osdi 2021 accepted papers

In addition, CLP outperforms Elasticsearch and Splunk Enterprise's log ingestion performance by over 13x, and we show CLP scales to petabytes of logs. The 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI '21) will take place as a virtual event on July 14-16, 2021. Kyuhwa Han, Sungkyunkwan University and Samsung Electronics; Hyunho Gwak and Dongkun Shin, Sungkyunkwan University; Jooyoung Hwang, Samsung Electronics. His work has included the Barrelfish multikernel research OS, as well as work on distributed stream processors, and using formal specifications to describe the hardware/software interfaces of modern computer systems. This paper presents Dorylus: a distributed system for training GNNs. 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. (Registered attendees: Sign in to your USENIX account to download these files. Concretely, Dorylus is 1.22 faster and 4.83 cheaper than GPU servers for massive sparse graphs. Prior or concurrent workshop publication does not preclude publishing a related paper in OSDI. Kirk Rodrigues, Yu Luo, and Ding Yuan, University of Toronto and YScope Inc. These scripts often make pages slow to load, partly due to a fundamental inefficiency in how browsers process JavaScript content: browsers make it easy for web developers to reason about page state by serially executing all scripts on any frame in a page, but as a result, fail to leverage the multiple CPU cores that are readily available even on low-end phones. We introduce a hybrid cryptographic protocol for privacy-adhering transformations of encrypted data. 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. 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. Additionally, there is no assurance that data processing and handling comply with the claimed privacy policies. 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. Shaghayegh Mardani, UCLA; Ayush Goel, University of Michigan; Ronny Ko, Harvard University; Harsha V. Madhyastha, University of Michigan; Ravi Netravali, Princeton University. 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. Second, Fluffy uses multiple existing Ethereum clients that independently implement the specification as cross-referencing oracles. As a member of ACCT, I have served two years on the bylaws and governance committee and two years on the finance and audit committee. Qing Wang, Youyou Lu, Junru Li, and Jiwu Shu, Tsinghua University. 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. All the times listed below are in Pacific Daylight Time (PDT). JEL codes: Q18, Q28, Q57 . A graph neural network (GNN) enables deep learning on structured graph data. We observe that, due to their intended security guarantees, SC schemes are inherently oblivioustheir memory access patterns are independent of the input data. Thanks to selective profiling, DMons profiling overhead is 1.36% on average, making it feasible for production use. 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. OSDI '21 Technical Sessions All the times listed below are in Pacific Daylight Time (PDT). 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. The abstractions we design for the privacy resource mirror those defined by Kubernetes for traditional resources, but there are also major differences. 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. As a result, data characteristics and device capabilities vary widely across clients. Academic and industrial participants present research and experience papers that cover the full range of theory and practice of computer . P3 exposes a simple API that captures many different classes of GNN architectures for generality. 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. PET then automatically corrects results to restore full equivalence. Last year, 70% of accepted OSDI papers participated in the . To evaluate the security guarantees of Storm, we build a formally verified reference implementation using the Labeled IO (LIO) IFC framework. Distributed Trust: Is Blockchain the answer? Currently, for large graphs, CPU servers offer the best performance-per-dollar over GPU servers. When registering your abstract, you must provide information about conflicts with PC members. Mingyu Li, Jinhao Zhu, and Tianxu Zhang, 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; Cheng Tan, Northeastern University; Yubin Xia, 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; Sebastian Angel, University of Pennsylvania; 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. These are hard deadlines, and no extensions will be given. GoJournals goal is to bring the advantages of journaling for code to specs and proofs. See www.cs.cmu.edu/~mmv/Veloso.html for her scientific publications. Reviews will be available for response on Wednesday, March 3, 2021. 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. Title Page, Copyright Page, and List of Organizers | We convert five state-of-the-art PM indexes using Nap. To achieve low overhead, selective profiling gathers runtime execution information selectively and incrementally. We develop MAGE, an execution engine for SC that efficiently runs SC computations that do not fit in memory. Mothy's current research centers on Enzian, a powerful hybrid CPU/FPGA machine designed for research into systems software. Researchers from the Software Systems Laboratory bagged Best Paper Awards at the 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2021) and the 2021 USENIX Annual Technical Conference (USENIX ATC 2021).. Jay Lepreau Best Paper Award, OSDI'21. Professor Veloso has been recognized with a multiple honors, including being a Fellow of the ACM, IEEE, AAAS, and AAAI. 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. Sanitizers detect unsafe actions such as invalid memory accesses by inserting checks that are validated during a programs execution. My paper has accepted to appear in the EuroSys2020; I will have a talk at the Hotstorage'19; The Paper about GCMA Accepted to TC; We present application studies for 8 applications, improving requests-per-second (RPS) by 7.7% and reducing RAM usage 2.4%. While compiler-based techniques have been proposed to improve data locality, they depend on heuristics, which can sometimes hurt performance. All submissions will be treated as confidential prior to publication on the USENIX OSDI 21 website; rejected submissions will be permanently treated as confidential. Simultaneous submission of the same work to multiple venues, submission of previously published work, or plagiarism constitutes dishonesty or fraud. 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. A PC member is a conflict if any of the following three circumstances applies: Institution: You are currently employed at the same institution, have been previously employed at the same institution within the past two years (not counting concluded internships), or are going to begin employment at the same institution during the review period. 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. 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. Indeed, it is a prime target for powerful adversaries such as nation states. In 2023 I started another two-year term on the . 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. In particular, responses must not include new experiments or data, describe additional work completed since submission, or promise additional work to follow. Password Just using Lambdas on top of CPU servers offers up to 2.75 more performance-per-dollar than training only with CPU servers. 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. Youngseok Yang, Seoul National University; Taesoo Kim, Georgia Institute of Technology; Byung-Gon Chun, Seoul National University and FriendliAI. 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. We describe Fluffy, a multi-transaction differential fuzzer for finding consensus bugs in Ethereum. Prior or concurrent publication in non-peer-reviewed contexts, like arXiv.org, technical reports, talks, and social media posts, is permitted. Papers accompanied by nondisclosure agreement forms will not be considered. 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. While verifying GoJournal, we found one serious concurrency bug, even though GoJournal has many unit tests. As a result, the design of a file system with respect to space management and crash consistency is simplified, requiring only 10.8K LOC for full functionality. However, a plethora of recent data breaches show that even widely trusted service providers can be compromised. Hence, kernel developers are constantly refining synchronization within OS kernels to improve scalability at the risk of introducing subtle bugs. 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. Table of Contents | GoJournal is implemented in Go, and Perennial is implemented in the Coq proof assistant. 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. Only two types of supplementary material are permitted: source code described in the paper and formal proofs sketched in the paper. Moreover, to handle dynamic workloads, Nap adopts a fast NAL switch mechanism. 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. 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. For any further information, please contact the PC chairs: pc-chairs-2022@eurosys.org. Here, we focus on hugepage coverage. 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. 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. USENIX, like other scientific and technical conferences and journals, prohibits these practices and may, on the recommendation of a program chair, take action against authors who have committed them. 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. One classical approach is to increase the efficiency of an allocator to minimize the cycles spent in the allocator code. We have made Fluffy publicly available at https://github.com/snuspl/fluffy to contribute to the security of Ethereum. Differential privacy (DP) enables model training with a guaranteed bound on this leakage. 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. 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. Existing frameworks optimize tensor programs by applying fully equivalent transformations, which maintain equivalence on every element of output tensors. Sat, Aug 7, 2021 3 min read researches review. Metadata from voice calls, such as the knowledge of who is communicating with whom, contains rich information about peoples lives. Submissions may include as many additional pages as needed for references but not for appendices. Today, privacy controls are enforced by data curators with full access to data in the clear. Lukas Burkhalter, Nicolas Kchler, Alexander Viand, Hossein Shafagh, and Anwar Hithnawi, ETH Zrich. 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. Zeph enforces privacy policies cryptographically and ensures that data available to third-party applications complies with users' privacy policies. 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. Proceedings Front Matter 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. 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. We will look at various problems and approaches, and for each, see if blockchain would help. Authors may upload supplementary material in files separate from their submissions. 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. In this paper, we propose Oort to improve the performance of federated training and testing with guided participant selection. 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. Her robot soccer teams have been RoboCup world champions several times, and the CoBot mobile robots have autonomously navigated for more than 1,000km in university buildings. Proceedings Cover | CLP's gains come from using a tuned, domain-specific compression and search algorithm that exploits the significant amount of repetition in text logs. Further, Vegito can recover from cascading machine failures by using the columnar backup in less than 60 ms.

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