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检索条件"任意字段=ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

共 758 条记录，以下是41-50 订阅

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POSTER: Minimizing speculation overhead in a parallel recognizer for regular texts 30

POSTER: Minimizing speculation overhead in a parallel recogn...

引用

30th symposium on principles and practice of parallel programming

作者： Borsotti, Angelo Breveglieri, Luca Morzenti, Angelo Reghizzi, Stefano Crespi Politecn Milan Milan Italy CNR IEIIT Milan Italy

ISBN: (纸本)9798400714436

Speculative data-parallel algorithms for language recognition have been widely experimented for various types of finite-state automata (FA), deterministic (DFA) and nondeterministic (NFA), often derived from regular 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. Our data-parallel algorithm is based on the new FA type called reduced-interface DFA (RI-DFA), which minimizes the speculation overhead without incurring in the penalty of nondeterministic transitions or of impractically enlarged DFA machines. The algorithm is theoretically efficient, because it combines the state-reduction of an NFA with the speed of deterministic transitions, thus improving on both DFA-based and NFA-based existing implementations. The practical applicability of the RI-DFA approach is confirmed by a quantitative comparison of the number of starting states for a large public benchmark of complex FAs. On multi-core computing architectures, the RI-DFA recognizer is considerably faster than the NFA-based one on all benchmarks, while it matches the DFA-based one on some benchmarks and performs much better on some others. The extra time needed to construct RI-DFA vs DFA is moderate and is compatible with a practical use. The full paper with all details is in [4]. © 2025 Copyright held by the owner/author(s).

关键词： regular language recognition data-parallel recognition algorithm minimal speculation speedup onmulti-core architecture multi-entry DFA reduced-interface DFA

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SBMGT: Scaling Bayesian Multinomial Group Testing 25

SBMGT: Scaling Bayesian Multinomial Group Testing

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30th symposium on principles and practice of parallel programming

作者： Chen, Weicong Qi, Hao Tatsuoka, Curtis Lu, Xiaoyi Univ Calif Merced Merced CA 95343 USA Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (纸本)9798400714436

Group testing is a widely used binary classification method that efficiently distinguishes between samples with and without a binary-classifiable attribute by pooling and testing subsets of a group. Bayesian Group Testing (BGT) is the state-of-the-art approach, which integrates prior risk information into a Bayesian Boolean Lattice framework to minimize test counts and reduce false classifications. However, BGT, like other existing group testing techniques, struggles with multinomial group testing, where samples have multiple binary-classifiable attributes that can be individually distinguished simultaneously. We address this need by proposing Bayesian Multinomial Group Testing (BMGT), which includes a new Bayesian-based model and supporting theorems for an efficient and precise multinomial pooling strategy. We further design and develop SBMGT, a high-performance and scalable framework to tackle BMGT's computational challenges by proposing three key innovations: 1) a parallel binaryencoded product lattice model with up to 99.8% efficiency;2) the Bayesian Balanced Partitioning Algorithm (BBPA), a multinomial pooling strategy optimized for parallel computation with up to 97.7% scaling efficiency on 4096 cores;and 3) a scalable multinomial group testing analytics framework, demonstrated in a real-world disease surveillance case study using AIDS and STDs datasets from Uganda, where SBMGT reduced tests by up to 54% and lowered false classification rates by 92% compared to BGT.

关键词： Multinomial group testing Bayesian methods parallel algorithms Graph algorithms

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MARLIN: Mixed-Precision Auto-Regressive parallel Inference on Large Language Models 25

MARLIN: Mixed-Precision Auto-Regressive Parallel Inference o...

引用

30th symposium on principles and practice of parallel programming

作者： Frantar, Elias Castro, Roberto L. Chen, Jiale Hoefler, Torsten Alistarh, Dan IST Austria Klosterneuburg Austria Univ A Coruna CITIC La Coruna Spain Swiss Fed Inst Technol Zurich Switzerland Neural Mag Inc Somerville NJ USA

ISBN: (纸本)9798400714436

As inference on Large Language Models (LLMs) emerges as an important workload in machine learning applications, model weight quantization has become a standard technique for efficient GPU deployment. Quantization not only reduces model size, but has also been shown to yield substantial speedups for single-user inference, due to reduced memory movement, with low accuracy impact. Yet, it remains a key open question whether speedups are achievable also in batched settings with multiple parallel clients, which are highly relevant for practical serving. It is unclear whether GPU kernels can be designed to remain practically memory-bound, while supporting the substantially increased compute requirements of batched workloads. In this paper, we resolve this question positively by introducing a new design for Mixed-precision Auto-Regressive LINear kernels, called MARLIN. Concretely, given a model whose weights are compressed via quantization to, e.g., 4 bits per element, MARLIN shows that batchsizes up to 16-32 can be practically supported with close to maximum (4x) quantization speedup, and larger batchsizes up to 64-128 with gradually decreasing, but still significant, acceleration. MARLIN accomplishes this via a combination of techniques, such as asynchronous memory access, complex task scheduling and pipelining, and bespoke quantization support. Our experiments show that MARLIN's near-optimal performance on individual LLM layers across different scenarios can also lead to significant end-to-end LLM inference speedups (of up to 2.8x) when integrated with the popular vLLM opensource serving engine. Finally, we show that MARLIN is extensible to further compression techniques, like NVIDIA 2:4 sparsity, leading to additional speedups.

关键词： Large language model (LLM) inference GPU programming Batch parallelism

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POSTER: TensorMD: Molecular Dynamics Simulation with Ab Initio Accuracy of 50 Billion Atoms 30

POSTER: TensorMD: Molecular Dynamics Simulation with Ab Init...

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30th symposium on principles and practice of parallel programming

作者： Ouyang, Yucheng Liu, Yin Shang, Honghui Chen, Zhenchuan Shan, Jiahao Cui, Huimin Feng, Xiaobing Chen, Xin Gao, Xingyu Wang, Lifang Song, Haifeng Chen, Xin Lin, Rongfen Li, Fang Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Inst Appl Phys & Computat Math Beijing Peoples R China Natl Res Ctr Parallel Comp Engn & Technol Beijing Peoples R 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., similar to 3x compared with state-of-the-art.

关键词： Machine Learning Interatomic Potentials ManyCore Processor GPU Molecular Dynamics

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Minimizing speculation overhead in a parallel recognizer for regular texts 25

Minimizing speculation overhead in a parallel recognizer for...

引用

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

关键词： GPU

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PPDP'08 Proceedings of the 10th International acm sigplan symposium on principles and practice of Declarative programming

PPDP'08 Proceedings of the 10th International ACM SIGPLAN Sy...

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PPDP 2008: 10th International acm sigplan symposium on principles and practice of Declarative programming

ISBN: (纸本)9781605581170

The proceedings contain 25 papers. The topics discussed include: order-sorted dependency pairs;macros for context-free grammars;inferring precise polymorphic type dependencies in logic programs;a type system for safe memory management and its proof of correctness;programming with proofs and explicit contexts;towards execution time estimation in abstract machine-based languages;similarity-based reasoning in qualified logic programming;classifying integrity checking methods with regard to inconsistency tolerance;comprehending finite maps for algorithmic debugging of higher-order functional programs;parallel execution of multi-set constraint rewrite rules;a rewriting framework for the composition of access control policies;global difference constraint propagation for finite domain solvers;and dynamic variable elimination during propagation solving.

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PPDP'09 - Proceedings of the 11th International acm sigplan symposium on principles and practice of Declarative programming

PPDP'09 - Proceedings of the 11th International ACM SIGPLAN ...

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11th International acm sigplan symposium on principles and practice of Declarative programming, PPDP'09

ISBN: (纸本)9781605585680

The proceedings contain 28 papers. The topics discussed include: session types for linear multithreaded functional programming;automatic refactoring of Erlang programs;model-checking higher-order functions;a dynamically customizable process-centered evaluation model;a higher-order logical framework for the algorithmic debugging and verification of declarative programs;alternation as a programming paradigm;set functions for functional logic programming;reasoning with hypothetical judgments and open terms in hybrid;an Isabelle/Hol-based model of Stratego-like traversal strategies;making induction manifest in modular AC12;implementing a fixed point semantics for a constraint deductive database based on hereditary Harrop formulas;algorithmic specifications in linear logic with subexponentials;a logical framework for configuration software;path-sensitive region analysis for mercury programs;and an embedded declarative data transformation language.

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PPDP'06 - Proceedings of the Eight acm sigplan symposium on principles and practice of Declarative programming

PPDP'06 - Proceedings of the Eight ACM SIGPLAN Symposium on ...

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PPDP'06 - 8th acm sigplan symposium on principles and practice of Declarative programming

ISBN: (纸本)1595933883

The proceedings contain 24 papers. The topics discussed include: typed polyadic pi-calculus in bigraphs;combining Fuzzy logic and behavioral similarity for non-strict program validation;type oriented construction of web user interfaces;an abductive framework for A-priori verification of web services;an efficient algorithms for XML type projection;type inference for spreadsheets;polymorphic algebraic data type reconstruction;extracting programs from type class proofs;computing constructor forms with non terminating rewrite programs;open data types and open functions;a historic functional and object-oriented calculus;combining algorithmic debugging and program slicing;practical type inference based on success typings;collection analysis for Horn clause programs;a model type system for multi-level generating extensions with persistent code;a type system equivalent to static single assignment;and poly-controlled partial evaluation.

关键词： Logic programming

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