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检索条件"任意字段=2nd International Symposium on Parallel and Distributed Processing and Applications"

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Qualitative QoS-aware Scheduling of Moldable parallel Jobs on HPC Clusters 22

Qualitative QoS-aware Scheduling of Moldable Parallel Jobs o...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Hou, Zhengxiong Liu, Yubing Shen, Hong Liu, Jiyuan Gu, Jianhua Northwestern Polytechnical University School of Computer Science Xi'an China

ISBN: (纸本)9798331509712

In service oriented high-performance computing (HPC) clusters, end users have various Quality of Service (QoS) requirements. Most of the existing research work focuses on quantitative QoS requirements, such as deadlines, for rigid jobs. While, in many cases, it is more convenient for users to qualitatively state QoS requirements (such as performance-sensitive) at the submission of their jobs. Almost all kinds of QoS requirements will be greatly impacted by job scheduling, which determine the degree of job parallelism, execution time and waiting time, etc. Most modern parallel applications are moldable in the sense that they can choose a resource allocation before execution. Traditional sequential job scheduling mechanism with fixed resource allocation appears to be an obstacle to improve QoS for end users. To address this issue, we propose a novel qualitative QoS-aware sub-queue simultaneous scheduling method for moldable parallel jobs (with variable resource allocation) on HPC Clusters. We first define the qualitative QoS models for end users, then present our sub-queue simultaneous scheduling method, including job sequencing and resource allocation algorithms for a set of moldable parallel jobs on multi-core clusters. Our method can efficiently sequence queuing jobs and allocate appropriate resources for simultaneously running some performance-sensitive jobs in a sub-queue rather than running them one by one. Experimental results demonstrate the effectiveness of our method to improving QoS for end users. © 2024 IEEE.

关键词： Quality of service

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LBCB: One-sided RDMA-based distributed B+ tree Index with Low Bandwidth Consumption 22

LBCB: One-sided RDMA-based Distributed B+ tree Index with Lo...

引用

22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Liu, Jibo Xi, Rui Cao, Qinzhen Nie, Xiaowen Hou, Zhuohan University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331509712

Disaggregated memory architecture segregates computing and memory resources into distinct pools interconnected by a high-speed one-sided RDMA (Remote Direct Memory Access) network, enhancing memory utilization, reducing costs, and facilitating elastic scaling of computing and memory resources. However, optimizing index structures to maximize the benefits of this framework poses significant challenges. Despite progress in B+ tree indexes for disaggregated memory systems, they still suffer from severe write and read amplification issues, which impede latency and throughput *** this paper, we propose LBCB, a B+ tree index for disaggregated memory, which significantly reduces the bandwidth consumption of index operations. First, LBCB introduces an RDMA friendly B+ tree leaf node structure, which improves concurrency while reducing the bandwidth consumption. Second, LBCB designs a logical fusion FAA lock that synchronizes more information with a single RDMA communication, significantly reducing the number of RDMA network round trips. Finally, LBCB optimistically compresses the critical path of index operations, further optimizing latency and throughput. Evaluation results show that compared with the state-of-the-art distributed B+ tree index, the write performance of LBCB is improved by 1.42 times. © 2024 IEEE.

关键词： Memory architecture

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Collecting Clustering Coefficient of distributed Graph Data with Shuffled Differential Privacy 22

Collecting Clustering Coefficient of Distributed Graph Data ...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Ding, Hongfa Fu, Peiwang Luo, Yingxuan Jiang, Heling Liu, Hai Guizhou University of Fiance and Economics School of Information Guiyang China Co. Ltd Guiyang China Jiangxi Datang International Fuzhou Power Generation Co. Ltd Fuzhou China

ISBN: (纸本)9798331509712

The intricate properties and relevance of graph data make it difficult to collect graph statistics privately via differential privacy (DP). Traditional centralized or local DP on graph data, face challenges like third-party threats and low data utility when collecting the clustering coefficient. In this regard, we introduce GCC-SDP, a scheme for collecting distributed Graph Clustering Coefficient with Shuffled DP (SDP). GCC-SDP gathers the local wedge lists of all edges and adjacency bit vectors through SDP and random response for calculating the noisy local triangle counts. It then collects the local degree values of all users by using Laplace mechanism, followed by estimating the global clustering coefficient of the global graph data by data collector. We provide specific steps of GCC-SDP and demonstrate through theoretical analysis that GCC-SDP conforms to various DPs, with unbiased results. Empirical experiments show that GCC-SDP performs better than existing local DP-based techniques across most accuracy metrics. © 2024 IEEE.

关键词： Data collection

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HM2: Efficient Host Memory Management for RDMA-Enabled distributed Systems 23

HM2: Efficient Host Memory Management for RDMA-Enabled Distr...

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32nd international symposium on High-Performance parallel and distributed Computing (HPDC) part of the ACM Federated Computing Research Conference (FCRC)

作者： Tang, Chen Chu, Zhaole Jin, Peiquan Luo, Yongping Guo, Kuankuan Univ Sci Technol China Hefei Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798400701559

Remote direct memory access (RDMA) supports zero-copy networking by transferring data from clients directly to host memory, eliminating the need to copy data between clients' memory and the data buffers in the hosting server. However, the hosting server must design efficient memory management schemes to handle incoming clients' data. In this paper, we propose a high-performance host memory management scheme called HM2 for RDMA-enabled distributed systems. We present a new buffer structure for incoming data from clients. In addition, we propose efficient data processing methods to reduce network transfers between clients and servers. We conducted a preliminary experiment to evaluate HM2, and the results showed HM2 achieved higher throughput than existing schemes, including L5 and FaRM.

关键词： RDMA memory management data buffer

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FHNTT: A flexible Number Theoretic Transform design based on hybrid-radix butterfly 22

FHNTT: A flexible Number Theoretic Transform design based on...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Li, Li Li, Rengang Zhao, Yaqian Li, Ruyang Su, Zhiyuan Li, Xuelei Ieit Systems Co. Ltd Jinan China Electronic Information Industry Co. Ltd Beijing China

ISBN: (纸本)9798331509712

Emerging technologies, such as cloud computing and artificial intelligence, significantly arouse concern about data security and privacy. Homomorphic encryption (HE) is a promising invention, which enables computation on encrypted data without decrypting it so as to ensure data security and privacy. Nevertheless, computation within homomorphic encryption involves time-consuming operations, e.g., Number Theoretic Transform (NTT). The tremendous computation overhead is the critical obstacle in deploying HE applications widely. Besides, in order to meet the performance and security requirements of different applications, it is pivotal to design parametric NTT architecture. In this paper, we propose a flexible and parametric NTT accelerating scheme based on hybrid-radix butterfly, named FHNTT. Specifically, we construct high radix butterfly units and divide the computation of them into several stages such that every stage can be performed pipelined. The number of required twiddle factors declines with the increase of radix value. In addition, we adopt address offset strategy to reduce memory consumption. We implement FHNTT on FPGA due to its fine-grained parallel computing capabilities and customized architecture. Empirical results show that FHNTT has an improved performance compared with other NTT architectures and supports a wide range of parameters. Concretely, FHNTT achieves up to 1.99 × to 2.78 × improvement in latency over other FPGA implementations and the memory utilization rate is up to 94%. Moreover, the flexibility makes FHNTT applicable to multiple use cases. © 2024 IEEE.

关键词： FPGA Acceleration Number Theoretic Transform parallel Computing Parametric architecture

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Alioth: A Machine Learning Based Interference -Aware Performance Monitor for Multi -Tenancy applications in Public Cloud 37

Alioth: A Machine Learning Based Interference -Aware Perform...

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37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Shi, Tianyao Yang, Yingxuan Cheng, Yunlong Gao, Xiaofeng Fang, Zhen Yang, Yongqiang Shanghai Jiao Tong Univ Shanghai Peoples R China Huawei Technol Cloud BU Beijing Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9798350337662

Multi-tenancy in public clouds may lead to colocation interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers want to know when such events happen and how serious the degradation is, to perform interference-aware migrations and alleviate the problem. However, virtual machines (VM) in Infrastructure-as-a-Service public clouds are black boxes to providers, where application-level performance information cannot be acquired. This makes performance monitoring intensely challenging as cloud providers can only rely on low-level metrics such as CPU usage and hardware counters. We propose a novel machine learning framework, Alioth, to monitor the performance degradation of cloud applications. To feed the data-hungry models, we first elaborate interference generators and conduct comprehensive co-location experiments on a testbed to build Alioth-dataset which reflects the complexity and dynamicity in real-world scenarios. Then we construct Alioth by (1) augmenting features via recovering low-level metrics under no interference using denoising auto-encoders, (2) devising a transfer learning model based on domain adaptation neural network to make models generalize on test cases unseen in offline training, and (3) developing a SHAP explainer to automate feature selection and enhance model interpretability. Experiments show that Alioth achieves an average mean absolute error of 5.29% offline and 10.8% when testing on applications unseen in the training stage, outperforming the baseline methods. Alioth is also robust in signaling quality-of-service violation under dynamicity. Finally, we demonstrate a possible application of Alioth's interpretability, providing insights to benefit the decisionmaking of cloud operators. The dataset and code of Alioth have been released on GitHub.

关键词： QoS Interference Multi-Tenancy Public Cloud Machine Learning

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Enabling Multi-threading in Heterogeneous Quantum-Classical Programming Models

Enabling Multi-threading in Heterogeneous Quantum-Classical ...

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37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Hayashi, Akihiro Adams, Austin Young, Jeffrey McCaskey, Alexander Dumitrescu, Eugene Sarkar, Vivek Conte, Thomas M. Georgia Inst Technol Atlanta GA 30332 USA NVIDIA Corp Santa Clara CA USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9798350311990

While quantum computers enable significant performance improvements for certain classes of applications, building a well-defined programming model has been a pressing issue. In this paper, we address some of the key limitations to realizing a generic heterogeneous parallel programming model for quantumclassical heterogeneous platforms. We discuss our experience in enabling user-level multi-threading in QCOR [1] as well as challenges that need to be addressed for programming future quantum-classical systems. Specifically, we discuss our design and implementation of introducing C++-based parallel constructs to enable 1) parallel execution of a quantum kernel with std::thread and 2) asynchronous execution with std::async. To do so, we provide a detailed overview of the current implementation of the QCOR programming model and runtime, and discuss how we add 1) thread-safety to some of its user-facing API routines, and 2) increase parallelism in QCOR by removing data races that inhibit multi-threading so as to better utilize available computing resources. We also present preliminary performance results with the Quantum++ [2] back end on a single-node Ryzen9 3900X machine that has 12 physical cores (24 hardware threads) with 128GB of RAM. The results show that running two Bell kernels with 12 threads per kernel in parallel outperforms running the kernels one after the other each with 24 threads (1.63x improvement). In addition, we observe the same trend when running two Shor's algorthm kernels in parallel (1.22x faster than executing the kernels one after the other). Furthermore, the parallel version is better in terms of strong scalability. We believe that our design, implementation, and results will open up an opportunity not only for 1) enabling quicker prototyping of parallel-aware quantum-classical algorithms on quantum circuit simulators in the short-term, but also for 2) realizing a generic parallel programming model for quantumclassical heterogeneous platforms in

关键词： Quantum-Classical Programming Models parallel Programming Models QCOR Heterogeneous Computing

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Application Analysis and Development Strategy of Cloud Computing Technology in Computer Data processing 2

Application Analysis and Development Strategy of Cloud Compu...

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2nd IEEE international Conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Lina, Liu Changchun Humanities and Sciences College China

ISBN: (纸本)9798350347456

In the past, data processing was completed on a computer or a server. This is because the resources available at that time were limited. However, with the progress of technology, it is possible to use multiple computers and servers to process large amounts of data. This is where cloud computing comes into play. Cloud computing allows users to access data from anywhere at any time using an Internet connection. It also reduces the cost of enterprises because they do not have to buy additional hardware or software for each user. Aiming at the problems of single scale of traditional computer data processing and poor degree of adaptive parallel computing, this paper proposes a multi-source information accelerated data processing method of cloud computing technology, analyzes the application of cloud computing technology in computer data processing, and adopts the dynamic optimization technology of cloud computing speed to build a dynamic adjustment model of multi-source information data flow application of cloud computing, so as to reduce the energy cost of cloud computing, Combined with the experience of data processing users, the model can support multi-user computing entity sharing in the cloud to improve the running efficiency of data flow applications and reduce the energy cost of computer data processing. This article first outlines the basic meaning of cloud computing technology, starting with providing conditions for data transmission and sharing, and highlighting the application value of cloud computing technology. It analyzes the trend of cloud computing technology, and combines the modeling of cloud computing technology in computer data processing to propose application countermeasures for cloud computing technology in computer data processing. © 2023 IEEE.

关键词： Cloud computing

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A Communication-efficient Collaborative Task Offloading Method Based on distributed Computing Cloud 22

A Communication-efficient Collaborative Task Offloading Meth...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Zhang, Ke Zong, Ruixing Song, Yingjie Zhou, Yong Xiao, Xiong Hunan University Changsha China

ISBN: (纸本)9798331509712

An interconnected distributed computing cloud, comprising network-linked computational nodes, facilitates the allocation of computational resources across diverse locations. The strategic offloading of computationally demanding tasks from resource-constrained nodes to proximate nodes endowed with ample resources can enhance the resource utilization within the computing cloud. Given the interdependencies among segmented tasks, improper task partitioning may engender supplementary overheads. Furthermore, the process of task offloading introduces additional communication overhead. Consequently, the judicious division of task components and the selection of appropriate offloading destinations assume paramount significance in mitigating collaborative computing complexity and communication overhead. To address these challenges, we propose a communication-efficient collaborative task offloading methodology, which entails the segmentation and offloading of computation-intensive tasks from the source node to one or more high-performance nodes to fully leverage device resources. Additionally, we devise a subtask placement strategy predicated on minimizing communication distances, whereby multiple subtasks are allocated to distinct nodes based on the calculated communication latencies between the source node and various computing nodes. Experiments demonstrate that our task offloading method exhibits the lowest overall loss. © 2024 IEEE.

关键词： distributed cloud

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P-AES: Advanced Encryption Standard parallel Optimization on MGPUSim 22

P-AES: Advanced Encryption Standard Parallel Optimization on...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Qin, Jiawei Wang, Yaobin Li, Ling Yang, Shuang Zhang, Xiaorong Southwest University of Science and Technology School of Computer Science and Technology Mianyang621010 China

ISBN: (纸本)9798331509712

Advanced Encryption Standard (AES), as one of the most popular encryption algorithms, has been widely studied on single GPU and CPU. However, the research on multi-GPU platforms is not deep enough, and with the rapid increase in data size, it is difficult for single-GPU platforms to meet the demand for high-performance computing. To solve this problem, this paper presents a novel AES parallelization study "P-AES"on MGPUSim, including its parallel execution mechanism as well as architectural design. In addition, we modify the instruction set of MGPUSim along with thread optimization to save memory overhead, increase data access speed, and improve system performance by loading S-boxes and key extension arrays from global memory to shared memory. The experimental results show that: (1) P-AES performs well on MGPUSim, achieving an average speedup of 1.5×-1.7× compared to the AES implementation using CUDA kernels, and a speedup of 2.75× compared to M-AES (the unoptimized AES on MGPUSim). (2) By conducting encryption experiments on plaintext data of varying sizes, we found that P-AES exhibits high stability and throughput. Compared to the modern sliced AES, the P-AES algorithm achieves a throughput of up to 812 Gbps, which is 1.3 times that of their implementation and 2.74 times that of M-AES. © 2024 IEEE.

关键词： Computer graphics equipment

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