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A New NVM Storage System with high throughput Device Driver 20

A New NVM Storage System with High Throughput Device Driver

引用

20th IEEE Int symposium on Parallel and Distributed Processing with Applicat / 15th IEEE Int Conf on Social Comp and Networking / 12th IEEE Int Conf on Big Data and Cloud Comp / 12th IEEE Int Conf on Sustainable Comp and Commun

作者： Cai, Tai Da, Jianfei Niu, Dejiao Li, Lei Shi, Jianchong Li, Ranzhuo Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Jiangsu Peoples R China Natl Univ Singapore Sch Sci Dept Stat & Data Sci Singapore Singapore

ISBN: (纸本)9781665464970

NVM device offers an effective way to solve the storage wall of the computer system, but its high read/write speed poses a significant challenge to the existing I/O software stack in Linux. this paper designs an effective BIO management strategy for NVM device driver and builds a high throughput NVM storage system to improve the concurrency and reduce BIO management overhead. First, a two-phase dynamic BIO management strategy module is proposed to separate BIO reception and execution, thereby improving the concurrency of BIO management in the NVM device driver. A lightweight BIO migration strategy is then designed to reduce the access number to NVM devices and improve the throughput of NVM device driver. Finally, based on Intel's open-source NVM device driver named PMEM, a high throughput NVM device driver named SVTNVM is implemented to build the new NVM storage system prototype. Some tests and analysis were carried out by using Filebench and Fio to simulate application workloads for evaluation. the results show that SVTNVM can improve IOPS by 29.3% and I/O bandwidth by 23.3% compared with current NVM storage system with PMEM.

关键词： NVM storage system NVM device driver Concurrency high throughput

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Hierarchical Fault-Tolerant NoC architecture for Reliable Communication

Hierarchical Fault-Tolerant NoC Architecture for Reliable Co...

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IEEE International symposium on Embedded Multicore Socs (MCSoC)

作者： Kasem Khalil Tamador Mohaidat Ahmed Sherif Magdy Bayoumi Department of Electrical and Computer Engineering University of Mississippi Mississippi USA School of Computing Sciences and Computer Engineering University of Southern Mississippi MS USA Department of Electrical and Computer Engineering University of Louisiana Lafayette LA USA

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

As multi-core systems continue to grow in complexity, Network-on-Chip (NoC) architectures have emerged as a scalable and efficient solution for managing on-chip communication. However, ensuring reliable communication in the presence of router faults remains a critical challenge, particularly for applications in safety-critical systems where failure can lead to catastrophic consequences. this paper proposes a novel fault-tolerant NoC architecture designed to improve reliability in multi-core systems. the proposed method divides the NoC into multiple regions, each managed by a dedicated Region Router (RR) and interconnected via a Centralized Router (CR). this hierarchical structure ensures that even in the presence of multiple faulty routers, data packets can be rerouted effectively, maintaining system functionality. We implement the proposed method on a Xilinx Virtex-7 FPGA and evaluate its performance in terms of latency, throughput, power consumption, and resource utilization. the experimental results demonstrate that the proposed method achieves a 15% improvement in system reliability with only a 3.2% increase in resource utilization, compared to the baseline NoC. Additionally, Mean Time to Failure (MTTF) is extended by 25%, while the latency remains within an acceptable range of 3.2 ns. these findings highlight the potential of our approach for use in safety-critical and high-performance computing systems, offering enhanced fault tolerance without significantly compromising performance.

关键词： Fault tolerance Scalability Fault tolerant systems computer architecture Routing throughput Robustness Reliability Resource management Field programmable gate arrays

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Optimizing Image Classification Models for Cloud Infrastructure with Elastic Scaling

Optimizing Image Classification Models for Cloud Infrastruct...

引用

computer Applications and Information Technology (ISCAIT), International symposium on

作者： Wei Wu Amazon 2121 8th Ave Seattle WA USA

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

Advances in image classification have led to significant improvements in various applications, yet challenges remain in optimizing these models for cloud infrastructures. Fluctuating workloads and the need for efficient resource allocation complicate the deployment of high-performing models. We propose a novel framework that enhances image classification performance specifically within cloud environments through Elastic Scaling. this approach leverages a modular architecture, facilitating real-time adjustments to computing resources based on demand and complexity of the models being utilized. Our methodology incorporates diverse data preprocessing techniques and evaluates multiple model architectures to strike a balance between accuracy and operational efficiency. Extensive experiments conducted across various datasets demonstrate the effectiveness of our solution, showcasing considerable enhancements in classification performance and substantial reductions in operational costs tied to cloud infrastructure. the findings highlight improvements in response times and resource utilization, creating an adaptive framework that effectively addresses differing image processing requirements. this work offers a comprehensive solution aimed at optimizing cloud-based image classification tasks while maximizing performance and efficiency.

关键词： Adaptation models Computational modeling Image processing Data preprocessing computer architecture Real-time systems Data models Resource management Time factors Image classification

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Kub: Enabling Elastic HPC Workloads on Containerized Environments

Kub: Enabling Elastic HPC Workloads on Containerized Environ...

引用

International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Daniel Medeiros Jacob Wahlgren Gabin Schieffer Ivy Peng Department of Computer Science KTH Royal Institute of Technology Sweden

the conventional model of resource allocation in HPC systems is static. thus, a job cannot leverage newly available resources in the system or release underutilized resources during the execution. In this paper, we present Kub, a methodology that enables elastic execution of HPC workloads on Kubernetes so that the resources allocated to a job can be dynamically scaled during the execution. One main optimization of our method is to maximize the reuse of the originally allocated resources so that the disruption to the running job can be minimized. the scaling procedure is coordinated among nodes through remote procedure calls on Kubernetes for deploying workloads in the cloud. We evaluate our approach using one synthetic benchmark and two production-level MPI-based HPC applications - GRO-MACS and CM1. Our results demonstrate that the benefits of adapting the allocated resources depend on the workload characteristics. In the tested cases, a properly chosen scaling point for increasing resources during execution achieved up to 2x speedup. Also, the overhead of checkpointing and data reshuffling significantly influences the selection of optimal scaling points and requires application-specific knowledge.

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To Derive or Not to Derive: I/O Libraries Take Charge of Derived Quantities Computation

To Derive or Not to Derive: I/O Libraries Take Charge of Der...

引用

International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Ana Gainaru Norbert Podhorszki Liz Dulac Qian Gong Scott Klasky Greg Eisenhauer Antonios Kougkas Xian-He Sun Jay Lofstead Oak Ridge National Laboratory Georgia Institute of Technology Illinois Institute of Technology Sandia National Laboratory

ISBN: (数字)9798350356168

ISBN: (纸本)9798350356175

the ever-increasing volume of data produced by HPC simulations necessitates scalable methods for data exploration and knowledge extraction. Scientific data analysis often involves complex queries across distributed datasets, requiring manipulation of multiple primary variables and generating derived data that needs to be handled efficiently, creating challenges for applications that need to parse many large datasets. Relying on individual applications to handle all intermediate data generally leads to redundant computations across studies and unnecessary data transfers. In this paper, we investigate the performance of different approaches where applications define derived variables as quantities of interest (QoIs) and offload the computation and transfer of these QoIs to the I/O library. this significantly reduces redundancy and optimizes data movement across the distributed storage and processing infrastructure by allowing control over when and where derived variables are computed. We present a detailed analysis of the performance-storage trade-offs associated with different solutions and showcase results for our study on two large-scale datasets created from climate and combustion simulations.

关键词： Analytical models Solid modeling Computational modeling high performance computing Redundancy Distributed databases Process control Libraries Data models Meteorology

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Mitigating Write Disturbance in Non-Volatile Memory via Coupling Machine Learning with Out-of-Place Updates

Mitigating Write Disturbance in Non-Volatile Memory via Coup...

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IEEE symposium on high-performance computer architecture

作者： Ronglong Wu Zhirong Shen Zhiwei Yang Jiwu Shu School of Informatics Xiamen University Xiamen Key Laboratory of Intelligent Storage and Computing Xiamen University

Non-volatile memory (NVM) opens up new opportunities to resolve scaling restrictions of main memory, yet it is still hindered by the write disturbance (WD) problem. the WD problem mistakenly transforms the values of NVM cells, hence seriously deteriorating memory reliability and downgrading access performance. Existing studies mainly mitigate the WD problem via encoding WD-prone data patterns under in-place updates, yet we find that when turning to out-of-place updates, they can gain the potential to reduce more WD errors. We present LearnWD, an approach that mitigates the WD problem in NVM via coupling machine learning with out-of-place updates. LearnWD first employs clustering algorithms to classify the stale data based on the error proneness. To perform a write operation, LearnWD carefully examines the aggressivity of new data and the error proneness of stale data, so as to speculatively minimize the resulting WD errors. We conduct extensive experiments using 15 real-world data sets with different data types, showing that LearnWD can assist a variety of data encoding schemes to further reduce 20.1% of WD errors, shorten 11.0% of write latency, and extend 21.9% of write endurance.

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OptBertDCNN: A framework based on BERT and optimized Deep Convolutional Neural Network for MQA 2023

OptBertDCNN: A framework based on BERT and optimized Deep Co...

引用

15th International Conference on Contemporary computing, IC3 2023

作者： Lahoti, Pawan Mittal, Namita Singh, Girdhari Department of Computer Science & Engineering MNIT Rajasthan Jaipur India

ISBN: (纸本)9798400700224

Multilingual Question Answering (MQA) generates accurate answers to the user's query despite the context language. MQA has gained popularity as individuals increasingly pose questions in both English and their native languages on social media. However, conventional Question Answering (QA) systems encounter difficulties in handling multiple languages. the development of MQA models is hindered by the lack of large-scale benchmark datasets, impeding the achievement of high performance compared to monolingual systems. An effective MQA system called OptBertDCNN is proposed to address this, leveraging multilingual cased Bidirectional Encoder Representations from Transformers (mBERT) and Optimized Deep Convolutional Neural Networks (CNN). OptBertDCNN employs a BERT tokenizer to segment sentences into tokens, enabling the extraction of features such as word embeddings from the pre-trained model, TF-IDF scores, SentiWordNet scores, and statistical features from both context and question tokens. these informative features are then inputted into OptBertDCNN, which is trained using the EHMQuAD dataset. Notably, OptBertDCNN achieves outstanding performance metrics, including an exact match of 0.755, precision of 0.765, recall of 0.773, and F1-score of 0.769. these results unequivocally demonstrate the effectiveness of OptBertDCNN in addressing the challenges of multilingual question answering. © 2023 ACM.

关键词： Convolutional neural networks

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Quantum Machine Learning Compiler for Hybrid Quantum-Classical Models

Quantum Machine Learning Compiler for Hybrid Quantum-Classic...

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International Conference on computing and Networking Technology (ICCNT)

作者： Srivageesh K Srinidhi K S Vishal U Someswara Shashank Meena Belwal Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the proposed work aims at designing a composite classical-quantum machine learning architecture for binary classification using VQCs. the goal is to use the power of quantum computing to enhance and improve the efficiency of classical machine learning techniques. this is done through the development of a customized compiler that maps the high-level quantum code to specific quantum circuits that are then incorporated with conventional Neural Networks into what can be termed as Quantum Machine Learning (QML) model. Goaded by the likelihood of quantum computation to enhance computational precision and speed, this proposed work presents an outline of the theoretical quantum circuits, architecture and development of this special compiler and linking of both atomic and classical models. A proof of concept is done using the Breast Cancer Wisconsin dataset to compare the capabilities of the suggested model with a more profound understanding of the strengths and weaknesses of this type of hybrid strategy.

关键词： Codes Computational modeling Neural networks Quantum mechanics Machine learning computer architecture Breast cancer Integrated circuit modeling Quantum circuit

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Deployment of parallel computing in a hybrid high-performance cluster based on virtualization technologies 14

Deployment of parallel computing in a hybrid high-performanc...

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14th International symposium on Intelligent Systems

作者： Volovich, K., I Denisov, S. A. Malkovsky, S., I Russian Acad Sci Fed Res Ctr Comp Sci & Control 44 Vavilova St Korpus 2 Moscow 119333 Russia Russian Acad Sci Far Eastern Branch Comp Ctr 65 Kim Yu Chen St Khabarovsk 680000 Russia

the article discusses the application of MPI technology when performing calculations on a hybrid high-performance computing cluster using virtualization on a container base. the features of the application of interprocess interaction in a virtualization environment associated with the construction of a single space of containers interacting over a high-performance computing network interconnect are considered. Approaches and algorithms for deploying and executing parallel processes in a computer cluster are proposed. the issue of the functioning of computing process control systems in the provision of PaaS services using virtualization technologies is considered. (C) 2021 the Authors. Published by Elsevier B.V.

关键词： HPC cluster hybrid architecture graphics accelerator workload management system CPU GPU MPI

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Geyser: A Compilation Framework for Qantum computing with Neutral Atoms 49

Geyser: A Compilation Framework for Qantum Computing with Ne...

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49th IEEE/ACM International symposium on computer architecture, ISCA 2022

作者： Patel, Tirthak Silver, Daniel Tiwari, Devesh Northeastern University Boston United States

ISBN: (纸本)9781450386104

Compared to widely-used superconducting qubits, neutral-atom quantum computing technology promises potentially better scalability and flexible arrangement of qubits to allow higher operation parallelism and more relaxed cooling requirements. the high performance computing (HPC) and architecture community is beginning to design new solutions to take advantage of neutral-atom quantum architectures and overcome its unique challenges. We propose Geyser, the frst work to take advantage of the multiqubit gates natively supported by neutral-atom quantum computers by appropriately mapping quantum circuits to three-qubit-friendly physical arrangement of qubits. then, Geyser creates multiple logical blocks in the quantum circuit to exploit quantum parallelism and reduce the number of pulses needed to realize physical gates. these circuit blocks elegantly enable Geyser to compose equivalent circuits with three-qubit gates, even when the original program does not have any multi-qubit gates. Our evaluation results show Geyser reduces the number of operation pulses by 25%-90% and improves the algorithm's output fdelity by 25%-60% points across different algorithms. © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Qubits

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