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Strengthening Healthcare Cybersecurity by Optimizing Adversarial Attack Defences in Deep Learning Models using GPU and parallel Processing Technologies 7

Strengthening Healthcare Cybersecurity by Optimizing Adversa...

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7th international conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Venugopal, Hridya Christo, Mary Subaja Kattankulathur Campus SRM Institute of Science and Technology Department of Networking and Communications Tamilnadu Chengalpattu603203 India

ISBN: (纸本)9798350372816

In healthcare, safeguarding patient data integrity is paramount, necessitating robust deep learning models. This research focuses on bolstering the cyber security defenses of these models, particularly against adversarial attacks, through the application of the Localized Adversarial Feature Attack (LAFEAT) technique. Leveraging Graphics Processing Units (GPU) and the TensorFlow framework, the study explores algorithmic enhancements like the Fast Gradient Sign Method (FGSM) and Model-Agnostic Meta-Learning (MAML). These techniques perturb input data gradients or adapt model parameters, enhancing resilience against adversarial perturbations. Additionally, Particle Swarm Optimization (PSO), a metaheuristic optimization technique, is examined to fortify defense mechanisms further. parallel processing techniques, utilizing parallel GPU or distributed clusters, are implemented to expedite the optimization process, reducing computational burdens and enhancing scalability. Despite advancements, gaps remain in understanding how these models can be optimized for real-world healthcare applications, particularly in terms of balancing computational efficiency and robustness. The research demonstrates tangible benefits, with a significant 10% increase in success rates against adversarial attacks compared to baseline methods. Moreover, parallel processing implementation results in a 30% reduction in optimization time, improving the efficiency of cyber security defenses for deep learning models in healthcare. These numerical enhancements underscore the research's value in fortifying healthcare systems against adversarial threats, ensuring the security and integrity of patient data while addressing existing gaps in the optimization of adversarial defenses in healthcare applications. The strengths of this research lie in its robust adversarial attack optimization techniques, integration of parallel processing for efficiency, and application to critical healthcare tasks. Comprehensive

关键词： Graphics processing unit

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A Fast Scalable Hardware Priority Queue and Optimizations for Multi-Pushes

A Fast Scalable Hardware Priority Queue and Optimizations fo...

引用

1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Collinson, Samuel Bai, Allan Sinnen, Oliver Univ Auckland Parallel & Reconfigurable Comp Lab Dept Elect Comp & Software Engn Auckland New Zealand

ISBN: (纸本)9798350364613;9798350364606

Priority queues (PQ) are an essential data structure for many-important algorithms. Hardware implementations of priority queues can accelerate such algorithms significantly. Usually a choice has to be made between very fast (fixed size) hardware PQs or scalable PQs. Fast hardware queues can provide single cycle push and pop operations, but are limited in size. Scalable hardware queues use embedded memory to achieve scalability, but thereby compromise on speed. This paper proposes a fast and scalable hardware priority queue that can be used for general purpose. It is based on a modular and flexible hybrid design, using a shift register queue combined with a heap-based queue. In the best case, i.e. when only the first queue is needed, push and pop operations only take a single cycle. In an experimental evaluation on a Xilinx FPGA we demonstrate that performance of the hybrid queue maintains a good balance between performance and scalability even for applications where the size of the working set of data can have large variance. We further propose an optimisation of the heap based queue to support workloads that repeat several consecutive push operations followed by a single pop operation, which is the workload, for instance, in state space search or ray-tracing.

关键词： heap hybrid hardware queues priority queue shift register

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Efficient VLSI Architectures of Multimode 2D FIR Filter Bank using distributed Arithmetic Methodology 6th

Efficient VLSI Architectures of Multimode 2D FIR Filter Bank...

引用

6th international conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Odugu, Venkata Krishna Satish, B. Janardhana Rao, B. Gade, Harish Babu Department of ECE CVR College of Engineering Hyderabad India

ISBN: (纸本)9789819984503

For the implementation of the hardware structure of filter architecture, the area, power, and delay efficiency are needed. Memory complexity is also important in the 2D FIR filter architecture, while used for image processing applications. In this work, a memory-efficient 2D FIR filter bank architecture is designed using parallel processing, symmetry, and distributed arithmetic methodology. The symmetry concept decreases the quantity of the multipliers of the filter architecture. parallel processing and multimode filter bank approaches will improve memory efficiency through memory reuse and memory sharing, respectively. The distributed arithmetic (DA)-based filter architectures reduce the multiplier complexity in terms of power and area. Four types of symmetry filter architectures along with one normal filter (without symmetry) are integrated as a multimode filter bank. The required filter can be selected by the control logic. In this filter bank, the memory module is shared by all the sub-filter, which is called memory sharing. The LUT-based DA methodology is used to implement the multipliers of each sub-filter arithmetic module. The proposed multimode filter bank architecture is implemented for the filter length N = 4 with two-level parallel processing using Xilinx Vivado tools. The utilization of resources is correlated with works mentioned in the literature. The area, timing delay and power consumption reports are generated using Genus synthesis tools in 45 nm CMOS technology from the Cadence vendor. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Filter banks

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Optimal Efficient control of a parallel Multi-Units System

Optimal Efficient control of a Parallel Multi-Units System

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2023 international conference on Future Energy Solutions, FES 2023

作者： Bahloul, Mohamed Khadem, Shafiuzzamand K. IERC/Tyndall National Institute University College Cork Cork Ireland

ISBN: (纸本)9798350332308

This paper proposes a novel deterministic approach to efficiently control parallel multi-units systems (PMUS). The proposed approach consists of online and offline sub-controllers. The online controller can be implemented in both centralised or distributed approaches. It computes the reference load of each unit using Lookup tables. The latter are designed using an offline formulation through solving a Mixed Integer Linear Programming (MILP). Besides its implementation simplicity, the proposed method can efficiently manage PMUS within a large unit number and can be applied for both identical or dissimilar characteristics system units. To analyse the performance of the proposed approach, simulations and case studies are carried out considering multi-unit power conversion systems. The obtained results highlight the proposed method's superiority over classic control methods. © 2023 IEEE.

关键词： Table lookup

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Hierarchical distributed Model Predictive Control for Building Energy systems 36

Hierarchical Distributed Model Predictive Control for Buildi...

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36th international conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy systems, ECOS 2023

作者： Mork, Maximilian Xhonneux, André Müller, Dirk Jülich Germany RWTH Aachen University E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate Aachen Germany

ISBN: (纸本)9781713874928

Buildings contribute to approximately 30 % of global energy consumption, which renders their energy-efficient control an effective measure to reduce overall energy consumption. This paper presents a hierarchical distributed Model Predictive Control (MPC) for building energy systems based on nonlinear Modelica controller models. It combines hierarchical and distributed optimization approaches to split the optimization complexity within the temporal and spatial dimension. The hierarchical optimization approach considers different dynamics in complex building energy systems and ensures both anticipation for systems with high inertia and reactivity with regard to errors in the forecasting of the disturbance quantities. The distributed optimization approach divides the centralized optimization problem into subproblems to improve the scalability and adaptability of the control framework. The subproblems are solved in a parallel and iterative manner and account for both thermal (heat transfer over zone boundaries) and hydraulic inter-zone coupling (induced by a central, shared Heating, Ventilation and Air Conditioning (HVAC) system). A particular focus of the control approach is placed on robustness with respect to errors in the forecasting of the disturbances that impact the building dynamics. The control performance of the proposed MPC framework is evaluated in a simulative case study of heating and shading control of a nonlinear six-room-building Modelica model, which is exposed to different forecast accuracies for the disturbances of occupancy, solar radiation and air exchange. The case study exhibits the benefits of the control framework in terms of energy consumption, thermal discomfort and computation time in comparison to a reference control concept of a non-hierarchical distributed MPC configuration. © (2023) by ECOS 2023 All rights reserved.

关键词： Model predictive control

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Accelerating MPI Collectives with Process-in-Process-based Multi-object Techniques 23

Accelerating MPI Collectives with Process-in-Process-based M...

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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： Huang, Jiajun Ouyang, Kaiming Zhai, Yujia Liu, Jinyang Si, Min Raffenetti, Ken Zhou, Hui Hori, Atsushi Chen, Zizhong Guo, Yanfei Thakur, Rajeev Univ Calif Riverside Riverside CA 92521 USA NVIDIA Corp Santa Clara CA USA Meta Platforms Inc Menlo Pk CA USA Argonne Natl Lab Argonne IL USA Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798400701559

In the exascale computing era, optimizing MPI collective performance in high-performance computing (HPC) applications is critical. Current algorithms face performance degradation due to system call overhead, page faults, or data-copy latency, affecting HPC applications' efficiency and scalability. To address these issues, we propose PiP-MColl, a Process-in-Process-based Multi-object Interprocess MPI Collective design that maximizes small message MPI collective performance at scale. PiP-MColl features efficient multiple sender and receiver collective algorithms and leverages Process-in-Process shared memory techniques to eliminate unnecessary system call, page fault overhead, and extra data copy, improving intra- and inter-node message rate and throughput. Our design also boosts performance for larger messages, resulting in comprehensive improvement for various message sizes. Experimental results show that PiP-MColl outperforms popular MPI libraries, including OpenMPI, MVAPICH2, and Intel MPI, by up to 4.6X for MPI collectives like MPI_Scatter and MPI_Allgather.

关键词： mpi collective process-in-process distributed systems message passing interface parallel algorithms

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LDPP: A Learned Directory Placement Policy in distributed File systems 51

LDPP: A Learned Directory Placement Policy in Distributed Fi...

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51st international conference on parallel Processing (ICPP)

作者： Wang, Yuanzhang Yang, Fengkui Zhang, Ji Zhou, Ke Li, Chunhua Liu, Chong Cheng, Zhuo Fang, Wei Liu, Jinhu Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan Peoples R China Huawei Co Ltd Chengdu Peoples R China

ISBN: (纸本)9781450397339

Load balance is a critical problem in distributed file systems. Previous works focus on how to distribute data evenly on different nodes or storage devices from the perspective of file level, but neglect to effectively take advantage of the directory's locality and the long duration of the directory's hotness, which may affect the degree of balance and cause performance degradation. To overcome this shortcoming, in this paper, we propose a learning-based directory placement policy, called LDPP, which determines the data layout by predicting the load. We first establish a relationship between directory request characteristics and state information to predict the state information of the directory (storage capacity, bandwidth, and IOPS). Then, the new directory is placed on different nodes in a multi-dimensional manner based on the Manhattan distance according to the predicted multidimensional state information. In addition, we also take into account the trade-off between the same category directory classified by the load prediction module and the peer directories and explore their influence on the balance. Extensive experiments demonstrate that LDPP not only efficiently alleviates load imbalance and increases the utilization of the resources but also improves DFS performance in practice, which can reduce service latency by up to 36% and increase IOPS and bandwidth by 8% and 9%, respectively.

关键词： DFS load balance directory placement machine learning

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A Reliability-Based Task Scheduling Method for Wireless distributed systems 23rd

A Reliability-Based Task Scheduling Method for Wireless Dist...

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23rd international conference on Computational Science and its Applications (ICCSA)

作者： Bodkhe, Sonali Mahajan, Anjali Sant Gadge Baba Amravati Univ Dept Comp Sci Amravati Maharashtra India Govt Polytech Nagpur Maharashtra India

ISBN: (纸本)9783031368042;9783031368059

parallel computing and distributed computing are the popular terminologies of scheduling. With advancement in technology, systems have become much more compact and fast and need of parallelization plays a major role for this compaction. Wireless computing is also a common concept associated with each new development. Scheduling of tasks has always been a challenging area and is an NP-complete problem. Moreover, when it comes to wireless distributed computing, reliable scheduling plays an important role in order to complete a task in a wireless distributed system. This work proposes an algorithm to dynamically schedule tasks on heterogeneous processors within a wireless distributed computing system. A lot of heuristics, meta-heuristics & genetics have been used earlier with scheduling strategies. However, most of them haven't taken reliability into account before scheduling. Here a heuristic that deals with reliable scheduling is considered. The scheduler also works within an environment which has dynamically changing resources and adapts itself to changing system resources. The testing was carried out with up to 200 tasks being scheduled while testing in a real time wireless distributed environment. Experiments have shown that the algorithm outperforms the other strategies and can achieve a better reliability along with no increase in make-span, in spite of wireless nodes.

关键词： distributed Computing Scheduling Reliability Wireless distributed systems (WDS)

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SR2C: A Structurally Redundant Short Reads Collapser for Optimizing DNA Data Compression 29

SR2C: A Structurally Redundant Short Reads Collapser for Opt...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Sun, Hui Ma, Huidong Zheng, Yingfeng Xie, Haonan Wang, Xiaofei Liu, Xiaoguang Wang, Gang Nankai University College of C.S. Nankai-Orange D.T. Joint Lab Nankai-Baidu Joint Lab ICIC TMCC SysNet DISSec GTIISC Tianjin300350 China Guangxi University School of Electrical Engineering Nanning530004 China

ISBN: (纸本)9798350330717

The current redundant sequence deduplication algorithms cannot remove structural repetitive DNA short reads such as mirror, reverse, paired, and complementary palindromes in high-throughput genomics sequencing data. Moreover, these methods also cannot construct indexes to recover the original sequences, thus failing to meet the requirements of lossless compression for downstream applications. To address these problems, we propose a data structure called Cycle-Hash-Linkage (CHL) and present a CPU parallelism optimization algorithm named SR2C (Structurally Redundant Short Reads Collapser) based on CHL to improve the compression ratio of DNA sequencing data. Experimental results on actual data from the NCBI database demonstrate that SR2C achieves an average residual sequence percentage improvement of 2.556% compared to the state-of-the-art redundant sequence deduplication algorithm, Minirmd. Furthermore, SR2C cascaded optimization improves the average compression ratios of compression algorithms Pigz, PBzip2, XZ, and 7Z by 92.345%, 78.999%, 10.132%, and 7.434%, respectively. By leveraging multi-core CPU parallel computation, SR2C effectively reduces time consumption, which achieves 2-5X deduplication and recovers *** same name Linux toolkit is freely available at https://***/fahaihi/SR2C. © 2023 IEEE.

关键词： data compression DNA sequencing data compression parallel algorithm redundancy deduplication rolling-hash algorithm structurally redundant reads

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Didal: distributed Data Library for Development of parallel Fragmented Programs 17th

Didal: Distributed Data Library for Development of Paralle...

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parallel Computing Technologies - 17th international conference, PaCT 2023, Proceedings

作者： Malyshkin, Victor Schukin, Georgy Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russia Novosibirsk State Technical University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia

ISBN: (纸本)9783031416729

Nowadays with rapid evolution of high-performance computing systems it’s becoming essential to have tools to simplify development of efficient portable parallel programs for these systems. Fragmented programming is a technology where parallel program is represented as a collection of pieces of data (data fragments) and computations on these pieces (computation fragments), able to be tuned to the resources of a computing system and automatically provide such facilities as dynamic load balancing. Didal is a distributed data library to support development of efficient parallel fragmented programs on distributed memory supercomputers. The library contains facilities for data partitioning, distribution and load balancing. In this paper foundations of the library are explained and applicability of the library is demonstrated with Particle-in-Cell (PIC) method implementation, which shows performance comparable to conventional parallel programming tools. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： parallel programming

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