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Fuzzy c-Lines for Vertically distributed Database with Missing Values

Fuzzy c-Lines for Vertically Distributed Database with Missi...

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international symposium on Soft Computing and Intelligent systems (SCIS)

作者： Kohei Kunisawa Katsuhiro Honda Seiki Ubukata Akira Notsu Graduate School of Engineering Osaka Prefecture University Sakai Osaka Japan Graduate School of Informatics Osaka Metropolitan University Sakai Osaka Japan Graduate School of Sustainable System Sciences Osaka Metropolitan University Sakai Osaka Japan

Privacy preserving data clustering is a useful method for extracting intrinsic cluster structures from distributed databases keeping personal privacy. In a previous research, a model of performing Fuzzy c-Lines clustering was proposed, where a privacy preserving scheme of k-means-type model was adopted with cryptographic calculation. This paper further improves the model for handling incomplete data ignoring the influences of missing values. The element-wise clustering criterion enables to derive local principal component vectors in each data sources by considering minimization of low-rank approximation of observed elements only. Then, fuzzy memberships of each object are calculated in a collaborative manner among organizations, where partial distances between objects and prototypes are derived with cryptographic framework so that intra-organization information is kept secret. The characteristic features of the proposed method are demonstrated through numerical experiments.

关键词： Privacy Data privacy Shape Soft sensors distributed databases Clustering algorithms Prototypes

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An evaluation of Cassandra NoSQL database on a low-power cluster 33

An evaluation of Cassandra NoSQL database on a low-power clu...

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33rd IEEE international symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： da Silva, Lucas Ferreira Lima, Joao V. F. Univ Fed Santa Maria Grad Program Comp Sci Santa Maria RS Brazil

ISBN: (纸本)9781665417303

The constant growth of social media, unconventional web technologies, mobile applications, and Internet of Things (IoT) devices, create challenges for cloud data systems in order to support huge datasets and very high request rates. NoSQL distributed databases such as Cassandra have been used for unstructured data storage and to increase horizontal scalability and high availability. In this paper, we evaluated Cassandra on a low-power low-cost cluster of commodity Single Board Computers (SBC). The cluster has 15 Raspberry Pi v3 nodes with Docker Swarm orchestration tool for Cassandra service deployment and ingress load balancing over SBCs. Experimental results demonstrated that hardware limitations impacted workload throughput, but read and write latencies were comparable to results from other works on high-end or virtualized platforms. Despite the observed limitations, the results show that a low-cost SBC cluster can support cloud serving goals such as scale-out, elasticity and high availability.

关键词： NoSQL databases Raspberry Pi low-power Single Board Computers Docker Swarm Big Data Cassandra

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Improving INT Inference Resilience of CNNs by Vulnerability-Aware Mixed-Precision Quantization

Improving INT Inference Resilience of CNNs by Vulnerability-...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Xiaohui Wei Pei Liu Chenyang Wang Zeyu Guan Qi Wu Hengshan Yue College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The application of INT quantization allows CNN model size and computation to be reduced exponentially, making extreme quantization a hot research topic. However, firstly the decrease in bitwidth increases the probability of error in critical bits, which is unacceptable for safety-critical scenarios that require high resilience of CNNs. Secondly, extreme quantization often requires retraining with high overhead. Considering the higher tolerance of errors in high-bitwidth INTs and the local resilience differences of CNNs, we use mixed precision quantization (MPQ) to trade off resilience and compression effects. Existing MPQ methods mostly focus on compression as the first goal and lack indicators of resilience or vulnerability. Based on a large number of vulnerability analyses, we propose to use the maximum bit average offset and the approximation parameter average quantization error to measure layer vulnerability. And we measure the vulnerability of the entire CNN in terms of the accumulation of layer offsets. Accordingly, we design a complete vulnerability-aware MPQ (VulnA-MPQ) framework. From the perspective of minimizing vulnerability and compression ratio, VulnA-MPQ is performed on multiple CNNs using genetic algorithm (GA) with low overhead. The CNNs only need to be fine-tuned to restore accuracy after VulnA-MPQ. Experiments demonstrate that VulnA-MPQ not only maintains accuracy and compression ratio better but also improves CNNs fault tolerance. Within the 2% accuracy threshold, the random error data tolerated by VGG16 weights and activations are improved by 33%. The number of random errors tolerated by MobileNetV3-large weights is improved by 8.87 times, and that of activations is improved by 4.64 times.

关键词： Fault tolerance distributed processing Quantization (signal) Accuracy Computational modeling Fault tolerant systems Measurement uncertainty Hardware Resilience Genetic algorithms

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Efficient distributed Secure Memory with Migratable Merkle Tree

Efficient Distributed Secure Memory with Migratable Merkle T...

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IEEE symposium on High-Performance Computer Architecture

作者： Erhu Feng Dong Du Yubin Xia Haibo Chen Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory

Hardware-assisted enclaves with memory encryption have been widely adopted in the prevailing architectures, e.g., Intel SGX/TDX, AMD SEV, ARM CCA, etc. However, existing enclave designs fall short in supporting efficient cooperation among cross-node enclaves (i.e., multi-machines) because the range of hardware memory protection is within a single node. A naive approach is to leverage cryptography at the application level and transfer data between nodes through secure channels (e.g., SSL). However, it incurs orders of magnitude costs due to expensive encryption/decryption, especially for distributed applications with large data transfer, e.g., MapReduce and graph computing. A secure and efficient mechanism of distributed secure memory is necessary but still *** paper proposes Migratable Merkle Tree (MMT), a design enabling efficient distributed secure memory to support distributed confidential computing. MMT sets up an integrity forest for distributed memory on multiple nodes. It allows an enclave to securely delegate an MMT closure, which contains both data and metadata of a subtree, to a remote enclave. By reusing the memory encryption mechanisms of existing enclaves, our design achieves secure data transfer without software re-encryption. We have implemented a prototype of MMT and a trusted firmware for management, and further applied MMT to real-world distributed applications. The evaluation results show that compared with existing systems using the AES-NI instruction, MMT can achieve up to 13x speedup on data transferring, and gain 12%~58% improvement on the end-to-end performance of MapReduce and PageRank.

关键词： Protocols Program processors Memory management Prototypes Metadata Data transfer Software

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DF-GAS: a distributed FPGA-as-a-Service Architecture towards Billion-Scale Graph-based Approximate Nearest Neighbor Search

DF-GAS: a Distributed FPGA-as-a-Service Architecture towards...

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IEEE/ACM international symposium on Microarchitecture (MICRO)

作者： Shulin Zeng Zhenhua Zhu Jun Liu Haoyu Zhang Guohao Dai Zixuan Zhou Shuangchen Li Xuefei Ning Yuan Xie Huazhong Yang Yu Wang Department of Electronic Engineering Tsinghua University Beijing China Qingyuan Research Institute Shanghai Jiao Tong University Shanghai China DAMO Academy Alibaba Group Sunnyvale USA ACCESS HKUST Hongkong China DAMO Academy Alibaba Group Sunnyvale USA

Embedding retrieval is a crucial task for recommendation systems. Graph-based approximate nearest neighbor search (GANNS) is the most commonly used method for retrieval, and achieves the best performance on billion-scale datasets. Unfortunately, the existing CPU- and GPU-based GANNS systems are difficult to optimize the throughput under the latency constraints on billion-scale datasets, due to the underutilized local memory bandwidth (5-45%) and the expensive remote data access overhead (∼85% of the total latency). In this paper, we first introduce a practically ideal GANNS architecture for billion-scale datasets, which facilitates a detailed analysis of the challenges and characteristics of distributed GANNS systems. Then, at the architecture level, we propose DF-GAS, a distributed FPGA-as-a-Service (FPaaS) architecture for accelerating billion-scale Graph-based Approximate nearest neighbor Search. DF-GAS uses a feature-packing memory access engine and a data prefetching and delayed processing scheme to increase local memory bandwidth by 36-42% and reduce remote data access overhead by 76.2%, respectively. At the system level, we exploit the "full-graph + sub-graph" hybrid parallel search scheme on distributed FPaaS system. It achieves million-level query-per-second with sub-millisecond latency on billion-scale GANNS for the first time. Extensive evaluations on million-scale and billion-scale datasets show that DF-GAS achieves an average of 55.4×, 32.2×, 5.4×, and 4.4× better latency-bounded throughput than CPUs, GPUs, and two state-of-the-art ANNS architectures, i.e., ANNA [23] and Vstore [27], *** CONCEPTS• Computer systems organization → Cloud computing.

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Hybrid Workload Scheduling on HPC systems

Hybrid Workload Scheduling on HPC Systems

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international symposium on parallel and distributed Processing (IPDPS)

作者： Yuping Fan Zhiling Lan Paul Rich William Allcock Michael E. Papka Illinois Institute of Technology Chicago IL Argonne National Laboratory Lemont IL Argonne National Laboratory Northern Illinois University

Traditionally, on-demand, rigid, and malleable applications have been scheduled and executed on separate systems. The ever-growing workload demands and rapidly developing HPC infrastructure trigger the interest of converging these applications on a single HPC system. Although allocating the hybrid workloads within one system could potentially improve system efficiency, it is difficult to balance the tradeoff between the responsiveness of on-demand requests, incentive for malleable jobs, and the performance of rigid applications. In this study, we present several scheduling mechanisms to address the issues involved in co-scheduling on-demand, rigid, and malleable jobs on a single HPC system. We extensively evaluate and compare their performance under various configurations and workloads. Our experimental results show that our proposed mechanisms are capable of serving on-demand workloads with minimal delay, offering incentives for declaring malleability, and improving system performance.

关键词： distributed processing System performance Production Delays

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A Novel DA-Based parallel Architecture for Inner-Product of Variable Vectors

A Novel DA-Based Parallel Architecture for Inner-Product of ...

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IEEE international symposium on Circuits and systems (ISCAS)

作者： Anil Kali Samrat L. Sabat Pramod K. Meher CASEST University of Hyderabad Hyderabad India Department of Computer Science & Engineering C. V. Raman Global University Bhubaneswar India

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Computation of the inner products is frequently used in machine learning (ML) algorithms apart from signal processing and communication applications. distributed arithmetic (DA) has been frequently employed for area-time efficient inner-product implementations. In conventional DA-based architectures, one of the vectors is constant and known a priori. Hence, the traditional DA architectures are not suitable when both vectors are variable. However, computing the inner product of a pair of variable vectors is frequently used for matrix multiplication of various forms and convolutional neural networks. In this paper, we present a novel DA-based architecture for computing the inner product of variable vectors. To derive the proposed architecture, the inner product of any given length is decomposed into a set of short-length inner products, such that the inner product could be computed by successive accumulation of the results of short-length inner products. We have designed a DA-based architecture for the computation of the short-length inner-product of variable vectors and used that in successive clock cycles to compute the whole inner-product by successive accumulation. The post-layout synthesis results using Cadence Innovus with a GPDK 90nm technology library show that the proposed DA-based parallel architecture offers significant advantages in area-delay product and energy consumption over the bit-serial DA architecture.

关键词： Energy consumption Machine learning algorithms Architecture Signal processing algorithms Machine learning Signal processing Vectors

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HACCS: Heterogeneity-Aware Clustered Client Selection for Accelerated Federated Learning

HACCS: Heterogeneity-Aware Clustered Client Selection for Ac...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Joel Wolfrath Nikhil Sreekumar Dhruv Kumar Yuanli Wang Abhishek Chandra Department of Computer Science and Engineering University of Minnesota Minneapolis USA

Federated Learning is a machine learning paradigm where a global model is trained in-situ across a large number of distributed edge devices. While this technique avoids the cost of transferring data to a central location and achieves a strong degree of privacy, it presents additional challenges due to the heterogeneous hardware resources available for training. Furthermore, data is not independent and identically distributed (IID) across all edge devices, resulting in statistical heterogeneity across devices. Due to these constraints, client selection strategies play an important role for timely convergence during model training. Existing strategies ensure that each individual device is included, at least periodically, in the training process. In this work, we propose HACCS, a Heterogeneity-Aware Clustered Client Selection system that identifies and exploits the statistical heterogeneity by representing all distinguishable data distributions instead of individual devices in the training process. HACCS is robust to individual device dropout, provided other devices in the system have similar data distributions. We propose privacy-preserving methods for estimating these client distributions and clustering them. We also propose strategies for leveraging these clusters to make scheduling decisions in a federated learning system. Our evaluation on real-world datasets suggests that our framework can provide 18% −38% reduction in time to convergence compared to the state of the art without any compromise in accuracy.

关键词： Training distributed processing Data privacy Costs distributed databases Machine learning Collaborative work

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When Moore Just Isn't Enough: Scaling ML in the Datacenter

When Moore Just Isn't Enough: Scaling ML in the Datacenter

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

作者： David Kanter MLCommons

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

As the industry drives towards more capable ML, workloads are rapidly evolving and the need for performance is nearly unlimited. Performance has vastly outstripped the pace of Moore's Law as measured by MLPerf™ through software/hardware co-design. This talk will discuss challenges in benchmarking ML Training and explore the design space and also identify opportunities in future ML systems.

关键词： Space exploration Industries distributed processing Conferences Benchmark testing Training Semiconductor device modeling

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WeTune: Automatic Discovery and Verification of Query Rewrite Rules 22

WeTune: Automatic Discovery and Verification of Query Rewrit...

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international Conference on Management of Data (SIGMOD)

作者： Wang, Zhaoguo Zhou, Zhou Yang, Yicun Ding, Haoran Hu, Gansen Ding, Ding Tang, Chuzhe Chen, Haibo Li, Jinyang Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Minist Educ Engn Res Ctr Domain Specif Operating Syst Beijing Peoples R China NYU Dept Comp Sci New York NY 10003 USA

ISBN: (纸本)9781450392495

Query rewriting transforms a relational database query into an equivalent but more efficient one, which is crucial for the performance of database-backed applications. Such rewriting relies on pre-specified rewrite rules. In existing systems, these rewrite rules are discovered through manual insights and accumulate slowly over the years. In this paper, we present WETUNE, a rule generator that automatically discovers new rewrite rules. Inspired by compiler super-optimization, WETUNE enumerates all valid logical query plans up to a certain size and tries to discover equivalent plans that could potentially lead to more efficient rewrites. The core challenge is to determine which set of conditions (aka constraints) allows one to prove the equivalence between a pair of query plans. We address this challenge by enumerating combinations of "interesting" constraints that relate tables and their attributes between each pair of queries. We also propose a new SMT-based verifier to verify the equivalence of a query pair under different enumerated constraints. To evaluate the usefulness of rewrite rules discovered by WETUNE, we apply them on the SQL queries collected from the 20 most popular open-source web applications on GitHub. WETUNE successfully optimizes 247 queries that existing databases cannot optimize, resulting in substantial performance improvements.

关键词： Query Rewriting Rewrite Rule Discovery SQL Solver

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