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检索条件"任意字段=Proceedings of the ninth ACM SIGPLAN symposium on Principles and practice of parallel programming"

共 564 条记录，以下是11-20 订阅

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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...

引用

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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DORADD: Deterministic parallel Execution in the Era of Microsecond-Scale Computing 25

DORADD: Deterministic Parallel Execution in the Era of Micro...

引用

30th symposium on principles and practice of parallel programming

作者： Liu, Zhengqing Unal, Musa Parkinson, Matthew J. Kogias, Marios Imperial Coll London London England Ecole Polytech Fed Lausanne Lausanne Switzerland Azure Res Austin TX USA

ISBN: (纸本)9798400714436

Deterministic parallelism is a key building block for distributed and fault-tolerant systems that offers substantial performance benefits while guaranteeing determinism. By studying existing deterministically parallel systems (DPS), we identify certain design pitfalls, such as batched execution and inefficient runtime synchronization, that preclude them from meeting the demands of mu s-scale and high-throughput distributed systems deployed in modern datacenters. We present DORADD, a deterministically parallel runtime with low latency and high throughput, designed for modern datacenter services. DORADD introduces a hybrid scheduling scheme that effectively decouples request dispatching from execution. It employs a single dispatcher to deterministically construct a dynamic dependency graph of incoming requests and worker pools that can independently execute requests in a work-conserving and synchronization-free manner. Furthermore, DORADD overcomes the single-dispatcher throughput bottleneck based on core pipelining. We use DORADD to build an in-memory database and compare it with Caracal, the current state-of-the-art deterministic database, via the YCSB and TPC-C benchmarks. Our evaluation shows up to 2.5x better throughput and more than 150x and 300x better tail latency in non-contended and contended cases, respectively. We also compare DO-RADD with Caladan, the state-of-the-art non-deterministic remote procedure call (RPC) scheduler, and demonstrate that determinism in DORADD does not incur any performance overhead.

关键词： parallel execution determinism runtime scheduling

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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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parallel Integer Sort: Theory and practice 24

Parallel Integer Sort: Theory and Practice

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29th acm sigplan Annual symposium on principles and practice of parallel programming (PPoPP)

作者： Dong, Xiaojun Dhulipala, Laxman Gu, Yan Sun, Yihan UC Riverside Riverside CA 92521 USA Univ Maryland Baltimore MD USA

ISBN: (纸本)9798400704352

Integer sorting is a fundamental problem in computer science. This paper studies parallel integer sort both in theory and in practice. In theory, we show tighter bounds for a class of existing practical integer sort algorithms, which provides a solid theoretical foundation for their widespread usage in practice and strong performance. In practice, we design a new integer sorting algorithm, DovetailSort, that is theoreticallyefficient and has good practical performance. In particular, DovetailSort overcomes a common challenge in existing parallel integer sorting algorithms, which is the difficulty of detecting and taking advantage of duplicate keys. The key insight in DovetailSort is to combine algorithmic ideas from both integer- and comparison-sorting algorithms. In our experiments, DovetailSort achieves competitive or better performance than existing state-of-the-art parallel integer and comparison sorting algorithms on various synthetic and real-world datasets.

关键词： Integer Sort Radix Sort parallel 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...

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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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Pure: Evolving Message Passing To Better Leverage Shared Memory Within Nodes 24

Pure: Evolving Message Passing To Better Leverage Shared Mem...

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29th acm sigplan Annual symposium on principles and practice of parallel programming (PPoPP)

作者： Psota, James Solar-Lezama, Armando MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9798400704352

Pure is a new programming model and runtime system explicitly designed to take advantage of shared memory within nodes in the context of a mostly message passing interface enhanced with the ability to use tasks to make use of idle cores. Pure leverages shared memory in two ways: (a) by allowing cores to steal work from each other while waiting on messages to arrive, and, (b) by leveraging *** lock-free data structures in shared memory to achieve highperformance messaging and collective operations between the ranks within nodes. We use microbenchmarks to evaluate Pure's key messaging and collective features and also show application speedups up to 2.1 Chi on the CoMD molecular dynamics and the miniAMR adaptive mesh *** applications scaling up to 4,096 cores.

关键词： parallel programming models distributed runtime systems task-based parallelism concurrent data structures lock-free data structures

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POSTER: RadiK: Scalable Radix Top-K Selection on GPUs 24

POSTER: RadiK: Scalable Radix Top-K Selection on GPUs

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29th acm sigplan Annual symposium on principles and practice of parallel programming (PPoPP)

作者： Li, Yifei Zhou, Bole Zhang, Jiejing Wei, Xuechao Li, Yinghan Chen, Yingda Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798400704352

By identifying the.. largest or smallest elements in a set of data, top-k selection is critical for modern high-performance databases and machine learning systems, especially with large data volumes. However, previous studies on its GPU implementation are mostly merge-based and rely heavily on the high-speed but size-limited on-chip memory, thereby resulting in a restricted upper bound on... This paper introduces RadiK, a highly optimized GPU-parallel radix top-k selection that is scalable with.., input length, and batch size. With a carefully designed optimization framework targeting high memory bandwidth and resource utilization, RadiK supports far larger.. than the prior art, achieving up to 2.5x speedup for non-batch queries and up to 4.8x speedup for batch queries. We also propose a lightweight refinement that strengthens the robustness of RadiK against skewed distributions by adaptively scaling the input elements.

关键词： Top-K Radix Select GPU-parallel Algorithm

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GraphCube: Interconnection Hierarchy-aware Graph Processing 24

GraphCube: Interconnection Hierarchy-aware Graph Processing

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29th acm sigplan Annual symposium on principles and practice of parallel programming (PPoPP)

作者： Gan, Xinbiao Wu, Guang Qiu, Shenghao Xiong, Feng Si, Jiaqi Fang, Jianbin Dong, Dezun Gong, Chunye Li, Tiejun Wang, Zheng NUDT Beijing Peoples R China Univ Leeds Leeds W Yorkshire England Natl Supercomputer Ctr Tianjin Peoples R China

ISBN: (纸本)9798400704352

Processing large-scale graphs with billions to trillions of edges requires efficiently utilizing parallel systems. However, current graph processing engines do not scale well beyond a few tens of computing nodes because they are oblivious to the communication cost variations across the interconnection hierarchy. We introduce GraphCube, a better approach to optimizing graph processing on large-scale parallel systems with complex interconnections. GraphCube features a new graph partitioning approach to achieve better load balancing and minimize communication overhead across multiple levels of the interconnection hierarchy. We evaluate GraphCube by applying it to fundamental graph operations performed on synthetic and real-world graph datasets. Our evaluation used up to 79,024 computing nodes and 1.2+ million processor cores. Our large-scale experiments show that GraphCube outperforms state-of-the-art parallel graph processing methods in throughput and scalability. Furthermore, GraphCube outperformed the top-ranked systems on the Graph 500 list.

关键词： Graph processing Graph partitioning parallel computing Vectorization Graph500

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CPMA: An Efficient Batch-parallel Compressed Set Without Pointers 24

CPMA: An Efficient Batch-Parallel Compressed Set Without Poi...

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29th acm sigplan Annual symposium on principles and practice of parallel programming (PPoPP)

作者： Wheatman, Brian Burns, Randal Buluc, Aydin Xu, Helen Johns Hopkins Univ Baltimore MD 21218 USA Lawrence Berkeley Natl Lab Lawrence KS USA Georgia Inst Technol Atlanta GA USA

ISBN: (纸本)9798400704352

This paper introduces the batch-parallel Compressed Packed Memory Array (CPMA), a compressed, dynamic, ordered set data structure based on the Packed Memory Array (PMA). Traditionally, batch-parallel sets are built on pointerbased data structures such as trees because pointer-based structures enable fast parallel unions via pointer manipulation. Whencompared with cache-optimized trees, PMAswere slower to update but faster to scan. The batch-parallel CPMA overcomes this tradeoff between updates and scans by optimizing for cache-friendliness. On average, the CPMA achieves 3x faster batch-insert throughput and 4x faster range-query throughput compared with compressed PaC-trees, a state-of-the-art batch-parallel set library based on cache-optimized trees. We further evaluate the CPMA compared with compressed PaC-trees and Aspen, a state-of-the-art system, on a realworld application of dynamic-graph processing. The CPMA is on average 1.2x faster on a suite of graph algorithms and 2x faster on batch inserts when compared with compressed PaC-trees. Furthermore, the CPMA is on average 1.3x faster on graph algorithms and 2x faster on batch inserts compared with Aspen.

关键词： packed memory array batch-parallel compression data structures dynamic graphs

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INFINEL: An efficient GPU-based processing method for unpredictable large output graph queries 24

INFINEL: An efficient GPU-based processing method for unpred...

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29th acm sigplan Annual symposium on principles and practice of parallel programming (PPoPP)

作者： Park, Sungwoo Oh, Seyeon Kim, Min-Soo Korea Adv Inst Sci & Technol Seoul South Korea GraphAI Seoul South Korea

ISBN: (纸本)9798400704352

With the introduction of GPUs, which are specialized for iterative parallel computations, the execution of computationintensive graph queries using a GPU has seen significant performance improvements. However, due to the memory constraints of GPUs, there has been limited research on handling large-scale output graph queries with unpredictable output sizes on a GPU. Traditionally, two-phase methods have been used, where the query is re-executed after splitting it into sub-tasks while only considering the size of the output in a static manner. However, two-phase methods become highly inefficient when used with graph data with extreme skew, failing to maximize the GPU performance. This paper proposes INFINEL, which handles unpredictable large output graph queries in a one-phase method through chunk allocation per thread and kernel stop/restart methods. We also propose applicable optimization techniques due to the corresponding unique characteristics of operating with low time/space overhead and not heavily relying on the GPU output buffer size. Through extensive experiments, we demonstrate that our one-phase method of INFINEL improves the performance by up to 31.5 times over the conventional twophase methods for triangle listing ULO query.

关键词： Graph query processing Large output parallel computing GPU

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