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Annual IEEE symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Johannes Menzel Christian Plessl Department of Computer Science Paderborn Center for Parallel Computing Paderborn University Paderborn Germany

ISBN: (数字)9798331502812

ISBN: (纸本)9798331502829

computing electron repulsion integrals (ERIs) is the major computational bottleneck of many quantum mechanical simulation methods, requiring trillions of ERI evaluations per time step. While the computation of independent ERIs is embarrassingly parallel, the efficient computation of individual ERIs on modern processor cores is difficult due to both an insufficient cache size for intermediates of the computation and irregular memory access patterns that are difficult to vectorize. In this paper, we present how our implementation on the AI Engine (AIE) architecture addresses both of these problems. First, we have defined a flexible graph structure, which we call an ERI-Engine, that can be implemented for all 231 canonical ERI quartets from {ss|ss} to {hh| hh} by distributing the computation over 2–14 AIEs. Second, for the larger quartets, we have devised a novel vectorization scheme that leverages the advanced floating-point unit of the AIEs, while also supporting vectorization of independent ERIs for the smaller quartets. Finally, ERI-Engines are horizontally and vertically stackable to fill the entire AIE array, and in particular, the vertically stacked ERI-Engines form a column that uses one or more time-shared channels to stream the results out of the AIE array, almost completely hiding the computational phases of individual ERI-Engines. In terms of absolute performance, we are competitive with recent high-performance implementations of ERI algorithms on FPGAs (SERI) and GPUs (LibintX), as well as well-established highly optimized CPU libraries (Libint, Libcint), while being the unequivocal leader in terms of energy efficiency.

关键词： Phased arrays Quantum computing Memory management Quantum mechanics Libraries Computational efficiency Artificial intelligence Engines Field programmable gate arrays Electrons

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Joint Location Deployment, Offloading and Resource Allocation in Multi-UAV Collaborative Edge computing Networks

Joint Location Deployment, Offloading and Resource Allocatio...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Mingyang Xu Xin Chen Libo Jiao Long Zhao Xiaoya Fan School of Computer Science Beijing Information Science & Technology University Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Unmanned aerial vehicles (UAVs) play a crucial role in mobile edge computing (MEC). On one hand, UAVs can serve as relay nodes, being flexibly deployed in various complex areas to provide network coverage services for users in remote regions. On the other hand, UAVs can carry edge servers, enabling them to approach terminal devices more conveniently and enhance the efficiency of task computation. However, in the face of a large number of computing tasks, the load balancing, network performance and computational resource management of the network are facing serious challenges due to the differences in task density caused by the irregular movement of ground users (GUs), as well as the limitations of the UAV’s computational capability and coverage. In order to address the above challenges, in this paper, We propose a multi-UAV collaborative MEC system. Our goal is to offload tasks as efficiently as possible with full and reasonable utilisation of computational resources, and to evenly distribute computational resources to meet the needs of varying levels of task intensity in a region through rational deployment of UAV locations and collaboration among UAVs. Specifically, in our proposed system framework, GUs can offload tasks to their associated UAVs and further decide whether or not to offload tasks to collaborative UAV or remote Base station (BS) based on the load of computational resources. We study the problem of location deployment of multiple UAVs for better offloading and resource allocation to tasks. Then, we model the task offloading and resource allocation process as a Markov decision process (MDP) focusing on minimising the average user cost of the system and propose a deep reinforcement learning (DRL)-based multi-uav collaborative computation offloading and resource allocation (DMCOA) algorithm. It has been shown through extensive simulation experiments that the algorithm can effectively reduce the average user cost of the MEC system.

关键词： Hands Costs Simulation Collaboration Transforms Autonomous aerial vehicles Load management Computational efficiency Resource management Servers

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Duo: Improving Data Sharing of Stateful Serverless Applications by Efficiently Caching Multi-Read Data

Duo: Improving Data Sharing of Stateful Serverless Applicati...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Zhuo Huang Hao Fan Chaoyi Cheng Song Wu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

A growing number of applications are moving to serverless architectures for high elasticity and fine-grained billing. For stateful applications, however, the use of serverless architectures is likely to lead to significant performance degradation, as frequent data sharing between different execution stages involves time-consuming remote storage access. Current platforms leverage memory cache to speed up remote access. However, conventional caching strategies show limited performance improvement. We experimentally find that the reason is that current strategies overlook the stage-dependent access patterns of stateful serverless applications, i.e., data that are read multiple times across stages (denoted as multi-read data) are wrongly evicted by data that are read only once (denoted as read-once data), causing a high cache miss ***, we propose a new caching strategy, Duo, whose design principle is to cache multi-read data as long as possible. Specifically, Duo contains a large cache list and a small cache list, which act as Leader list and Wingman list, respectively. Leader list ignores the data that is read for the first time to prevent itself from being polluted by massive read-once data at each stage. Wingman list inspects the data that are ignored or evicted by Leader list, and pre-fetches the data that will probably be read again based on the observation that multi-read data usually appear periodically in groups. Compared to the state-of-the-art works, Duo improves hit ratio by 1.1×-2.1× and reduces the data sharing overhead by 25%-62%.

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A Performance Portability Study Using Tensor Contraction Benchmarks

A Performance Portability Study Using Tensor Contraction Ben...

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

作者： M. Emin Ozturk Omid Asudeh Gerald Sabin P. Sadayappan Aravind Sukumaran-Rajam Kahlert School of Computing University of Utah Salt Lake City Utah RNET Technologies Dayton Ohio Meta Platforms Menlo Park California

Driven by the end of Moore’s law, heterogeneous architectures, particularly GPUs, are experiencing a surge in demand and utilization. While these platforms hold the potential for achieving high performance, their programming remains challenging and requires extensive hardware knowledge. This complexity is further exacerbated by the different proprietary languages utilized by various vendors. In this paper, we conduct a performance-portability study on two portable languages, SYCL and Kokkos. Specifically, we focus on the case study of tensor contractions and employ COGENT, a DSL compiler for tensor contractions, to generate CUDA code for the 48 different tensor contractions in the TCCG benchmark suite. We extend COGENT to produce Kokkos code, and use Hipify and SycloMatic, which are tools that convert CUDA code to HIP and SYCL. Our analysis involves a comparison of the performance of each framework on both Nvidia and AMD GPUs. Our experiments show that identically tiled tensor contraction kernels in Kokkos and SYCL can exhibit significant performance differences compared to the corresponding CUDA/HIP program, respectively on Nvidia/AMD GPUs. The main reason for the performance differences arise from differences in register usage and the management of register spills to thread-private stack memory, affecting overall degree of thread-level concurrency and the volume of data movement to/from GPU DRAM.

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SIMPLE, DETERMINISTIC, CONSTANT-ROUND COLORING IN CONGESTED CLIQUE AND MPC

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SIAM JOURNAL ON computing 2021年第5期50卷 1603-1626页

作者： Czumaj, Artur Davies, Peter Parter, Merav Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Univ Warwick Ctr Discrete Math & Its Applicat Coventry CV4 7AL W Midlands England IST Austria A-3400 Klosterneuburg Austria Weizmann Inst Sci IL-7610001 Rehovot Israel

We settle the complexity of the ( increment +1)-coloring and ( increment +1)-list coloring problems in the CONGESTED CLIQUE model by presenting a simple deterministic algorithm for both problems running in a constant number of rounds. This matches the complexity of the recent breakthrough randomized constant-round ( increment + 1)-list coloring algorithm due to Chang et al. [Proceedings of the 38th ACM symposium on Principles of distributed computing, 2019] and significantly improves upon the state-of-the-art O (log increment )-round deterministic ( increment + 1)-coloring bound of Parter [Proceedings of the 45th Annual international Colloquium on Automata, Languages and Programming]. A remarkable property of our algorithm is its simplicity. Whereas the state-of-the-art randomized algorithms for this problem are based on the quite involved local coloring algorithm of Chang, Li, and Pettie [Proceedings of the 50th Annual ACM SIGACT symposium on Theory of computing, 2018], our algorithm can be described in just a few lines. At a high level, it applies a careful derandomization of a recursive procedure which partitions the nodes and their respective palettes into separate bins. We show that after O (1) recursion steps, the remaining uncolored subgraph within each bin has linear size and thus can be solved locally by collecting it to a single node. This algorithm can also be implemented in the massively parallel computation (MPC) model provided that each machine has linear (in n, the number of nodes in the input graph) space. We also show an extension of our algorithm to the MPC regime, in which machines have sublinear space: we present the first deterministic ( increment + 1)-list coloring algorithm designed for sublinear-space MPC, which runs in O (log increment + log log n) rounds.

关键词： congested clique massively parallel computation coloring derandomization

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FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout

FedBIAD: Communication-Efficient and Accuracy-Guaranteed Fed...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Jingjing Xue 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

Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are usually bandwidth-constrained, and the uplink is much slower than the downlink in wireless networks, which causes a severe uplink communication bottleneck. A prominent direction to alleviate this problem is federated dropout, which drops fractional weights of local models. However, existing federated dropout studies focus on random or ordered dropout and lack theoretical support, resulting in unguaranteed performance. In this paper, we propose Federated learning with Bayesian Inference-based Adaptive Dropout (FedBIAD), which regards weight rows of local models as probability distributions and adaptively drops partial weight rows based on importance indicators correlated with the trend of local training loss. By applying FedBIAD, each client adaptively selects a high-quality dropping pattern with accurate approximations and only transmits parameters of non-dropped weight rows to mitigate uplink costs while improving accuracy. Theoretical analysis demonstrates that the convergence rate of the average generalization error of FedBIAD is minimax optimal up to a squared logarithmic factor. Extensive experiments on image classification and next-word prediction show that compared with status quo approaches, FedBIAD provides 2× uplink reduction with an accuracy increase of up to 2.41% even on non-Independent and Identically distributed (non-IID) data, which brings up to 72% decrease in training time.

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Adaptive Verifiable Coded computing: Towards Fast, Secure and Private distributed Machine Learning

Adaptive Verifiable Coded Computing: Towards Fast, Secure an...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Tingting Tang Ramy E. Ali Hanieh Hashemi Tynan Gangwani Salman Avestimehr Murali Annavaram Computer Science Department University of Southern California Los Angeles USA Electrical Engineering Department University of Southern California Los Angeles USA

Stragglers, Byzantine workers, and data privacy are the main bottlenecks in distributed cloud computing. Some prior works proposed coded computing strategies to jointly address all three challenges. They require either a large number of workers, a significant communication cost or a significant computational complexity to tolerate Byzantine workers. Much of the overhead in prior schemes comes from the fact that they tightly couple coding for all three problems into a single framework. In this paper, we propose Adaptive Verifiable Coded computing (AVCC) framework that decouples the Byzantine node detection challenge from the straggler tolerance. AVCC leverages coded computing just for handling stragglers and privacy, and then uses an orthogonal approach that leverages verifiable computing to mitigate Byzantine workers. Furthermore, AVCC dynamically adapts its coding scheme to trade-off straggler tolerance with Byzantine protection. We evaluate AVCC on a compute-intensive distributed logistic regression application. Our experiments show that AVCC achieves up to 4.2× speedup and up to 5.1% accuracy improvement over the state-of-the-art Lagrange coded computing approach (LCC). AVCC also speeds up the conventional uncoded implementation of distributed logistic regression by up to 7.6×, and improves the test accuracy by up to 12.1%.

关键词： Deep learning Privacy distributed processing Costs Neural networks Encoding Hardware

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A Comparative Study of Intersection-Based Triangle Counting Algorithms on GPUs

A Comparative Study of Intersection-Based Triangle Counting ...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Jiangbo Li Zichen Xu Minh Pham Yicheng Tu Qihe Zhou School of MCS Nanchang University Nanchang China Jiaxing Neofelis Technologies Co. Ltd. Jiaxing China Depart. of CSE University of South Florida Tampa FL USA Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Counting triangles in large graphs, being a crucial problem in graph computing, has attracted significant attention from research communities. There is a large body of work dedicated to algorithmic design and efficient implementation on parallel platforms such as GPUs. Among them, the intersection-based triangle counting algorithm is found to be the most efficient approach and a few GPU implementations have been proposed following this algorithm. However, there remains a gap in understanding how these algorithms perform when confronted with diverse real-world graph datasets. It is a well-established fact that the performance of GPU code is heavily influenced by data characteristics, including graph size and node degree, often leading to issues such as workload imbalances and inefficient memory access. The goal of this study is to systematically evaluate the performance and analyze the behavior of eight recently published intersection-based triangle counting implementations. For that, we developed a unified testing framework that facilitates fast performance assessment of any triangle counting algorithm. Our experiments show that the TRUST algorithm outperforms competitors in most cases. To our surprise, the Polak algorithm, with a simple design, has displayed commendable performance across the board. Notably, it even surpasses the TRUST algorithm when processing small datasets. We conducted an in-depth analysis on the resource consumption patterns of these implementations in relation to their performance and identified key factors that contributed to their behaviors. Based on insights gained from such analysis, we proposed a novel algorithm named GroupTC that delivers outstanding performance under all types of datasets.

关键词： distributed processing Codes Graphics processing units Testing

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29th Reconfigurable Architectures Workshop (RAW 2022)

29th Reconfigurable Architectures Workshop (RAW 2022)

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

作者： Jü rgen Becker Lana Josipović Viktor K. Prasanna Marco Santambrogio Ramachandran Vaidyanathan

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

This book presents the proceedings of the 29th Reconfigurable Architectures Workshop (RAW 2022) held in Lyon in May 2022. RAW 2022 is associated with the 36th Annual international parallel & distributed Processing symposium (IPDPS 2022) and is sponsored by the IEEE Computer Society's Technical Committee on parallel Processing. The workshop is one of the major meetings for researchers to present ideas, results, and ongoing research on both theoretical and practical advances in reconfigurable computing.

关键词： Conferences Reconfigurable architectures distributed processing parallel processing Industries Hardware Field programmable gate arrays

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Towards a GraphBLAS Implementation for Go

Towards a GraphBLAS Implementation for Go

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

作者： Pascal Costanza Ibrahim Hurt Timothy G. Mattson Intel Extreme Scale Computing Group Brussels Belgium Intel Extreme Scale Computing Group Hillsboro OR USA Intel Parallel Computing Lab Ocean Park WA USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The GraphBLAS are building blocks for constructing graph algorithms as linear algebra. They are defined mathematically with the goal that they would eventually map onto a variety of programming languages. Today they exist in C, C++, Python, MATLAB®, and Julia. In this paper, we describe the GraphBLAS for the Go programming language. A particularly interesting aspect of this work is that using the concurrency features of the Go language, we aim to build a runtime system that uses the GraphBLAS nonblocking mode by default.

关键词： Concurrent computing distributed processing Runtime Conferences Prototypes Linear algebra Licenses

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