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检索条件"任意字段=ACM SIGPLAN Conference on Object-oriented Programming, Systems, Languages, and Applications"

共 2225 条记录，以下是11-20 订阅

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OOPSLA '08: Companion to the 23rd acm sigplan conference on object oriented programming systems languages and applications

OOPSLA '08: Companion to the 23rd ACM SIGPLAN conference on ...

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23rd acm sigplan conference on object oriented programming systems languages and applications,OOPSLA 2008

The proceedings contain 79 papers. The topics discussed include: agile architecture methodology: long term strategy interleaved with short term tactics;retaining comments when refactoring code;decentralized control of automatic guided vehicles: applying multi-agent systems in practice;NOAO imaging meta data quality improvement: a case study of the evolution of a service oriented system;performance pitfalls in large-scale Java applications translated from COBOL;processing heterogeneous abstract syntax trees with the mutable class pattern;a workload model of topic-based publish/subscribe systems;SyQL: an object oriented, fuzzy, temporal query language for repositories of software artifacts;aspect weaving for OSGi;automated testing of non-functional requirements;compile-time type-checking for custom type qualifiers in Java;enforcing reference and object immutability in Java;and refactoring support for the groovy-eclipse plug-in.

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OOPSLA 2002 - acm sigplan object oriented programming systems languages and applications conference - Practitioners Reports

OOPSLA 2002 - ACM SIGPLAN Object Oriented Programming System...

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17th Annual acm conference on object-oriented programming, systems, languages and applications, OOPSLA 2002

ISBN: (纸本)1581134711

The proceedings contain 9 papers. The topics discussed include: migrating legacy engineering applications to Java;hitting the target: adding interaction design to agile software development;a common multi-platform hardware object model;extreme embedded a report from the front line;designing a web services project for maximum value: the 90 day challenge;transformation of an application data layer;agile techniques to avoid firefighting at a start-up;making RUP agile;and defining and implementing a scientific analysis software architecture.

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Proceedings and Companion to the WikiSym'07 ISMM'07, HPC-GECO/CompFrame'07 - 22nd acm sigplan conference on object-oriented programming systems and applications Companion , OOPSLA'07

Proceedings and Companion to the WikiSym'07 ISMM'07, HPC-GEC...

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WikiSym'07, ISMM'07, HPC-GECO/CompFrame'07, DLS'07 - 22nd acm sigplan conference on object-oriented programming systems and applications Companion, OOPSLA'07

ISBN: (纸本)9781595938657

The proceedings contain 100 papers. The topics discussed include: the popularity cycle of graphical tools, UML, and libraries of associations;the first workshop on programming languages and integrated development environments (PLIDE);No silver bullet: a retrospective on the essence and accidents of software engineering;the first international workshop on unified data mining engine: addressing challenges;selecting object-oriented source code metrics to improve predictive models using a parallel genetic Algorithm;open framework for conformance testing via scenarios;a framework for distributed collaborative conceptual model development;adding rigorous statistics to the Java benchmarker's toolbox;status report on JSR-305: annotations for software defect detection;and synthesizing reactive systems from LSC requirements using the play-engine.

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OS2G: A High-Performance DPU Offloading Architecture for GPU-based Deep Learning with object Storage 25

OS2G: A High-Performance DPU Offloading Architecture for GPU...

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30th acm International conference on Architectural Support for programming languages and Operating systems, ASPLOS 2025

作者： Jin, Zhen Chen, Yiquan Liang, Mingxu Wang, Yijing Fang, Guoju Zhou, Ao Zhang, Keyao Xu, Jiexiong Lin, Wenhai Lin, Yiquan Zhao, Shushu Shi, Wenkai He, Zhenhua Cai, Shishun Chen, Wenzhi Zhejiang University Hangzhou China Alibaba Group Hangzhou China

ISBN: (纸本)9798400710797

object storage is increasingly attractive for deep learning (DL) applications due to its cost-effectiveness and high scalability. However, it exacerbates CPU burdens in DL clusters due to intensive object storage processing and multiple data movements. Data processing unit (DPU) offloading is a promising solution, but naively offloading the existing object storage client leads to severe performance degradation. Besides, only offloading the object storage client still involves redundant data movements, as data must first transfer from the DPU to the host and then from the host to the GPU, which continues to consume valuable host resources. In this paper, we propose OS2G, a high-performance offloading architecture designed to free up valuable CPU resources while providing high-performance storage services for DL applications. The key idea of OS2G is to offload the object storage client to a DPU and enable direct data transfer between the DPU and GPU. Specifically, we design a high-performance OS2G Client running on the DPU, utilizing asynchrony, pre-reading, and concurrency strategies to provide high-performance object storage services. Additionally, we propose the GPUDirect DPU (GDD) technique for OS2G to optimize the data path, allowing direct data transfer between the DPU-accelerated storage system and the GPU computing system, fully bypassing the host. Results demonstrate that compared to S3FS and S3Connector, OS2G reduces the execution time of the ResNet18 model by 34.3% and 50.4%, and also decreases CPU consumption by 61.9% and 57.7%, respectively. © 2025 acm.

关键词： Graphics processing unit

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Towards End-to-End Optimization of LLM-based applications with Ayo 25

Towards End-to-End Optimization of LLM-based Applications wi...

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30th acm International conference on Architectural Support for programming languages and Operating systems, ASPLOS 2025

作者： Tan, Xin Jiang, Yimin Yang, Yitao Xu, Hong The Chinese University of Hong Kong Hong Kong Beijing China

ISBN: (纸本)9798400710797

Large language model (LLM)-based applications consist of both LLM and non-LLM components, each contributing to the end-to-end latency. Despite great efforts to optimize LLM inference, end-to-end workflow optimization has been overlooked. Existing frameworks employ coarse-grained orchestration with task modules, which confines optimizations to within each module and yields suboptimal scheduling decisions. We propose fine-grained end-to-end orchestration, which utilizes task primitives as the basic units and represents each query's workflow as a primitive-level dataflow graph. This explicitly exposes a much larger design space, enables optimizations in parallelization and pipelining across primitives of different modules, and enhances scheduling to improve application-level performance. We build Ayo, a novel orchestration framework for LLM-based applications that implements this scheme. Comprehensive experiments show that Ayo can achieve up to 2.09x speedup over existing systems across various popular LLM applications. © 2025 acm.

关键词： Data flow graphs

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Fusion: An Analytics object Store Optimized for Query Pushdown 25

Fusion: An Analytics Object Store Optimized for Query Pushdo...

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30th acm International conference on Architectural Support for programming languages and Operating systems, ASPLOS 2025

作者： Lu, Jianan Raina, Ashwini Cidon, Asaf Freedman, Michael J. Princeton University Princeton United States Columbia University New York United States

ISBN: (纸本)9798400706981

The prevalence of disaggregated storage in public clouds has led to increased latency in modern OLAP cloud databases, particularly when handling ad-hoc and highly-selective queries on large objects. To address this, cloud databases have adopted computation pushdown, executing query predicates closer to the storage layer. However, existing pushdown solutions are inefficient in erasure-coded storage. Cloud storage employs erasure coding that partitions analytics file objects into fixed-sized blocks and distributes them across storage nodes. Consequently, when a specific part of the object is queried, the storage system must reassemble the object across nodes, incurring significant network latency. In this work, we present Fusion, an object store for analytics that is optimized for query pushdown on erasure-coded data. It co-designs its erasure coding and file placement topologies, taking into account popular analytics file formats (e.g., Parquet). Fusion employs a novel stripe construction algorithm that prevents fragmentation of computable units within an object, and minimizes storage overhead during erasure coding. Compared to existing erasure-coded stores, Fusion improves median and tail latency by 64% and 81%, respectively, on TPC-H, and up to 40% and 48% respectively, on real-world SQL queries. Fusion achieves this while incurring a modest 1.2% storage overhead compared to the optimal. © 2025 acm.

关键词： Cloud storage

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DarwinGame: Playing Tournaments for Tuning applications in Noisy Cloud Environments 25

DarwinGame: Playing Tournaments for Tuning Applications in N...

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30th acm International conference on Architectural Support for programming languages and Operating systems, ASPLOS 2025

作者： Basu Roy, Rohan Gadepally, Vijay Tiwari, Devesh University of Utah Salt Lake CityUT United States Massachusetts Institute of Technology LexingtonMA United States Northeastern University BostonMA United States

ISBN: (纸本)9798400706981

This work introduces a new subarea of performance tuning - performance tuning in a shared interference-prone computing environment. We demonstrate that existing tuners are significantly suboptimal by design because of their inability to account for interference during tuning. Our solution, DarwinGame, employs a tournament-based design to systematically compare application executions with different tunable parameter configurations, enabling it to identify the relative performance of different tunable parameter configurations in a noisy environment. Compared to existing solutions, DarwinGame achieves more than 27% reduction in execution time, with less than 0.5% performance variability. DarwinGame is the first performance tuner that will help developers tune their applications in shared, interference-prone, cloud environments. © 2025 acm.

关键词： performance interference

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Hierarchical Prefetching: A Software-Hardware Instruction Prefetcher for Server applications 25

Hierarchical Prefetching: A Software-Hardware Instruction Pr...

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30th acm International conference on Architectural Support for programming languages and Operating systems, ASPLOS 2025

作者： Zhang, Tingji Grot, Boris He, Wenjian Lv, Yashuai Qu, Peng Su, Fang Wang, Wenxin Zhang, Guowei Zhang, Xuefeng Zhang, Youhui Tsinghua University Beijing China University of Edinburgh Edinburgh United Kingdom Huawei Research Zurich Switzerland Huawei Technologies Co. Ltd. Shanghai China Zhongguancun National Laboratory Beijing China

ISBN: (纸本)9798400710797

The large working set of instructions in server-side applications causes a significant bottleneck in the front-end, even for high-performance processors equipped with fetch-directed instruction prefetching (FDIP). Prefetchers specifically designed for server scenarios typically rely on a record-and-replay mechanism that exploits the repetitiveness of instruction sequences. However, the efficacy of these techniques is compromised by discrepancies between actual and predicted control flows, resulting in loss of coverage and timeliness. This paper proposes Hierarchical Prefetching, a novel approach that tackles the limitations of existing prefetchers. It identifies common coarse-grained functionality blocks (called Bundles) within the server code and prefetches them as a whole. Bundles are significantly larger than typical prefetch targets, encompassing tens to hundreds of kilobytes of code. The approach combines simple software analysis of code for bundle formation and light-weight hardware for record-and-replay prefetching. The prefetcher requires under 2KB of on-chip storage by keeping most of the metadata in main memory. Experiments with 11 popular server workloads reveal that Hierarchical Prefetching significantly improves miss coverage and timeliness over prior techniques, achieving a 6.6% average performance gain over FDIP. © 2025 acm.

关键词： Application programs

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Hardware Sentinel: Protecting Software applications from Hardware Silent Data Corruptions 25

Hardware Sentinel: Protecting Software Applications from Har...

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30th acm International conference on Architectural Support for programming languages and Operating systems, ASPLOS 2025

作者： Dutta, Rhea Dixit, Harish Dattatraya Van Riel, Rik Vunnam, Gautham Sankar, Sriram Meta Platforms Inc. Menlo ParkCA United States

ISBN: (纸本)9798400710797

Silent Data Corruptions (SDCs) pose a significant challenge in large-scale infrastructures, affecting data center applications unpredictably and reducing service reliability. Primarily caused by silicon defects, traditional hardware testing methods are insufficient to prevent SDC propagation. SDCs are influenced by various factors, including data randomization, workload characteristics, environmental conditions, and aging, necessitating top-down approaches from the application layer. In this paper, we introduce Hardware Sentinel, a novel framework that detects SDCs through typical software failure indicators such as segmentation faults, core dumps, application crashes, and logs. We have validated our framework in a large-scale data center fleet, across diverse application, kernel, and hardware configurations, achieving a high success rate of SDC detection. Hardware Sentinel has uncovered novel instances of SDCs, surpassing the detection capabilities of published testing techniques. Our analysis of over 6 years' worth of application and system failure data within a large-scale infrastructure has successfully identified hundreds of defective CPUs that triggered SDCs. Notably, the Hardware Sentinel flow increases effective coverage over existing hardware-testing methods like Fleetscanner (out-of-production testing) by 1.74x and Ripple (in-production testing) by 1.92x. We share the top kernel exceptions with the highest correlation to silent data corruption failures. We present results spanning 7 CPU generations from multiple semiconductor manufacturers, 13 large-scale workloads, and 27 data center regions, providing insights into the trade-offs involved in detection and fleet deployment. © 2025 acm.

关键词： Digital storage

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Proceedings of the 2003 acm conference on object-oriented programming, systems, languages, and applications (OOPSLA'03)

Proceedings of the 2003 ACM Conference on Object-Oriented Pr...

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Proceedings of the 2003 acm conference on object-oriented programming, systems, languages, and applications

This Volume 38 Number 11 of the conference proceedings contains 26 papers. Topics discussed include refactoring and reflection, smalltalkiana, error repair, generics, Java performance, language design, object-oriented... 详细信息

关键词： Computer programming languages

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