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

作者： Hu, Siyu Zhao, Tong Sha, Qiuchen Li, Enji Meng, Xiangyu Liu, Liping Wang, Lin-Wang Tan, Guangming Jia, Weile Chinese Acad Sci Inst Comp Technol State Key Lab Proc Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China China Univ Petr Qingdao Inst Software Coll Comp Sci & Technol Qingdao Peoples R China Chinese Acad Sci Inst Semicond Beijing Peoples R China

ISBN: (纸本)9798400704352

Neural Network Molecular Dynamics (NNMD) has become a major approach in material simulations, which can speedup the molecular dynamics (MD) simulation for thousands of times, while maintaining ab initio accuracy, thus has a potential to fundamentally change the paradigm of material simulations. However, there are two time-consuming bottlenecks of the NNMD developments. One is the data access of ab initio calculation results. the other, which is the focus of the current work, is reducing the training time of NNMD model. the training of NNMD model is different from most other neural network training because the atomic force (which is related to the gradient of the network) is an important physical property to be fit. Tests show the traditional stochastic gradient methods, like the Adam algorithms, cannot efficiently deploy the multisample minibatch algorithm. As a result, a typical training (taking the Deep Potential Molecular Dynamics (DeePMD) as an example) can take many hours. In this work, we designed a heuristic minibatch quasi-Newtonian optimizer based on Extended Kalman Filter method. An early reduction of gradient and error is adopted to reduce memory footprint and communication. the memory footprint, communication and settings of hyper-parameters of this new method are analyzed in detail. Computational innovations such as customized kernels of the symmetry-preserving descriptor are applied to exploit the computing power of the heterogeneous architecture. Experiments are performed on 8 different datasets representing different real case situations, and numerical results show that our new method has an average speedup of 32.2 compared to the Reorganized Layer-wised Extended Kalman Filter with 1 GPU, reducing the absolute training time of one DeePMD model from hours to several minutes, making it one step toward online training.

关键词： parallel training Molecular dynamics First principle ab initio GPU

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NUMA-aware graph-structured analytics 2015

NUMA-aware graph-structured analytics

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20th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2015

作者： Zhang, Kaiyuan Chen, Rong Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781450332057

Graph-structured analytics has been widely adopted in a number of big data applications such as social computation, web-search and recommendation systems. though much prior research focuses on scaling graph-analytics on distributed environments, the strong desire on performance per core, dollar and joule has generated considerable interests of processing large-scale graphs on a single server-class machine, which may have several terabytes of RAM and 80 or more cores. However, prior graph-analytics systems are largely neutral to NUMA characteristics and thus have suboptimal performance. this paper presents a detailed study of NUMA characteristics and their impact on the efficiency of graph-analytics. Our study uncovers two insights: 1) either random or interleaved allocation of graph data will significantly hamper data locality and parallelism;2) sequential inter-node (i.e., remote) memory accesses have much higher bandwidth than both intra- and inter-node random ones. Based on them, this paper describes Polymer, a NUMA-aware graph-analytics system on multicore with two key design decisions. First, Polymer differentially allocates and places topology data, application-defined data and mutable runtime states of a graph system according to their access patterns to minimize remote accesses. Second, for some remaining random accesses, Polymer carefully converts random remote accesses into sequential remote accesses, by using lightweight replication of vertices across NUMA nodes. To improve load balance and vertex convergence, Polymer is further built with a hierarchical barrier to boost parallelism and locality, an edge-oriented balanced partitioning for skewed graphs, and adaptive data structures according to the proportion of active vertices. A detailed evaluation on an 80-core machine shows that Polymer often outperforms the state-of-the-art single-machine graph-analytics systems, including Ligra, X-Stream and Galois, for a set of popular real-world and synthetic grap

关键词： Random access storage

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Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

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28th acm sigplan Annual symposium on principles and practice of parallel programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. they lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. then, an optimistic STM method is used to reduce structure conflicts. the query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 acm.

关键词： Graphics processing unit

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Lifetime-Based Optimization for Simulating Quantum Circuits on a New Sunway Supercomputer 23

Lifetime-Based Optimization for Simulating Quantum Circuits ...

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28th acm sigplan Annual symposium on principles and practice of parallel programming, PPoPP 2023

作者： Chen, Yaojian Liu, Yong Shi, Xinmin Song, Jiawei Liu, Xin Gan, Lin Guo, Chu Fu, Haohuan Gao, Jie Chen, Dexun Yang, Guangwen Tsinghua University Beijing China National Supercomputing Center in Wuxi Zhejiang Lab Hangzhou China Information Engineering University Zhengzhou China National Supercomputing Center in Wuxi China Tsinghua University National Supercomputing Center in Wuxi China National Research Centre of Parallel Engineering and Technology Beijing China Tsinghua University National Supercomputing Center in Wuxi Zhejiang Lab Hangzhou China

ISBN: (纸本)9798400700156

High-performance classical simulator for quantum circuits, in particular the tensor network contraction algorithm, has become an important tool for the validation of noisy quantum computing. In order to address the memory limitations, the slicing technique is used to reduce the tensor dimensions, but it could also lead to additional computation overhead that greatly slows down the overall performance. this paper proposes novel lifetime-based methods to reduce the slicing overhead and improve the computing efficiency, including, an interpretation method to deal with slicing overhead, an inplace slicing strategy to find the smallest slicing set and an adaptive tensor network contraction path refiner customized for Sunway architecture. Experiments show that in most cases the slicing overhead with our inplace slicing strategy would be less than the Cotengra, which is the most used graph path optimization software at present. Finally, the resulting simulation time is reduced to 96.1s for the Sycamore quantum processor RQC, with a sustainable single-precision performance of 308.6Pflops using over 41M cores to generate 1M correlated samples, which is more than 5 times performance improvement compared to 60.4 Pflops in 2021 Gordon Bell Prize work. © 2023 Owner/Author.

关键词： Timing circuits

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A Scalable Hybrid Total FETI Method for Massively parallel FEM Simulations 23

A Scalable Hybrid Total FETI Method for Massively Parallel F...

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28th acm sigplan Annual symposium on principles and practice of parallel programming, PPoPP 2023

作者： Lin, Kehao Zhou, Chunbao Zeng, Yan Nie, Ningming Wang, Jue Li, Shigang Feng, Yangde Wang, Yangang Yao, Kehan Yao, Tiechui Zhang, Jilin Wan, Jian Hangzhou Dianzi University Hangzhou China Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798400700156

the Hybrid Total Finite Element Tearing and Interconnecting (HTFETI) method plays an important role in solving large-scale and complex engineering problems. this method needs to handle numerous matrix-vector multiplications. Directly calling the vendor-optimized library for general matrix-vector multiplication (gemv) on GPU leads to low performance, since it does not consider optimizations for different matrix sizes in HTFETI, i.e. different row and column sizes. In addition, state-of-the-art graph partitioning methods cannot guarantee load balancing for HTFETI, since the matrix size is determined by the length of the subdomain boundary. To solve the problems above, we first port gemv to the multi-stream pipeline scheme and develop a new batched kernel function on GPU, which brings 15%∼30% throughput improvement and 37% average GFLOPs improvement, respectively. We also propose a multi-grained load-balancing scheme based on graph repartitioning and work-stealing, and the load imbalance ratio is down to 1.05∼1.09 from 1.5. We have successfully applied the scalable HTFETI method to simulate the whole core assembly of China Experimental Fast Reactor (CEFR) for steady-state analysis, and the efficiencies of weak scalability and strong scalability reach 78% and 72% on 12,288 GPUs, respectively. As far as we know, this is the first time that HTFETI has been used in large-scale and high-fidelity whole core assembly simulation. © 2023 Owner/Author.

关键词： Scalability

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Real-Time Matching of Antescofo Temporal Patterns 14

Real-Time Matching of <i>Antescofo</i> Temporal Patterns

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16th International symposium on principles and practice of Declarative programming (PPDP)

作者： Giavitto, Jean-Louis Echeveste, Jose IRCAM UMR STMS 9912 CNRS 1 Pl Igor Stravinsky F-75004 Paris France INRIA MuTant Project 1 Pl Igor Stravinsky F-75004 Paris France Univ Paris 06 Sorbonne Univ 1 Pl Igor Stravinsky F-75004 Paris France

ISBN: (纸本)9781450329477

online matching. Antescofo is a real-time system for performance coordination between musicians and computer processes during live music concert. ATP are used to define complex events that correspond to a combination of perceived events in the musical environment as well as arbitrary logical and metrical temporal conditions. the real-time recognition of such event is used to trigger arbitrary actions in the style of event-condition-action rules. the musical context, the rationales of temporal patterns and several illustrative examples are introduced to motivate the design of ATP. the semantics of ATP matching is defined to parallel the well-known notion of regular expression and Brzozowski's derivatives but extended to handle an infinite alphabet, arbitrary predicates, elapsing time and inhibitory conditions. this approach is compared to those developed in log auditing and for the runtime verification of realtime logics. ATP are implemented by translation into a core subset of the Antescofo domain-specific language. this compilation has proven efficient enough to avoid the extension of the real-time runtime of the language and has been validated with composers in actual pieces.

关键词： timed regular expressions event-driven programming score following timed and reactive system domain-specific language computer music Antescofo

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JParEnt: parallel entropy decoding for JPEG decompression on heterogeneous multicore architectures 7

JParEnt: Parallel entropy decoding for JPEG decompression on...

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Euro-Par Conference / 7th International Workshop on programming Models and Applications for Multicores and Manycores (PMAM) held in conjunction with the 21st sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Sodsong, Wasuwee Jung, Minyoung Park, Jinwoo Burgstaller, Bernd Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9781450341967

the JPEG format employs Huffman codes to compress the entropy data of an image. Huffman codewords are of variable length, which makes parallel entropy decoding a difficult problem. To determine the start position of a codeword in the bitstream, the previous codeword must be decoded first. We present JParEnt, a new approach to parallel entropy decoding for JPEG decompression on heterogeneous multicores. JParEnt conducts JPEG decompression in two steps: (1)an efficient sequential scan of the entropy data on the CPU to determine the start-positions (boundaries) of coefficient blocks in the bitstream, followed by (2)a parallel entropy decoding step on the graphics processing unit (GPU). the block boundary scan constitutes a reinterpretation of the Huffman-coded entropy data to determine codeword boundaries in the bitstream. We introduce a dynamic workload partitioning scheme to account for GPUs of low compute power relative to the CPU. this configuration has become common with the advent of SoCs with integrated graphics processors (IGPs). We leverage additional parallelism through pipelined execution across CPU and GPU. For systems providing a unified address space between CPU and GPU, we employ zero-copy to completely eliminate the data transfer overhead. Our experimental evaluation of JParEnt was conducted on six heterogeneous multicore systems: one server and two desktops with dedicated GPUs, one desktop with an IGP, and two embedded systems. For a selection of more than 1000JPEG images, JParEnt outperforms the SIMD-implementation of the libjpeg-turbo library by up to a factor of 4.3x, and the previously fastest JPEG decompression method for heterogeneous multicores by up to a factor of 2.2x. JParEnt's entropy data scan consumes 45% of the entropy decoding time of libjpeg-turbo on average. Given this new ratio for the sequential part of JPEG decompression, JParEnt achieves up to97% of the maximum attainable speedup (95% on average). On the IGP-based desktop platform,

关键词： JPEG decoding entropy decoding prefix codes heterogeneous multicores GPU programming

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OpenCilk: A Modular and Extensible Software Infrastructure for Fast Task-parallel Code 23

OpenCilk: A Modular and Extensible Software Infrastructure f...

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28th acm sigplan Annual symposium on principles and practice of parallel programming, PPoPP 2023

作者： Schardl, Tao B. Lee, I.-Ting Angelina MIT CSAIL United States Washington University St. Louis United States

ISBN: (纸本)9798400700156

this paper presents OpenCilk, an open-source software infrastructure for task-parallel programming that allows for substantial code reuse and easy exploration of design choices in language abstraction, compilation strategy, runtime mechanism, and productivity-tool development. the OpenCilk infrastructure consists of three main components: a compiler designed to compile fork-join task-parallel code, an efficient work-stealing runtime scheduler, and a productivity-tool development framework based on compiler instrumentation designed for fork-join parallel computations. OpenCilk is modular - modifying one component for the most part does not necessitate modifications to the other components - and easy to extend - its construction naturally encourages code reuse. Despite being modular and easy to extend, OpenCilk produces high-performing code. We investigated OpenCilk's modularity, extensibility, and performance through several case studies, including a study to extend OpenCilk to support multiple parallel runtime systems, including Cilk Plus, OpenMP, and oneTBB. OpenCilk's design enables rapid prototyping of new compiler back ends to target different parallel-runtime ABIs. Each back end required fewer than 2000 new lines of code. We examined the OpenCilk runtime's performance empirically on 15 benchmark Cilk programs and found that it outperforms the other runtimes by a geometric mean of 4% - 26% on 1 core and 10% - 120% on 48 cores. © 2023 Owner/Author.

关键词： parallel programming

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parallel algorithms for image histogramming and connected components with an experimental study

Parallel algorithms for image histogramming and connected co...

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proceedings of the 5th acm sigplan symposium on principles and practice of parallel programming

作者： Bader, David A. JaJa, Joseph Univ of Maryland College Park United States

this paper presents efficient and portable implementations of two useful primitives in image processing algorithms, histogramming and connected components. Our general framework is a single-address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. Our connected components algorithm uses a novel approach for parallel merging which performs drastically limited updating during iterative steps, and concludes with a total consistency update at the final step. the algorithms have been coded in Split-C and run on a variety of platforms. Our experimental results are consistent with the theoretical analysis and provide the best known execution times for these two primitives, even when compared with machine-specific implementations. More efficient implementations of Split-C will likely result in even faster execution times.

关键词： parallel algorithms

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Dynamic instrumentation of threaded applications

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 1999年 49-59页

作者： Xu, Zhichen Miller, Barton P. Naim, Oscar Univ of Wisconsin Madison WI United States

the design of non-trace based performance instrumentation techniques for threaded programs is investigated to provide detailed performance data while maintaining control of instrumentation costs. the design is based on low contention data structures. the Paradyn's dynamic instrumentation is extended to handle threaded programs. To associate data with individual threads, all threads must share the same instrumentation code and assign each thread with its own private copy of performance counters or timers. the asynchrony in a threaded program poses a major challenge to dynamic instrumentation.

关键词： parallel processing systems

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