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. Paper Submission Information All submissions must be received by 11:59 PM AoE (UTC-12) on the day of the corresponding deadline. We propose PET, the first DNN framework that optimizes tensor programs with partially equivalent transformations and automated corrections. 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 . NrOS replicates kernel state on each NUMA node and uses operation logs to maintain strong consistency between replicas. OSDI takes a broad view of the systems area and solicits contributions from many fields of systems practice, including, but not limited to, operating systems, file and storage systems, distributed systems, cloud computing, mobile systems, secure and reliable systems, systems aspects of big data, embedded systems, virtualization, networking as it relates to operating systems, and management and troubleshooting of complex systems. 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. 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. OSDI'20: 14th USENIX Conference on Operating Systems Design and ImplementationNovember 4 - 6, 2020 ISBN: 978-1-939133-19-9 Published: 04 November 2020 Sponsors: ORACLE, VMware, Google Inc., Amazon, Microsoft Get Alerts for this Conference Save to Binder Export Citation Bibliometrics Citation count 96 Downloads (6 weeks) 317 Downloads (12 months) For example, talks may be shorter than in prior years, or some parts of the conference may be multi-tracked. A hardware-accelerated thread scheduler makes sub-nanosecond decisions, leading to high CPU utilization and low tail response time for RPCs. This yielded 6% fewer TLB miss stalls, and 26% reduction in memory wasted due to fragmentation. Pages should be numbered, and figures and tables should be legible in black and white, without requiring magnification. 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. Proceedings Cover | 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 present application studies for 8 applications, improving requests-per-second (RPS) by 7.7% and reducing RAM usage 2.4%. 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. Swapnil Gandhi and Anand Padmanabha Iyer, Microsoft Research. Camera-ready submission (all accepted papers): 15 Mars 2022. 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. Shaghayegh Mardani, UCLA; Ayush Goel, University of Michigan; Ronny Ko, Harvard University; Harsha V. Madhyastha, University of Michigan; Ravi Netravali, Princeton University. 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. Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury, University of Michigan. 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. We demonstrate the above using design, implementation and evaluation of blk-switch, a new Linux kernel storage stack architecture. Differential privacy (DP) enables model training with a guaranteed bound on this leakage. 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. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. Further, Vegito can recover from cascading machine failures by using the columnar backup in less than 60 ms. OSDI '21 Technical Sessions All the times listed below are in Pacific Daylight Time (PDT). USENIX discourages program co-chairs from submitting papers to the conferences they organize, although they are allowed to do so. To adapt to different workloads, prior works mix or switch between a few known algorithms using manual insights or simple heuristics. Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. 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. The copyback-aware block allocation considers different copy costs at different copy paths within the SSD. Consensus bugs are bugs that make Ethereum clients transition to incorrect blockchain states and fail to reach consensus with other clients. Machine learning (ML) models trained on personal data have been shown to leak information about users. These limitations require state-of-the-art systems to distribute training across multiple machines. The conference papers and full proceedings are available to registered attendees now and will be available to everyone beginning Wednesday, July 14, 2021. Message from the Program Co-Chairs. OSDI will provide an opportunity for authors to respond to reviews prior to final consideration of the papers at the program committee meeting. 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. Sanitizers detect unsafe actions such as invalid memory accesses by inserting checks that are validated during a programs execution. 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. Papers not meeting these criteria will be rejected without review, and no deadline extensions will be granted for reformatting. All deadline times are 23:59 hrs UTC. We also welcome work that explores the interface to related areas such as computer architecture, networking, programming languages, analytics, and databases. The conference papers and full proceedings are available to registered attendees now and will be available to everyone beginning Wednesday, July 14, 2021. 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. 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. We develop MAGE, an execution engine for SC that efficiently runs SC computations that do not fit in memory. 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. For realistic workloads, KEVIN improves throughput by 68% on average. 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. One classical approach is to increase the efficiency of an allocator to minimize the cycles spent in the allocator code. HotNets provides a venue for discussing innovative ideas and for debating future research agendas in networking. Papers so short as to be considered extended abstracts will not receive full consideration. 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. The experimental results show that Penglai can support 1,000s enclave instances running concurrently and scale up to 512GB secure memory with both encryption and integrity protection. 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. 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. 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. Cores can safely and concurrently read from their local kernel replica, eliminating remote NUMA accesses. She also has made contributions in network security, including scalable data expiration, distributed algorithms despite malicious participants, and DDOS prevention techniques. Password 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. Taking place in Carlsbad, CA from 11-13 July, OSDI is a highly selective flagship conference in computer science, especially on the topic of computer systems. Author Response Period PLDI is a premier forum for programming language research, broadly construed, including design, implementation, theory, applications, and performance. The abstractions we design for the privacy resource mirror those defined by Kubernetes for traditional resources, but there are also major differences. We argue that a key-value interface between a file system and an SSD is superior to the legacy block interface by presenting KEVIN. 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). OSDI brings together professionals from academic and industrial backgrounds in a premier forum for discussing the design, implementation, and implications of systems software. This is the first OSDI in an odd year as OSDI moves to a yearly cadence. 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. 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. PLDI seeks outstanding research that extends and/or applies programming-language concepts to advance the field of computing. 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. We propose a new framework for computing the embeddings of large-scale graphs on a single machine. 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. We also propose two file system techniques for ZNS+-aware LFS. Radia Perlman is a Fellow at Dell Technologies. By submitting a paper, you agree that at least one of the authors will attend the conference to present it. Typically, monolithic kernels share state across cores and rely on one-off synchronization patterns that are specialized for each kernel structure or subsystem. We present selective profiling, a technique that locates data locality problems with low-enough overhead that is suitable for production use. All the times listed below are in Pacific Daylight Time (PDT). Table of Contents | Erhu Feng, Xu Lu, Dong Du, Bicheng Yang, and Xueqiang Jiang, Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University; Engineering Research Center for Domain-specific Operating Systems, Ministry of Education, China; Yubin Xia, Binyu Zang, 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. We present Nap, a black-box approach that converts concurrent persistent memory (PM) indexes into NUMA-aware counterparts. For conference information, . We prove that DistAI is guaranteed to find the -free inductive invariant that proves the desired safety properties in finite time, if one exists. Concurrency control algorithms are key determinants of the performance of in-memory databases. Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, and Yufei Ding, University of California, Santa Barbara. 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. Authors may upload supplementary material in files separate from their submissions. We develop a prototype of Zeph on Apache Kafka to demonstrate that Zeph can perform large-scale privacy transformations with low overhead. We implemented the ZNS+ SSD at an SSD emulator and a real SSD. ), Program Co-Chairs: Angela Demke Brown, University of Toronto, and Jay Lorch, Microsoft Research.
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