Distributed systems are notoriously hard to implement correctly due to non-determinism. USENIX Security '21 has three submission deadlines. 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. 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. Proceedings Front Matter A hardware-accelerated thread scheduler makes sub-nanosecond decisions, leading to high CPU utilization and low tail response time for RPCs. Questions? Submissions violating the detailed formatting and anonymization rules will not be considered for review. 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. This post is for recording some notes from a few OSDI'21 papers that I got fun. 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. Submissions may include as many additional pages as needed for references but not for appendices. Second, Fluffy uses multiple existing Ethereum clients that independently implement the specification as cross-referencing oracles. One classical approach is to increase the efficiency of an allocator to minimize the cycles spent in the allocator code. These are hard deadlines, and no extensions will be given. JEL codes: Q18, Q28, Q57 . 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%). The 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI '21) will take place as a virtual event on July 14-16, 2021. blk-switch evaluation over a variety of scenarios shows that it consistently achieves s-scale average and tail latency (at both 99th and 99.9th percentiles), while allowing applications to near-perfectly utilize the hardware capacity. In some cases, the quality of these artifacts is as important as that of the document itself. Graph Neural Networks (GNNs) have gained significant attention in the recent past, and become one of the fastest growing subareas in deep learning. For general conference information, see https://www.usenix.org/conference/osdi22. Four months after we reported the bugs to Geth developers, one of the bugs was triggered on the mainnet, and caused nodes using a stale version of Geth to hard fork the Ethereum blockchain. Unfortunately, because devices lack the semantic information about which I/O requests are latency-sensitive, these heuristics can sometimes lead to disastrous results. Writing a correct operating system kernel is notoriously hard. Authors are also encouraged to contact the program co-chairs, [email protected], if needed to relate their OSDI submissions to relevant submissions of their own that are simultaneously under review or awaiting publication at other venues. To help more profitably utilize sanitizers, we introduce SanRazor, a practical tool aiming to effectively detect and remove redundant sanitizer checks. Kernel code requires manual memory management and type-unsafe code and must efficiently handle complex, asynchronous events. 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. Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, and Yufei Ding, University of California, Santa Barbara. 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. 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. Sam Kumar, David E. Culler, and Raluca Ada Popa, University of California, Berkeley. However, a plethora of recent data breaches show that even widely trusted service providers can be compromised. Concurrency control algorithms are key determinants of the performance of in-memory databases. 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. 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. Most existing schedulers expect users to specify the number of resources for each job, often leading to inefficient resource use. Grand Rapids, Michigan, United States . Starting with small invariant formulas and strongest possible invariants avoids large SMT queries, improving SMT solver performance. 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. This paper addresses a key missing piece in the current ecosystem of decentralized services and blockchain apps: the lack of decentralized, verifiable, and private search. As increasingly more sensitive data is being collected to gain valuable insights, the need to natively integrate privacy controls in data analytics frameworks is growing in importance. This approach misses possible optimization opportunities as transformations that only preserve equivalence on subsets of the output tensors are excluded. Pollux is implemented and publicly available as part of an open-source project at https://github.com/petuum/adaptdl. OSDI '21 Technical Sessions | USENIX 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. 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. The key insight guiding our design is computation separation. NrOS is primarily constructed as a simple, sequential kernel with no concurrency, making it easier to develop and reason about its correctness. High-performance tensor programs are critical for efficiently deploying deep neural network (DNN) models in real-world tasks. The NAL maintains 1) per-node partial views in PM for serving insert/update/delete operations with failure atomicity and 2) a global view in DRAM for serving lookup operations. 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. Despite having the same end goals as traditional ML, FL executions differ significantly in scale, spanning thousands to millions of participating devices. Poor data locality hurts an application's performance. Precision Conservation: Linking Set-aside and Working Lands Policy 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. Simultaneous submission of the same work to multiple venues, submission of previously published work, or plagiarism constitutes dishonesty or fraud. In this paper, we present P3, a system that focuses on scaling GNN model training to large real-world graphs in a distributed setting. She developed the technology for making network routing self-stabilizing, largely self-managing, and scalable. 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. Author Response Period 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. 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. To evaluate the security guarantees of Storm, we build a formally verified reference implementation using the Labeled IO (LIO) IFC framework. Authors must limit their responses to (a) correcting factual errors in the reviews or (b) directly addressing questions posed by reviewers. Main conference program: 5-8 April 2022. Commonly used log archival and compression tools like Gzip provide high compression ratio, yet searching archived logs is a slow and painful process as it first requires decompressing the logs. 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. Sep 2021 - Present 1 year 7 months. 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. 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. When registering your abstract, you must provide information about conflicts with PC members. 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. 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. 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. This change is receiving considerable attention in the architecture and security communities, for example, but in contrast, so-called OS researchers are mostly in denial. The conference papers and full proceedings are available to registered attendees now and will be available to everyone beginning Wednesday, July 14, 2021. The chairs may reject abstracts or papers on the basis of egregious missing or extraneous conflicts. 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. VLDB 2021 - 47th International Conference on Very Large Data Bases PET discovers and applies program transformations that improve computation efficiency but only maintain partial functional equivalence. Important Dates Abstract registrations due: Thursday, December 3, 2020, 3:00 pm PST Complete paper submissions due: Thursday, December 10, 2020, 3:00pm PST Author Response Period News Baris Kasikci's Home Page - Electrical Engineering and Computer We will look at various problems and approaches, and for each, see if blockchain would help. . The device then "calibrates" its interrupts to completions of latency-sensitive requests. Oort Accepted to Appear at OSDI'2021 | Mosharaf Chowdhury 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. If your accepted paper should not be published prior to the event, please notify [email protected]. Software Systems Laboratory Wins Best Paper Awards at the OSDI and We observe that, due to their intended security guarantees, SC schemes are inherently oblivioustheir memory access patterns are independent of the input data. 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. 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. 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. 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. 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. A graph neural network (GNN) enables deep learning on structured graph data. Shaghayegh Mardani, UCLA; Ayush Goel, University of Michigan; Ronny Ko, Harvard University; Harsha V. Madhyastha, University of Michigan; Ravi Netravali, Princeton University. 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. The conference papers and full proceedings are available to registered attendees now and will be available to everyone beginning Wednesday, July 14, 2021. In this paper, we show how to address this inefficiency without requiring pages to be rewritten or browsers to be modified. 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. We demonstrate the above using design, implementation and evaluation of blk-switch, a new Linux kernel storage stack architecture.