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检索条件"任意字段=16th ACM Symposium on Principles and Practice of Parallel Programming"

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Approximation algorithms for minimizing empirical error by axis-parallel hyperplanes

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16th European Conference on Machine Learning (ECML)/9th European Conference on principles and practice of Knowledge Discovery in Databases (PKDD)

作者： Elomaa, T Kujala, J Rousu, J Tampere Univ Technol Inst Software Syst FIN-33101 Tampere Finland Univ London Royal Holloway & Bedford New Coll Dept Comp Sci London NW1 4NS England

ISBN: (纸本)3540292438

Many learning situations involve separation of labeled training instances by hyperplanes. Consistent separation is of theoretical interest, but the real goal is rather to minimize the number of errors using a bounded number of hyperplanes. Exact minimization of empirical error in a high-dimensional grid induced into the feature space by axis-parallel hyperplanes is NP-hard. We develop two approximation schemes with performance guarantees, a greedy set covering scheme for producing a consistently labeled grid, and integer programming rounding scheme for finding the minimum error grid with bounded number of hyperplanes.

关键词： Algorithms

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Training one DeePMD Model in Minutes: a Step towards Online Learning 24

Training one DeePMD Model in Minutes: a Step towards Online ...

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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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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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A case study in SIMD text processing with parallel bit streams UTF-8 to UTF-16 transcoding

A case study in SIMD text processing with parallel bit strea...

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作者： Cameron, Robert D. School of Computing Science Simon Fraser University Canada

ISBN: (纸本)9781595939609

High performance SIMD text processing using the method of parallel bit streams is introduced with a case study of UTF-8 to UTF-16 transcoding. A forward transform converts byte-oriented character stream data into eight parallel bit streams. Decoding, validation and computation of UTF-8 indexed UTF-16 bit streams are performed using bit-parallel logic and shifting operations. Conversion from UTF-8 indexing to UTF-16 indexing is performed using parallel bit deletion. the inverse transform is applied to yield high and low UTF-16 byte streams which are then merged. Combined with optimization techniques for blocks of ASCII data, speed-ups of 3 to 25 times are achieved on commodity processors compared with optimized byte-at-a-time code. Further applications of the method of parallel bit streams to bulk text processing applications are briefly discussed along with future prospects for the combination of intraregister and intrachip parallelism on multicore processors. Copyright © 2008 acm.

关键词： Text processing

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Array data-flow analysis and its use in array privatization 93

Array data-flow analysis and its use in array privatization

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20th Annual acm SIGPLAN-SIGACT symposium on principles of programming Languages, POPL'93

作者： Maydan, Dror E. Amarasinghe, Saman P. Lam, Monica S. Stanford Univ CA United States

ISBN: (纸本)0897915607

Data-flow analysis of scalar variables and data dependence analysis on array elements are two important program analyses used in optimizing and parallelizing compilers. Traditional data-flow analysis models accesses of array elements simply as accesses to the entire array, and is inadequate for parallelizing loops in array-based programs. On the other hand, data dependence analysis differentiates between different array elements but is flow-insensitive. this paper studies the combination of these two analyses - data-flow analysis of accesses to individual array elements. the problem of finding precise array data-flow information in the domain of loop nests where the loop bounds and array indices are affine functions of loop indices was first formulated by Feautrier. Feautrier's algorithm, based on parametric integer programming techniques, is general but inefficient. this paper presents an efficient algorithm that can find the same precise information for many of the programs found in practice. In this paper, we argue that data-flow analysis of individual array elements is necessary for effective automatic parallelization. In particular, we demonstrate the use of array data-flow analysis in an important optimization known as array privatization. By demonstrating that array data-flow analysis can be computed efficiently and by showing the importance of the optimizations enabled by the analysis, this paper suggests that array data-flow analysis may become just as important in future optimizing and parallelizing compilers as data-flow and data dependence analysis are in today's compilers.

关键词： parallel processing systems

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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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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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Architectural Support for Cilk Computations on Many-core Architectures

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acm SIGPLAN NOTICES 2009年第4期44卷 285-286页

作者： Long, Guoping Fan, Dongrui Zhang, Junchao Chinese Acad Sci Inst Comp Technol Key Lab Comp Syst & Architecture Beijing 100864 Peoples R China

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Para-functional programming: A paradigm for programming multiprocessor systems 86

Para-functional programming: A paradigm for programming mult...

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13th AnnuaJ acm symposium on the principles of programming Languages, POPL 1986

作者： Hudak, Paul Smith, Lauren Yale University Department of Computer Science United States

ISBN: (纸本)089791175X

One of the most important pragmatic advantages of functional languages is that concurrency in a program is implicit-there is no need for special constructs to express parallelism as is required in most conventional languages. Furthermore, it is fairly easy for compilers to automatically determine the concurrency as a step toward decomposing a program for execution on a suitable parallel architecture. Yet it is often the case that one knows precisely the optimal decomposition for execution on a particular machine, and one can never expect a compiler to determine such optimal mappings in all cases. this paper is concerned with ways to allow the programmer to explicitly express this mapping of program to machine, by using annotations that, given a few minor constraints, cannot alter the functional semantics of the program. We show through several deeded examples the expressiveness and conciseness of the resulting " para-functional programming methodology, using an experimental language called ParAlfl based on our ideas. We also give a formal denotational description of the mapping semantics using a notion of execution trees.

关键词： Functional programming

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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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