times by 37-50% relative to state-of-the-art DL schedulers, even when they are provided with ideal resource and training configurations for every job. For instance, FAST 21 and NSDI 21 have author-notification dates after the OSDI 21 abstract-registration deadline. With the help of thousands of Lambda threads, Dorylus scales GNN training to billion-edge graphs. Authors may upload supplementary material in files separate from their submissions. 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. Used Zotero to organize papers about the stress and diffusion between anode and electrolyte and made a summary . USENIX new Date().getFullYear()>document.write(new Date().getFullYear()); Grants for Black Computer Science Students Application, Propose an interesting, compelling solution, Demonstrate the practicality and benefits of the solution, Clearly describe the paper's contributions, Clearly articulate the advances beyond previous work. Submissions may include as many additional pages as needed for references but not for appendices. Evaluation on a four-node machine with Optane DC Persistent Memory shows that Nap can improve the throughput by up to 2.3 and 1.56 under write-intensive and read-intensive workloads, respectively. Based on the observation that real-world workloads always feature skewed access patterns, Nap introduces a NUMA-aware layer (NAL) on the top of existing concurrent PM indexes, and steers accesses to hot items to this layer. SanRazor adopts a novel hybrid approach it captures both dynamic code coverage and static data dependencies of checks, and uses the extracted information to perform a redundant check analysis. He joined Intel Research at Berkeley in April 2002 as a principal architect of PlanetLab, an open, shared platform for developing and deploying planetary-scale services. For general conference information, see https://www . Typically, monolithic kernels share state across cores and rely on one-off synchronization patterns that are specialized for each kernel structure or subsystem. KEVIN combines a fast, lightweight, and POSIX compliant file system with a key-value storage device that performs in-storage indexing. Our evaluation shows that PET outperforms existing systems by up to 2.5, by unlocking previously missed opportunities from partially equivalent transformations. This motivates the need for a new approach to data privacy that can provide strong assurance and control to users. Responses should be limited to clarifying the submitted work. Submissions violating the detailed formatting and anonymization rules will not be considered for review. Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury, University of Michigan. 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. Conference Dates: Apr 12, 2021 - Apr 14, 2021. She is the author of the textbook Interconnections (about network layers 2 and 3) and coauthor of Network Security. USENIX discourages program co-chairs from submitting papers to the conferences they organize, although they are allowed to do so. Sanitizers detect unsafe actions such as invalid memory accesses by inserting checks that are validated during a programs execution. Main conference program: 5-8 April 2022. 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. 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. How can we design systems that will be reliable despite misbehaving participants? 23 artifacts received the Artifacts Functional badge (88%). The copyback-aware block allocation considers different copy costs at different copy paths within the SSD. 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. sosp ACM Symposium on Operating Systems Principles. Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. 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. Consensus bugs are extremely rare but can be exploited for network split and theft, which cause reliability and security-critical issues in the Ethereum ecosystem. Existing decentralized systems like Steemit, OpenBazaar, and the growing number of blockchain apps provide alternatives to existing services. Thanks to selective profiling, DMons profiling overhead is 1.36% on average, making it feasible for production use. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. We present TEMERAIRE, a hugepage-aware enhancement of TCMALLOC to reduce CPU overheads in the applications code. Sponsored by USENIX in cooperation with ACM SIGOPS. DistAI generates data by simulating the distributed protocol at different instance sizes and recording states as samples. Pollux promotes fairness among DL jobs competing for resources based on a more meaningful measure of useful job progress, and reveals a new opportunity for reducing DL cost in cloud environments. We demonstrate that Marius achieves the same level of accuracy but is up to one order of magnitude faster. We evaluate PrivateKube and DPF on microbenchmarks and an ML workload on Amazon Reviews data. Consensus bugs are bugs that make Ethereum clients transition to incorrect blockchain states and fail to reach consensus with other clients. The program co-chairs will use this information at their discretion to preserve the anonymity of the review process without jeopardizing the outcome of the current OSDI submission. Log search and log archiving, despite being critical problems, are mutually exclusive. . Authors must make a good faith effort to anonymize their submissions, and they should not identify themselves or their institutions either explicitly or by implication (e.g., through the references or acknowledgments). If your accepted paper should not be published prior to the event, please notify production@usenix.org. For general conference information, see https://www.usenix.org/conference/osdi22. Zeph executes privacy-adhering data transformations in real-time and scales to thousands of data sources, allowing it to support large-scale low-latency data stream analytics. 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. NrOS is primarily constructed as a simple, sequential kernel with no concurrency, making it easier to develop and reason about its correctness. Welcome to the SOSP 2021 Website. Secure Computation (SC) is a family of cryptographic primitives for computing on encrypted data in single-party and multi-party settings. 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. 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. Proceedings Front Matter However, memory allocation decisions also impact overall application performance via data placement, offering opportunities to improve fleetwide productivity by completing more units of application work using fewer hardware resources. PET discovers and applies program transformations that improve computation efficiency but only maintain partial functional equivalence. 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). To achieve low overhead, selective profiling gathers runtime execution information selectively and incrementally. Machine learning (ML) models trained on personal data have been shown to leak information about users. This post is for recording some notes from a few OSDI'21 papers that I got fun. Papers must be in PDF format and must be submitted via the submission form. She developed the technology for making network routing self-stabilizing, largely self-managing, and scalable. To resolve the problem, we propose a new LFS-aware ZNS interface, called ZNS+, and its implementation, where the host can offload data copy operations to the SSD to accelerate segment compaction. If your paper is accepted and you need an invitation letter to apply for a visa to attend the conference, please contact conference@usenix.org as soon as possible. For more details on the submission process, and for templates to use with LaTeX, Word, etc., authors should consult the detailed submission requirements. J.P. Morgan AI Research partners with applied data analytics teams across the firm as well as with leading academic institutions globally. Academic and industrial participants present research and experience papers that cover the full range of theory . This talk will discuss several examples with very different solutions. Using selective profiling, we build DMon, a system that can automatically locate data locality problems in production, identify access patterns that hurt locality, and repair such patterns using targeted optimizations. Our evaluation shows that DistAI successfully verifies 13 common distributed protocols automatically and outperforms alternative methods both in the number of protocols it verifies and the speed at which it does so, in some cases by more than two orders of magnitude. 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. 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. 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. 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. Additionally, there is no assurance that data processing and handling comply with the claimed privacy policies. We present selective profiling, a technique that locates data locality problems with low-enough overhead that is suitable for production use. This paper presents the design and implementation of CLP, a tool capable of losslessly compressing unstructured text logs while enabling fast searches directly on the compressed data. 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.
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