For instance, FAST 21 and NSDI 21 have author-notification dates after the OSDI 21 abstract-registration deadline. Cores can safely and concurrently read from their local kernel replica, eliminating remote NUMA accesses. 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. 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. Authors may submit a response to those reviews until Friday, March 5, 2021. We evaluate PrivateKube and DPF on microbenchmarks and an ML workload on Amazon Reviews data. 23 artifacts received the Artifacts Functional badge (88%). For conference information, see: . We observe that scalability challenges in training GNNs are fundamentally different from that in training classical deep neural networks and distributed graph processing; and that commonly used techniques, such as intelligent partitioning of the graph do not yield desired results. Extensive experiments show that GNNAdvisor outperforms the state-of-the-art GNN computing frameworks, such as Deep Graph Library (3.02 faster on average) and NeuGraph (up to 4.10 faster), on mainstream GNN architectures across various datasets. Tao Luo, Mingen Pan, Pierre Tholoniat, Asaf Cidon, and Roxana Geambasu, Columbia University; Mathias Lcuyer, Microsoft Research. 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. We present case studies and end-to-end applications that show how Storm lets developers specify diverse policies while centralizing the trusted code to under 1% of the application, and statically enforces security with modest type annotation overhead, and no run-time cost. Zeph enforces privacy policies cryptographically and ensures that data available to third-party applications complies with users' privacy policies. 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. Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, and Yufei Ding, University of California, Santa Barbara. 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. However, your OSDI submission must use an anonymized name for your project or system that differs from any used in such contexts. Computation separation makes it possible to construct a deep, bounded-asynchronous pipeline where graph and tensor parallel tasks can fully overlap, effectively hiding the network latency incurred by Lambdas. The key insight in blk-switch is that Linux's multi-queue storage design, along with multi-queue network and storage hardware, makes the storage stack conceptually similar to a network switch. Shaghayegh Mardani, UCLA; Ayush Goel, University of Michigan; Ronny Ko, Harvard University; Harsha V. Madhyastha, University of Michigan; Ravi Netravali, Princeton University. Grand Rapids, Michigan, United States . Furthermore, such performance can be achieved without any modification in applications, network hardware, kernel CPU schedulers and/or kernel network stack. She is the author of the textbook Interconnections (about network layers 2 and 3) and coauthor of Network Security. 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 . 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). With the help of thousands of Lambda threads, Dorylus scales GNN training to billion-edge graphs. We implement a variant of a log-structured merge tree in the storage device that not only indexes file objects, but also supports transactions and manages physical storage space. 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. Only two types of supplementary material are permitted: source code described in the paper and formal proofs sketched in the paper. They collectively make the backup fresh, columnar, and fault-tolerant, even facing millions of concurrent transactions per second. 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. Second, it innovates on the underlying cryptographic machinery and constructs a new private information retrieval scheme, FastPIR, that reduces the time to process oblivious access requests for mailboxes. 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. Academic and industrial participants present research and experience papers that cover the full range of theory and practice of computer . 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. The abstractions we design for the privacy resource mirror those defined by Kubernetes for traditional resources, but there are also major differences. Notification of conditional accept/reject for revisions: 3 March 2022. In addition, CLP outperforms Elasticsearch and Splunk Enterprise's log ingestion performance by over 13x, and we show CLP scales to petabytes of logs. 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. 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. Research Impact Score 9.24. . We compare Marius against two state-of-the-art industrial systems on a diverse array of benchmarks. Devices employ adaptive interrupt coalescing heuristics that try to balance between these opposing goals. The chairs will review paper conflicts to ensure the integrity of the reviewing process, adding or removing conflicts if necessary. By submitting a paper, you agree that at least one of the authors will attend the conference to present it. Our evaluation shows that PET outperforms existing systems by up to 2.5, by unlocking previously missed opportunities from partially equivalent transformations. Starting with small invariant formulas and strongest possible invariants avoids large SMT queries, improving SMT solver performance. The 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI '21) will take place as a virtual event on July 1416, 2021. Session Chairs: Nadav Amit, VMware Research Group, and Ada Gavrilovska, Georgia Institute of Technology, Stephen Ibanez, Alex Mallery, Serhat Arslan, and Theo Jepsen, Stanford University; Muhammad Shahbaz, Purdue University; Changhoon Kim and Nick McKeown, Stanford University. Typically, monolithic kernels share state across cores and rely on one-off synchronization patterns that are specialized for each kernel structure or subsystem. Moreover, to handle dynamic workloads, Nap adopts a fast NAL switch mechanism. . CLP's gains come from using a tuned, domain-specific compression and search algorithm that exploits the significant amount of repetition in text logs. 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. This paper demonstrates that it is possible to achieve s-scale latency using Linux kernel storage stack, even when tens of latency-sensitive applications compete for host resources with throughput-bound applications that perform read/write operations at throughput close to hardware capacity. Papers not meeting these criteria will be rejected without review, and no deadline extensions will be granted for reformatting. Qing Wang, Youyou Lu, Junru Li, and Jiwu Shu, Tsinghua University. Pollux improves scheduling performance in deep learning (DL) clusters by adaptively co-optimizing inter-dependent factors both at the per-job level and at the cluster-wide level. We describe PrivateKube, an extension to the popular Kubernetes datacenter orchestrator that adds privacy as a new type of resource to be managed alongside other traditional compute resources, such as CPU, GPU, and memory. Prepublication versions of the accepted papers from the summer submission deadline are available below. Title Page, Copyright Page, and List of Organizers | We introduce a hybrid cryptographic protocol for privacy-adhering transformations of encrypted data. A graph neural network (GNN) enables deep learning on structured graph data. For any further information, please contact the PC chairs: pc-chairs-2022@eurosys.org. For instance, the following are not sufficient grounds to specify a conflict with a PC member: they have reviewed the work before, they are employed by your competitor, they are your personal friend, they were your post-doc advisor or advisee, or they had the same advisor as you. Please identify yourself as a presenter and include your mailing address in your email. First, Fluffy mutates and executes multi-transaction test cases to find consensus bugs which cannot be found using existing fuzzers for Ethereum. 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. For example, talks may be shorter than in prior years, or some parts of the conference may be multi-tracked. 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. GoJournal is implemented in Go, and Perennial is implemented in the Coq proof assistant. 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. Concurrency control algorithms are key determinants of the performance of in-memory databases. Based on the observation that invariants are often concise in practice, DistAI starts with small invariant formulas and enumerates all strongest possible invariants that hold for all samples. Sep 2021 - Present 1 year 7 months. Currently, for large graphs, CPU servers offer the best performance-per-dollar over GPU servers. The wire-to-wire RPC response time through the nanoPU is just 69ns, an order of magnitude quicker than the best-of-breed, low latency, commercial NICs. We propose a learning-based framework that instead explicitly optimizes concurrency control via offline training to maximize performance. We will look at various problems and approaches, and for each, see if blockchain would help. 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. 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. The main contribution of this paper is GoJournal, a verified, concurrent journaling system that provides atomicity for storage applications, together with Perennial 2.0, a framework for formally specifying and verifying concurrent crash-safe systems. Pollux simultaneously considers both aspects. This budget is a scarce resource that must be carefully managed to maximize the number of successfully trained models. Accepted papers will be allowed 14 pages in the proceedings, plus references. However, Addra improves message latency in this architecture, which is a key performance metric for voice calls. 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. 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. She also has made contributions in network security, including scalable data expiration, distributed algorithms despite malicious participants, and DDOS prevention techniques. 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. Despite their extensive use for debugging and vulnerability discovery, sanitizer checks often induce a high runtime cost. PET discovers and applies program transformations that improve computation efficiency but only maintain partial functional equivalence. All deadline times are 23:59 hrs UTC. Secure Computation (SC) is a family of cryptographic primitives for computing on encrypted data in single-party and multi-party settings. Papers accompanied by nondisclosure agreement forms will not be considered. 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. We develop MAGE, an execution engine for SC that efficiently runs SC computations that do not fit in memory. Welcome to the SOSP 2021 Website. She has been recognized with many industry honors including induction into the National Academy of Engineering, the Inventor Hall of Fame, The Internet Hall of Fame, Washington State Academy of Science, and lifetime achievement awards from USENIX and SIGCOMM. In this paper, we propose Oort to improve the performance of federated training and testing with guided participant selection.
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