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. We develop MAGE, an execution engine for SC that efficiently runs SC computations that do not fit in memory. The biennial ACM Symposium on Operating Systems Principles is the world's premier forum for researchers, developers, programmers, and teachers of computer systems technology. USENIX discourages program co-chairs from submitting papers to the conferences they organize, although they are allowed to do so. (Registered attendees: Sign in to your USENIX account to download these files. Papers accompanied by nondisclosure agreement forms will not be considered. 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; 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. Welcome to the 2021 USENIX Annual Technical Conference (ATC '21) submissions site! We present Storm, a web framework that allows developers to build MVC applications with compile-time enforcement of centrally specified data-dependent security policies. The chairs may reject abstracts or papers on the basis of egregious missing or extraneous conflicts. In the Ethereum network, decentralized Ethereum clients reach consensus through transitioning to the same blockchain states according to the Ethereum specification. PET then automatically corrects results to restore full equivalence. The 20th ACM Workshop on Hot Topics in Networks (HotNets 2021) will bring together researchers in computer networks and systems to engage in a lively debate on the theory and practice of computer networking. Memory allocation represents significant compute cost at the warehouse scale and its optimization can yield considerable cost savings. We propose a learning-based framework that instead explicitly optimizes concurrency control via offline training to maximize performance. In this paper, we propose a software-hardware co-design to support dynamic, fine-grained, large-scale secure memory as well as fast-initialization. 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. In this paper, we present Vegito, a distributed in-memory HTAP system that embraces freshness and performance with the following three techniques: (1) a lightweight gossip-style scheme to apply logs on backups consistently; (2) a block-based design for multi-version columnar backups; (3) a two-phase concurrent updating mechanism for the tree-based index of backups. To achieve low overhead, selective profiling gathers runtime execution information selectively and incrementally. 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. This budget is a scarce resource that must be carefully managed to maximize the number of successfully trained models. Welcome to the 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI '22) submissions site. 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. 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. Lukas Burkhalter, Nicolas Kchler, Alexander Viand, Hossein Shafagh, and Anwar Hithnawi, ETH Zrich. 2019 - Present. 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. To remedy this, we introduce DeSearch, the first decentralized search engine that guarantees the integrity and privacy of search results for decentralized services and blockchain apps. Devices employ adaptive interrupt coalescing heuristics that try to balance between these opposing goals. Editor in charge: Daniel Petrolia . As has been standard practice in OSDI and SOSP in recent years, we will allow authors to submit quick responses to PC reviews: they will be made available to the PC before the final online discussion and PC meeting. 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. Sat, Aug 7, 2021 3 min read researches review. 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. This motivates the need for a new approach to data privacy that can provide strong assurance and control to users. In particular, responses must not include new experiments or data, describe additional work completed since submission, or promise additional work to follow. 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. Registering abstracts a week before paper submission is an essential part of the paper-reviewing process, as PC members use this time to identify which papers they are qualified to review. However, a plethora of recent data breaches show that even widely trusted service providers can be compromised. A hardware-accelerated thread scheduler makes sub-nanosecond decisions, leading to high CPU utilization and low tail response time for RPCs. CLP's gains come from using a tuned, domain-specific compression and search algorithm that exploits the significant amount of repetition in text logs. In this paper, we propose Oort to improve the performance of federated training and testing with guided participant selection. HotCRP.com signin Sign in using your HotCRP.com account. For realistic workloads, KEVIN improves throughput by 68% on average. Machine learning (ML) models trained on personal data have been shown to leak information about users. Paper abstracts and proceedings front matter are available to everyone now. Ankit Bhardwaj and Chinmay Kulkarni, University of Utah; Reto Achermann, University of British Columbia; Irina Calciu, VMware Research; Sanidhya Kashyap, EPFL; Ryan Stutsman, University of Utah; Amy Tai and Gerd Zellweger, VMware Research. This is unfortunate because good OS design has always been driven by the underlying hardware, and right now that hardware is almost unrecognizable from ten years ago, let alone from the 1960s when Unix was written. To evaluate the security guarantees of Storm, we build a formally verified reference implementation using the Labeled IO (LIO) IFC framework. 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. 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. 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 Pollux simultaneously considers both aspects. For example, talks may be shorter than in prior years, or some parts of the conference may be multi-tracked. Proceedings Cover | We demonstrate that Marius achieves the same level of accuracy but is up to one order of magnitude faster. The copyback-aware block allocation considers different copy costs at different copy paths within the SSD. This talk will discuss several examples with very different solutions. 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. All the times listed below are in Pacific Daylight Time (PDT). Forgot your password? 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. Paper Submission Information All submissions must be received by 11:59 PM AoE (UTC-12) on the day of the corresponding deadline. The device then "calibrates" its interrupts to completions of latency-sensitive requests. 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. Across a wide range of pages, phones, and mobile networks covering web workloads in both developed and emerging regions, Horcrux reduces median browser computation delays by 31-44% and page load times by 18-37%. KEVIN combines a fast, lightweight, and POSIX compliant file system with a key-value storage device that performs in-storage indexing. We present NrOS, a new OS kernel with a safer approach to synchronization that runs many POSIX programs. PLDI is a premier forum for programming language research, broadly construed, including design, implementation, theory, applications, and performance. Here, we focus on hugepage coverage. Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, and Yufei Ding, University of California, Santa Barbara. PET discovers and applies program transformations that improve computation efficiency but only maintain partial functional equivalence. This is especially true for DPF over Rnyi DP, a highly composable form of DP. 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. 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. We argue that a key-value interface between a file system and an SSD is superior to the legacy block interface by presenting KEVIN. 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 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. This post is for recording some notes from a few OSDI'21 papers that I got fun. Prior or concurrent publication in non-peer-reviewed contexts, like arXiv.org, technical reports, talks, and social media posts, is permitted. Sam Kumar, David E. Culler, and Raluca Ada Popa, University of California, Berkeley. We introduce a hybrid cryptographic protocol for privacy-adhering transformations of encrypted data. Copyright to the individual works is retained by the author[s]. 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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