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Productive Programming of distributed Systems with the SHAD C++ Library 21

Productive Programming of Distributed Systems with the SHAD ...

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30th international symposium on High-Performance parallel and distributed computing, HPDC 2021

作者： Castellana, Vito Giovanni Minutoli, Marco Pacific Northwest National Laboratory RichlandWA United States

ISBN: (纸本)9781450382175

High-performance computing (HPC) is often perceived as a matter of making large-scale systems (e.g., clusters) run as fast as possible, regardless the required programming effort. However, the idea of "bringing HPC to the masses"has recently emerged. Inspired by this vision, we have designed SHAD, the Scalable High-performance Algorithms and Data-structures library [1][6]. SHAD is open source software, written in C++, for C++ developers. Unlike other HPC libraries for distributed systems, which rely on SPMD models, SHAD adopts a shared-memory programming abstraction, to make C++ programmers feel at home. Underneath, SHAD manages tasking and data-movements, moving the computation where data resides and taking advantage of asynchrony to tolerate network latency. At the bottom of his stack, SHAD can interface with multiple runtime systems: this not only improves developer's productivity, by hiding the complexity of such software and of the underlying hardware, but also greatly enhance code portability. Thanks to its abstraction layers, SHAD can indeed target different systems, ranging from laptops to HPC clusters, without any need for modifying the user-level *** have prototyped and open-sourced the implementation of (a subset of) the C++ standard library (STL) targeting multi-node HPC clusters. Our work allows plain STL-based C++ code to scale on HPC systems, with no need for rewriting the code to exploit the complex hardware. SHAD is available under Apache v2 License at https://***/pnnl/SHAD. In this paper we overview the design of the SHAD library, depicting its main components: runtime systems abstractions for tasking;parallel and distributed data-structures;STL-compliant interfaces and algorithms. © 2020 Copyright is held by the owner/author(s).

关键词： parallel programming

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Towards Faster Fully Homomorphic Encryption Implementation with Integer and Floating-point computing Power of GPUs

Towards Faster Fully Homomorphic Encryption Implementation w...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Guang Fan Fangyu Zheng Lipeng Wan Lili Gao Yuan Zhao Jiankuo Dong Yixuan Song Yuewu Wang Jingqiang Lin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cryptography University of Chinese Academy of Sciences Beijing China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing China Ant Group Hangzhou China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Cyber Security University of Science and Technology of China Hefei China

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without knowledge of the plaintext message and currently has been the focus of both academia and industry. However, the performance issue hinders its large-scale application, highlighting the urgent requirements of high-performance FHE *** noticing the tremendous potential of GPUs in the field of cryptographic acceleration, this paper comprehensively investigates how to convert the available computing resources residing in GPUs into FHE workhorses, and implement a full set of low-level and middle-level FHE primitives based on two arithmetic units (i.e., INT32 and FP64 units) with three types of data precision (i.e., INT32, INT64 and FP64). This paper gives a comprehensive evaluation and comparison based on each road-map. Our implementations of fundamental functions outperform the implementations on the same platform by 1.7× to 16.7×. Taking CKKS FHE schemes as a case study, our implementation of homomorphic multiplication achieves 3.2× speedup over the state-of-the-art GPU-based implementation, even considering the difference of platforms. The detailed evaluation and comparison of this paper would offer a vital reference for the follow-up work to choose appropriate underlying arithmetic units and important primitive optimizations in GPU-based FHE implementations.

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CliZ: Optimizing Lossy Compression for Climate Datasets with Adaptive Fine-tuned Data Prediction

CliZ: Optimizing Lossy Compression for Climate Datasets with...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Zizhe Jian Sheng Di Jinyang Liu Kai Zhao Xin Liang Haiying Xu Robert Underwood Shixun Wu Jiajun Huang Zizhong Chen Franck Cappello University of California Riverside CA USA Argonne National Laboratory Lemont IL USA Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA Florida State University Tallahassee FL USA University of Kentucky Lexington KY USA National Center for Atmospheric Research Boulder CO USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Benefiting from the cutting-edge supercomputers that support extremely large-scale scientific simulations, climate research has advanced significantly over the past decades. However, new critical challenges have arisen regarding efficiently storing and transferring large-scale climate data among distributed repositories and databases for post hoc analysis. In this paper, we develop CliZ, an efficient online error-controlled lossy compression method with optimized data prediction and encoding methods for climate datasets across various climate models. On the one hand, we explored how to take advantage of particular properties of the climate datasets (such as mask-map information, dimension permutation/fusion, and data periodicity pattern) to improve the data prediction accuracy. On the other hand, CliZ features a novel multi-Huffman encoding method, which can significantly improve the encoding efficiency. Therefore significantly improving compression ratios. We evaluated CliZ versus many other state-of-the-art error-controlled lossy compressors (including SZ3, ZFP, SPERR, and QoZ) based on multiple real-world climate datasets with different models. Experiments show that CliZ outperforms the second-best compressor (SZ3, SPERR, or QoZ1.1) on climate datasets by 20%-200% in compression ratio. CliZ can significantly reduce the data transfer cost between the two remote Globus endpoints by 32%-38%.

关键词： distributed processing Costs distributed databases Predictive models Data transfer Encoding Compressors

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FedTrip: A Resource-Efficient Federated Learning Method with Triplet Regularization

FedTrip: A Resource-Efficient Federated Learning Method with...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Xujing Li Min Liu Sheng Sun Yuwei Wang Hui Jiang Xuefeng Jiang Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China

In the federated learning scenario, geographically distributed clients collaboratively train a global model. Data heterogeneity among clients significantly results in inconsistent model updates, which evidently slow down model convergence. To alleviate this issue, many methods employ regularization terms to narrow the discrepancy between client-side local models and the server-side global model. However, these methods impose limitations on the ability to explore superior local models and ignore the valuable information in historical models. Besides, although the up-to-date representation method simultaneously concerns the global and historical local models, it suffers from unbearable computation cost. To accelerate convergence with low resource consumption, we innovatively propose a model regularization method named FedTrip, which is designed to restrict global-local divergence and decrease current-historical correlation for alleviating the negative effects derived from data heterogeneity. FedTrip helps the current local model to be close to the global model while keeping away from historical local models, which contributes to guaranteeing the consistency of local updates among clients and efficiently exploring superior local models with negligible additional computation cost on attaching operations. Empirically, we demonstrate the superiority of FedTrip via extensive evaluations. To achieve the target accuracy, FedTrip outperforms the state-of-the-art baselines in terms of significantly reducing the total overhead of client-server communication and local computation.

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Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments

Apodotiko: Enabling Efficient Serverless Federated Learning ...

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IEEE/ACM international symposium on Cluster computing and the Grid (CCGRID)

作者： Mohak Chadha Alexander Jensen Jianfeng Gu Osama Abboud Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (near Munich) Germany Huawei Technologies Munich Germany

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized. Recent works on designing systems for efficient FL have shown that utilizing serverless computing technologies, particularly Function-as-a-Service (FaaS) for FL, can enhance resource efficiency, reduce training costs, and alleviate the complex infrastructure management burden on data holders. However, current serverless FL systems still suffer from the presence of stragglers, i.e., slow clients that impede the collaborative training process. While strategies aimed at mitigating stragglers in these systems have been proposed, they overlook the diverse hardware resource configurations among FL clients. To this end, we present Apodotiko, a novel asynchronous training strategy designed for serverless FL. Our strategy incorporates a scoring mechanism that evaluates each client’s hardware capacity and dataset size to intelligently prioritize and select clients for each training round, thereby minimizing the effects of stragglers on system performance. We comprehensively evaluate Apodotiko across diverse datasets, considering a mix of CPU and GPU clients, and compare its performance against five other FL training strategies. Results from our experiments demonstrate that Apodotiko outperforms other FL training strategies, achieving an average speedup of 2.75x and a maximum speedup of 7.03x. Furthermore, our strategy significantly reduces cold starts by a factor of four on average, demonstrating suitability in serverless environments.

关键词： Training Federated learning System performance Prevention and mitigation Collaboration Serverless computing Graphics processing units distributed databases Hardware Data models

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international Workshop on Quantum Classical Cooperative computing (QCCC 2022)

Proceedings - 2022 IEEE 36th International Parallel and Dist...

引用

Proceedings - 2022 IEEE 36th international parallel and distributed Processing symposium Workshops, IPDPSW 2022 2022年 598-598页

作者： Li, Ang Guan, Qiang Pacific Northwest National Laboratory United States Kent State University United States

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Approximation Algorithms and Simulated Annealing Heuristics for Row-and-Column Pruning of Deep Neural Networks

Approximation Algorithms and Simulated Annealing Heuristics ...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Ping-Han Tu Yu-Che Cheng Ding-Yong Hong Pangfeng Liu Jan-Jan Wu The Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Convolutional neural networks (CNNs) have achieved immense success in computer vision and other field of science. Despite the achievements, state-of-the-art CNN models have grown to gigantic sizes that demand a lot of computing power and memory resources. As a model compression technique, parameter pruning can lower the computation and memory a CNN model needs. Previous work has proposed a parameter pruning method, which compresses the neural networks by eliminating the rows and columns of the weight matrices. Removing rows and columns preserves the dense structure of the weight matrix, rather than the sparse structure produced by unstructured pruning. Although row-and-column pruning effectively reduces the model size, making the model as compact as possible without sacrificing accuracy remains a challenge. We prove that the row-and-column pruning problem is an NP-complete problem, and we propose two approximation algorithms that provide solutions within twice the cost of the optimal solution. We also show that the approximation ratio cannot be improved for these two algorithms. Finally, we propose two schemes based on simulated annealing to solve the row-and-column pruning problem. The two proposed schemes improve accuracy by 2.7% and 1.6%, respectively, on the ResNet-18 model with 93% sparsity using the Food-101 dataset.

关键词： distributed processing Accuracy Costs Computational modeling Heuristic algorithms Simulated annealing Approximation algorithms Convolutional neural networks Sparse matrices NP-complete problem

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VNEC: A Vectorized Non-Empty Column Format for SpMV on CPUs

VNEC: A Vectorized Non-Empty Column Format for SpMV on CPUs

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international symposium on parallel and distributed Processing (IPDPS)

作者： Luhan Wang Haipeng Jia Lei Xu Cunyang Wei Kun Li Xianmeng Jiang Yunquan Zhang Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Microsoft Research

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Sparse matrix-vector multiplication (SpMV) is a widely used computational kernel for many applications. The performance of existing vectorization-oriented and locality-optimized SpMV works is limited by increasing additional memory accesses to the output vector or using expensive gather operations. To address these issues, we present the Vectorized Non-Empty Column (VNEC), a novel SpMV storage format aiming to optimize locality and vectorization while alleviating the existing limitations. The VNEC chunks the sparse matrix by rows and removes the empty columns from each row block to improve input vector locality and reduce extra output vector memory access. It can also relieve the cost of expensive gather operations by padding zeros and employing less costly vector load instruction. Specifically, we design two variants of VNEC for different non-zero distributions and propose an effective heuristic selection model by introducing the Intra-Row Density (IRD) to evaluate which variant is suitable for optimizing a given matrix. Experimental results show that in a multicore environment, VNEC achieves up to 6.94× speedup (2.10× on average) against the standard MKL SpMV routine on the x86 CPU and up to 5.92× speedup (1.73× on average) over ArmPL on the ARM CPU. We emphasize that the VNEC format is practical for real-world iterative applications because of its low preprocessing overhead for format conversion.

关键词： distributed processing Multicore processing Memory management Graphics processing units Vectors Sparse matrices Iterative methods

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international Workshop on Quantum Classical Cooperative computing (QCCC 2022)

International Workshop on Quantum Classical Cooperative Comp...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Ang Li Qiang Guan

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The past five years have seen tremendous development in quantum computing technology, remarked by the demonstration of quantum supremacy. Although many quantum algorithms declare exponential speedups over their classical counterparts, today's quantum devices in the noisy-intermediate scale quantum (NISQ) era are very susceptible to environmental noise, internal interference, manufacturing imperfection, and technology limitation. Consequently, quantum algorithms that are more robust to noise, or can be effectively decomposed into small pieces for incremental or parallel quantum execution become promising. The purpose of this workshop is to explore innovative ways of quantum-classical cooperative computing (QCCC) to make quantum computing more effective and scalable in NISQ platforms. The workshop will focus heavily on how classical computing can improve NISQ device execution efficiency, scalability, or compensate for noise impact or technology deficiency, with particular emphasis on demonstrable approaches on existing NISQ platforms, such as IBM-Q, IonQ and Rigetti.

关键词： Conferences distributed processing Quantum algorithm Working environment noise Scalability Noise measurement Manufacturing

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An Efficient Algorithm for Clustering Sets

An Efficient Algorithm for Clustering Sets

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international Workshop/symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： Libero Nigro Franco Cicirelli Engineering Department of Informatics Modelling Electronics and Systems Science DIMES University of Calabria Rende Italy CNR National Research Council of Italy - Institute for High Performance Computing and Networking (ICAR) Rende Italy

This paper proposes an algorithm, named HWK-Sets, based on K-Means, suited for clustering data which are variable-sized sets of elementary items. Clustering sets is difficult because data objects do not have numerical attributes and it is not possible to use the classical Euclidean distance upon which K-Means is normally based. An adaptation of the Jaccard distance between sets is used, which exploits application-sensitive information. More in particular, the Hartigan and Wong variation of K-Means is adopted which uses medoids as cluster representatives, can work with several seeding methods and can favor the fast attainment of a careful solution. The paper introduces HWK-Sets which is implemented in Java by parallel streams. Then, the efficiency and accuracy of HWK-Sets are demonstrated by simulation experiments.

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