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检索条件"任意字段=Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming"

共 364 条记录，以下是31-40 订阅

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proceedings of the ACM SIGPLAN symposium on principles and practice of parallel programming, PPOPP: Chairs' Welcome

Proceedings of the ACM SIGPLAN Symposium on Principles and P...

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proceedings of the ACM SIGPLAN symposium on principles and practice of parallel programming, PPOPP 2011年 iii页

作者： Cascaval, Calin Yew, Pen-Chung Qualcomm Research United States Academia Sinica Taiwan University of Minnesota United States

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proceedings of the 24th International symposium on principles and practice of Declarative programming, PPDP 2022 - Co-located with CLAS 2022 (including LOPSTR 2022)

Proceedings of the 24th International Symposium on Principle...

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24th International symposium on principles and practice of Declarative programming, PPDP 2022

ISBN: (纸本)9781450397032

the proceedings contain 12 papers. the topics discussed include: a monadic implementation of functional logic programs;ASP-based multi-shot reasoning via DLV2 with incremental grounding;contextual equivalence in a probabilistic call-by-need lambda-calculus;nominal matching logic;a logic for formalizing properties of LF specifications;a predicate construct for declarative programming in imperative languages;a typed lambda calculus with gradual intersection types;user guided abductive proof generation for answer set programming queries;certified derivation of small-step from big-step skeletal semantics;and session types revisited: a decade later.

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Minimizing speculation overhead in a parallel recognizer for regular texts 25

Minimizing speculation overhead in a parallel recognizer for...

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proceedings of the 30th ACM SIGPLAN Annual symposium on principles and practice of parallel programming

作者： Angelo Borsotti Luca Breveglieri Angelo Morzenti Stefano Crespi Reghizzi Politecnico di Milano Milano Italy Politecnico di Milano and CNR-IEIIT Milano Italy

ISBN: (纸本)9798400714436

Speculative data-parallel algorithms for language recognition have been widely experimented for various types of finitestate automata (FA), deterministic (DFA) and nondeterministic (NFA), often derived fromregular expressions (RE). Such an algorithm cuts the input string into chunks, independently recognizes each chunk in parallel by means of identical FAs, and at last joins the chunk results and checks the overall consistency. In chunk recognition, it is necessary to speculatively start the FAs in any state, thus causing an overhead that reduces the speedup over a serial algorithm. the existing data-parallel DFA-based recognizers suffer from an excessive number of starting states, and the NFA-based ones suffer from the number of nondeterministic transitions.

关键词： data-parallel recognition algorithm

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Magneto: Accelerating parallel Structures in DNNs via Co-Optimization of Operators 25

Magneto: Accelerating Parallel Structures in DNNs via Co-Opt...

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proceedings of the 30th ACM SIGPLAN Annual symposium on principles and practice of parallel programming

作者： Zhanyuan Di Leping Wang Ziyi Ren En Shao Jie Zhao Siyuan Feng Dingwen Tao Guangming Tan Ninghui Sun SKLP Institute of Computing Technology CAS University of Chinese Academy of Sciences SKLP Institute of Computing Technology CAS Hunan University Shanghai Jiao Tong University

ISBN: (纸本)9798400714436

Deep neural networks (DNNs) increasingly rely on parallel structures to enhance performance and efficiency. However, existing machine learning compilers (MLCs) face challenges in optimizing these structures due to limited parallel fusion scopes and insufficient consideration of intra-operator information. this paper introduces Magneto, a novel framework designed to accelerate parallel structures in DNNs through the co-optimization of parallel operators. By expanding the scope of parallel operator fusion and introducing a dedicated co-tuning algorithm, Magneto unlocks new opportunities for co-optimization. Experimental results demonstrate that Magneto outperforms NVIDIA TensorRT and AMD MIGraphX, achieving speedups of 3.02× and 4.19×, respectively.

关键词： DNN

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TensorMD: Molecular Dynamics Simulation with Ab Initio Accuracy of 50 Billion Atoms 25

TensorMD: Molecular Dynamics Simulation with Ab Initio Accur...

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proceedings of the 30th ACM SIGPLAN Annual symposium on principles and practice of parallel programming

作者： Yucheng Ouyang Ying Liu Honghui Shang Zhenchuan Chen Jiahao Shan Huimin Cui Xiaobing Feng Xin Chen Xingyu Gao Lifang Wang Haifeng Song Rongfen Lin Fang Li Institute of Computing Technology Chinese Academy of Sciences Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China Institute of Applied Physics and Computational Mathematics Beijing China

ISBN: (纸本)9798400714436

Molecular dynamics simulation emerges as an important area that HPC+AI helps to investigate the physical properties, with machine-learning interatomic potentials (MLIPs) being used. General-purpose machine-learning (ML) tools have been leveraged in MLIPs, but they are not perfectly matched with each other, since many optimization opportunities in MLIPs have been missed by ML tools. this inefficiency arises from the fact that HPC+AI applications work with far more computational complexity compared with pure AI scenarios. this paper has developed an MLIP, named TensorMD, independently from any ML tool. TensorMD has been evaluated on two supercomputers and scaled to 51.8 billion atoms, i.e., ~ 3× compared with state-of-the-art.

关键词： GPU

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Building parallel programming language constructs in the AbleC extensible C compiler framework A PPoPP Tutorial 19

Building parallel programming language constructs in the Abl...

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24th ACM SIGPLAN symposium on principles and practice of parallel programming (PPoPP)

作者： Carlson, Travis Van Wyk, Eric Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781450362252

In this tutorial participants learn how to build their own parallel programming language features by developing them as language extensions in the ableC [4] extensible C compiler framework. By implementing new parallel programming abstractions as language extensions one can build on an existing host language and thus avoid re-implementing common language features such as the type checking and code generation of arithmetic expressions and control flow statements. Using ableC, one can build expressive language features that fit seamlessly into the C11 host language.

关键词： parallel programming

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Provably and Practically Efficient Granularity Control 19

Provably and Practically Efficient Granularity Control

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24th ACM SIGPLAN symposium on principles and practice of parallel programming (PPoPP)

作者： Acar, Umut A. Aksenov, Vitaly Chargueraud, Arthur Rainey, Mike Carnegie Mellon Univ Pittsburgh PA 15213 USA INRIA Paris France ITMO Univ St Petersburg Russia Univ Strasbourg ICube CNRS Strasbourg France Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9781450362252

Over the past decade, many programming languages and systems for parallel-computing have been developed, e.g., Fork/Join and Habanero Java, parallel Haskell, parallel ML, and X10. Although these systems raise the level of abstraction for writing parallel codes, performance continues to require labor-intensive optimizations for coarsening the granularity of parallel executions. In this paper, we present provably and practically efficient techniques for controlling granularity within the run-time system of the language. Our starting point is "oracle-guided scheduling", a result from the functional-programming community that shows that granularity can be controlled by an "oracle" that can predict the execution time of parallel codes. We give an algorithm for implementing such an oracle and prove that it has the desired theoretical properties under the nested-parallel programming model. We implement the oracle in C++ by extending Cilk and evaluate its practical performance. the results show that our techniques can essentially eliminate hand tuning while closely matching the performance of hand tuned codes.

关键词： parallel programming languages granularity control

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Implementing parallel and Concurrent Tree Structures 19

Implementing Parallel and Concurrent Tree Structures

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24th ACM SIGPLAN symposium on principles and practice of parallel programming (PPoPP)

作者： Sun, Yihan Blelloch, Guy Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450362252

As one of the most important data structures used in algorithm design and programming, balanced search trees are widely used in real-world applications for organizing data. Answering the challenges thrown up by modern largevolume and ever-changing data, it is important to consider parallelism, concurrency, and persistence. this tutorial will introduce techniques for supporting functionalities on trees, including various parallel algorithms, concurrency, multiversioning, etc. In particular, this tutorial will focus on an algorithmic framework for parallel balanced binary trees, which works for multiple balancing schemes, including AVL trees, red-black trees, weight-based trees, and treaps. this framework allows for theoretically-efficient algorithms. the corresponding implementation is available as a library, which demonstrates good performance both sequentially and in parallel in various use scenarios. this tutorial will focus on the following topics: 1) the algorithms and techniques used in the PAM library;2) the interface of the library and a hands-on introduction to the download/installation of the library;3) examples of applying the library to various applications and 4) introduction about other useful techniques for parallel tree structures and performance comparisons with PAM.

关键词： balanced tree augmented map parallel concurrent library PAM ordered set ordered map

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Incremental Flattening for Nested Data parallelism 19

Incremental Flattening for Nested Data Parallelism

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24th ACM SIGPLAN symposium on principles and practice of parallel programming (PPoPP)

作者： Henriksen, Troels thoroe, Frederik Elsman, Martin Oancea, Cosmin Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9781450362252

Compilation techniques for nested-parallel applications that can adapt to hardware and dataset characteristics are vital for unlocking the power of modern hardware. this paper proposes such a technique, which builds on flattening and is applied in the context of a functional data-parallel language. Our solution uses the degree of utilized parallelism as the driver for generating a multitude of code versions, which together cover all possible mappings of the application's regular nested parallelism to the levels of parallelism supported by the hardware. these code versions are then combined into one program by guarding them with predicates, whose threshold values are automatically tuned to hardware and dataset characteristics. Our unsupervised method-of statically clustering datasets to code versions-is different from autotuning work that typically searches for the combination of code transformations producing a single version, best suited for a specific dataset or on average for all datasets. We demonstrate-by fully integrating our technique in the repertoire of a compiler for the Futhark programming language-significant performance gains on two GPUs for three real-world applications, from the financial domain, and for six Rodinia benchmarks.

关键词： functional language parallel compilers GPGPU

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throughput-Oriented GPU Memory Allocation 19

Throughput-Oriented GPU Memory Allocation

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24th ACM SIGPLAN symposium on principles and practice of parallel programming (PPoPP)

作者： Gelado, Isaac Garland, Michael NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9781450362252

throughput-oriented architectures, such as GPUs, can sustain three orders of magnitude more concurrent threads than multicore architectures. this level of concurrency pushes typical synchronization primitives (e.g., mutexes) over their scalability limits, creating significant performance bottlenecks in modules, such as memory allocators, that use them. In this paper, we develop concurrent programming techniques and synchronization primitives, in support of a dynamic memory allocator, that are efficient for use with very high levels of concurrency. We formulate resource allocation as a two-stage process, that decouples accounting for the number of available resources from the tracking of the available resources themselves. To facilitate the accounting stage, we introduce a novel bulk semaphore abstraction that extends traditional semaphore semantics by optimizing for the case where threads operate on the semaphore simultaneously. We also similarly design new collective synchronization primitives that enable groups of cooperating threads to enter critical sections together. Finally, we show that delegation of deferred reclamation to threads already blocked greatly improves efficiency. Using all these techniques, our throughput-oriented memory allocator delivers both high allocation rates and low memory fragmentation on modern GPUs. Our experiments demonstrate that it achieves allocation rates that are on average 16.56 times higher than the counterpart implementation in the CUDA 9 toolkit.

关键词： Concurrency Memory Allocation GPU programming

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