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Lessons learned from comparing C-CUDA and Python-Numba for GPU-Computing 28

Lessons learned from comparing C-CUDA and Python-Numba for G...

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28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Oden, Lena Fernuniv Inst Math & Informat Hagen Germany

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165820

Python as programming language is increasingly gaining importance, especially in data science, scientific, and parallel programming. It is faster and easier to learn than classical programming languages such as C. However, usability often comes at the cost of performance and applications written in Python are considered to be much slower than applications written in C or FORTRAN. Further, it does not allow the usage of CPUs-besides of pre-compiled libraries. However, the Numba package promises performance similar to C code for compute intensive parts of a Python application and it supports CUDA, which allows the use of GPUs inside a Python application. In this paper we compare the perforinance of iVumba-CUDA and C-CUDA for different kinds of applications. For compute intensive benchmarks, the performance of the Numba version only reaches between 50% and 85% performance of the CCUDA version, despite the reduction operation, where the Numba version outperforms CUDA. Analyzing the PTX code and CUDA performance counters revealed that index-calculation is one 'uniting factor in Numba. Another problem is the type interference for single precision computations, as sonic values are computed in double precision. By optimizing this within the Numba package, the performance of Numba improves. However, C-CUDA applications still outperform the Numba versions. Further analysis with the CloverLeav Mini App shows that Numba performance further decreases for applications with multiple different compute kernels. The non-CPU part slows down these applications, due to the slow Python interpreter. This leads to a worse GPU utilization.

关键词： Python Graphics processing units Libraries Kernel Indexes Benchmark testing

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Field Programmable Gate Array: An Extensive Review, Recent Trends, Challenges and applications

Field Programmable Gate Array: An Extensive Review, Recent T...

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international conference on Computing for Sustainable Global Development (INDIACom)

作者： Arti Badhoutiya Zainb Jaffer Heba Mohammed Hussein Ashima Juyal Manisha Mittal Rohit Anand Department of Electrical Engineering GLA University Mathura India Medical Technical College Al-Farahidi University Iraq Department of Dentistry Al-Zahrawi University College Karbala Iraq Department of ECE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of ECE GTBIT New Delhi India Department of ECE G.B. Pant DSEU Okhla-1 Campus (Formerly GBPEC) New Delhi India

A field programmable gate array (FPGA) is a type of programmable logic device that the consumer can modify after production to carry out a variety of tasks, from fundamental logic gate operations to AI systems and beyond. More than 70,000 documents pertaining to FPGA have been found in the two most prominent scientific databases, Scopus and Clarivate Web of Science. These articles demonstrate the wide variety of FPGA-based applications, from navigation systems on Mars rovers to a new mechanism that allows the kilograms redefinition’s magnetic suspension system. This paper provides a comprehensive overview of field-programmable gate arrays (FPGAs), taking readers on a journey from the basics to sophisticated programming techniques. No matter how much or how little experience you have with FPGA programming, this study will be an invaluable resource for filling in the gaps in your knowledge. Readers will have a solid grounding in essential topics including FPGA architecture, hardware description languages, and synthesis. The present work addresses critical subjects such as FPGA architecture, hardware description languages, and synthesis, thereby providing readers with a robust foundation. Furthermore, it boldly ventures into the domain of advanced topics, including high-level synthesis and parallel processing, amplifying its appeal to seasoned FPGA enthusiasts. It is unequivocally evident that this work serves as an indispensable resource, equipping individuals from diverse backgrounds with the proficiency to fully exploit the remarkable potential of FPGAs across a myriad of applications and industries. This study also provides an overview of the recent trends and evolution of the related applications.

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distributed economy automatic generation control algorithm for multi inverter ac microgrid

Distributed economy automatic generation control algorithm f...

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international conference on applications and techniques in Cyber Intelligence, ATCI 2020

作者： Jing, Wang Chaohu University ChaohuAnhui238000 China

ISBN: (纸本)9783030539795

In the traditional droop control, the VSG algorithm and the voltage compensation link are added to make the inverter have the large inertia characteristic of the synchronous generator (SG). The output voltage of each parallel inverter is the same, and the active and reactive power are distributed according to the rated capacity ratio. Through the design of the voltage current control loop, the equivalent output impedance of the inverter is inductive, so as to meet the droop control requirements. It is proved that the parallel inverter has good dynamic and static characteristics. Matlab/Simulink simulation and experiment verify the feasibility of the proposed control strategy. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： MATLAB

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RITA: Efficient Memory Allocation Scheme for Containerized parallel Systems to Improve Data processing Latency 13

RITA: Efficient Memory Allocation Scheme for Containerized P...

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IEEE 13th international conference on Cloud Computing (CLOUD)

作者： Jia, Danlin Bayati, Mahsa Lee, Ron Mi, Ningfang Northeastern Univ Boston MA 02115 USA Samsung Semicond San Jose CA USA

ISBN: (纸本)9781728187808

The emerging resource-sharing container-based virtualization is prevalent in IT, as it is a much lighter deployment in the cloud environment compared to VM-based virtualization. distributed data-processing workloads executing in parallel take advantages of resource sharing, fast delivery, and excellent portability of containerization, but also suffer from resource competition and performance interference. Especially for memory virtualization, data-processing frameworks allocate physical memory (i.e., RAM) and swap to applications specified by users, without considering cache-characteristics and parallelism of applications, which induces performance degradation and significantly protracted latency which is worse given over-provisioning. We design an efficient memory allocation scheme (RITA) for containerized parallel systems to improve data processing latency. RITA monitors memory usage and cache characteristics of applications, and dynamically re-allocates memory resources. We implement RITA in a real-world system, which can easily migrate to other container-based virtualization environments. Our experimental results show that RITA provides remarkable latency improvement for memory intensive distributed data-processing workloads.

关键词： Containerization distributed Data-processing Latency Memory Allocation

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Investigating Novel Approaches to Privacy-Aware Healthcare Data Sharing in Cloud Environment

Investigating Novel Approaches to Privacy-Aware Healthcare D...

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international conference on Inventive Computation Technologies (ICICT)

作者： Sakthidevi. I G. Fathima Adhiyamaan College of Engineering (Autonomous) Hosur India Dept of CSE Adhiyamaan College of Engineering (Autonomous) Hosur India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In the contemporary landscape of healthcare data management, the proliferation of cloud computing has revolutionized the storage, accessibility, and analysis of vast volumes of sensitive patient information. The transition to cloud-based infrastructures offers unparalleled scalability, cost-efficiency, and flexibility, thereby enticing healthcare organizations to leverage cloud technologies for data storage and processing. However, alongside the advantages come formidable challenges, notably concerning data privacy and security. This paper presents a novel Privacy-Aware Data Sharing Framework tailored for healthcare applications within cloud environments. In response to growing concerns regarding data privacy and security, the proposed framework integrates a newly developed algorithm, termed PrivacyGuard, to ensure robust privacy protection while facilitating efficient data sharing. PrivacyGuard combines Attribute-Based Encryption (ABE) with Data Fragmentation techniques to enhance privacy preservation. The framework employs ABE to encrypt healthcare data based on attribute-based access policies, enabling only authorized users with corresponding attributes to decrypt and access the data. Moreover, Data Fragmentation is utilized to break down sensitive data into smaller fragments distributed across multiple cloud servers, mitigating the risk of unauthorized access. To evaluate the effectiveness of the Privacy-Aware Data Sharing Framework, comprehensive simulation analyses are conducted, comparing its performance with several existing algorithms using appropriate simulation metrics such as throughput, latency, computational overhead and security overhead. Results demonstrate that PrivacyGuard significantly improves data privacy while maintaining satisfactory performance levels compared to traditional approaches. This research contributes to advancing privacy-preserving techniques in healthcare data sharing within cloud environments, addressing critical concerns surrou

关键词： Data privacy Cloud computing distributed databases Medical services Organizations Throughput Security

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Memory Access Granularity Aware Lossless Compression for GPUs

Memory Access Granularity Aware Lossless Compression for GPU...

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international Symposium on parallel and distributed processing (IPDPS)

作者： Sohan Lal Manuel Renz Julian Hartmer Ben Juurlink Technische Universit&#x00E4 t Hamburg Germany t Berlin Germany

High-bandwidth off-chip memory has played a key role in the success of Graphics processing Units (GPUs) as an accelerator. However, as memory bandwidth scaling continues to lag behind the computational power, it remains a key bottleneck in computing systems. While memory compression has shown immense potential to increase the effective memory bandwidth by compressed data transfers between on-chip and off-chip memory, the large memory access granularity (MAG) of off-chip memory limits compression techniques from achieving a high effective compression ratio. Unfortunately, state-of-the-art lossless memory compression techniques do not take the large MAG of off-chip memory into account. A recent study has used MAG-aware approximation to increase the effective compression ratio, however, not all applications can tolerate errors, which limits its applicability. We propose extensions and GPU-specific optimizations to adapt a lossless memory compression technique to a MAG size to increase the effective compression ratio and performance gain. Our technique is based on the well-known Base-Delta-Immediate (BDI) compression technique that compresses a memory block to a common base and multiple deltas. We leverage the key observation that deltas often contain enough leading zeros to compress a block to a multiple of MAG without any loss of information. We show that MAG-aware BDI provides, on average, 48 % higher effective compression ratio, 10% (up to 27%) higher speedup, and 16% bandwidth reduction compared to normal BDI. While BDI, FPC, and CPACK have a similar compression ratio, MAG-aware BDI outperforms FPC, CPACK, and SLC by 56%, 47%, and 33%, respectively.

关键词： distributed processing Graphics processing units Bandwidth Performance gain Data transfer Encoding System-on-chip

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Evaluating Unified Memory Performance in HIP

Evaluating Unified Memory Performance in HIP

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IEEE international Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Zheming Jin Jeffrey S. Vetter Oak Ridge National Laboratory

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Heterogeneous unified memory management between a CPU and a GPU is a major challenge in GPU computing. Recently, unified memory (UM) has been supported by software and hardware components on AMD computing platforms. The support could simplify the complexities of memory management. In this paper, we attempt to have a better understanding of UM by evaluating the performance of UM programs on an AMD MI100 GPU. More specifically, we evaluate data migration using UM against other data transfer techniques for the overall performance of an application, assess the impacts of three commonly used optimization techniques on the kernel execution time of a vector add sample, and compare the performance and productivity of selected benchmarks with and without UM. The performance overhead associated with UM is not trivial, but it can improve programming productivity by reducing lines of code for scientific applications. We aim to present early results and feedback on the UM performance to the vendor.

关键词： Productivity Performance evaluation Codes Memory management Graphics processing units Benchmark testing Programming

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Real-Time Audio Noise Reduction and Speech Enhancement Using LadderNet With Hybrid Spectrogram Time-Domain Audio Separation Network

Real-Time Audio Noise Reduction and Speech Enhancement Using...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Ayesha Siddiqua CH Hussaian Basha Haider Mohammed Abbas P. Merlin Sheela V. Indhumathi Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical & Electronics Engineering School of Engineering SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of English New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Sona College of Arts and Science Salem India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In recent years, the rapid growth of modern technology has improved people lives, but it also comes with a downside: increased exposure to noise from complex industrial infrastructure and machinery. To overcome this issue, the existing method namely a single-channel Deep Neural Network (DNN) model have developed for speech signal processing, but it led to limitations in online processing. Therefore, this research proposes a LadderNet with Hybrid Spectrogram Time-domain Audio Separation Network (HSTasNet) model for reducing the noise and enhancing speech enhancement in real-time audio processing. This procedure begins with the collection of data from real-time audio signal and then preprocessed with spectral gating filter to reduce noise from signals. After that, t-distributed Stochastic Neighbor Embedding (t-SNE) technique to extract the optimal features from preprocessed signals. Finally, LadderNet and HS-TasNet techniques are employed for noise reduction and speech enhancement. As per the results, the proposed LadderNet-HSTasNet model offered outstanding results in terms of Perceptual Evaluation of Speech Quality (PESQ) of 3.847, Short-Time Objective Intelligibility (STOI) of 1.874, and Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) of 16.792 when compared with existing Dual-Path Recurrent Neural Network (DPRNN) model.

关键词： Wireless communication Recurrent neural networks Noise reduction Stochastic processes Artificial neural networks Speech enhancement Feature extraction Real-time systems Time-domain analysis Spectrogram

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Seagull Optimization Algorithm for Underwater Sensor Network Localization

Seagull Optimization Algorithm for Underwater Sensor Network...

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Communication, Computing and Signal processing (IICCCS), IEEE international conference on

作者： Zaid Alsalami G.Ramya Pushpa Arya Barolia Sachin Yadav A N Satyanarayana L. Pattathu Rani Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Electronics & Communication Engineering IES College of Technology Bhopal M.P. India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India ECE Department Sreenidhi Institute of Science and Technology Hyderabad India Department of Electrical and Electronics Engineering KCG College of Technology Chennai India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

This paper presents a new technique to a position sensor in UWSNs with the recognition of the Seagull Optimization Algorithm. UWSNs are particularly useful for applications in environmental monitoring and military monitoring, but accurate sensor localization is difficult due to the signal loss in underwater environments and in the presence of external factors. Based on the Motivation Model, the SOA, motivated by the seagulls’ distributed foraging behavior, is a possible solution to the dilemma by striking a midway between exploration and exploitation. The synthesis of exact methodology is outlined here along with the creation of synthetic dataset and a proof of concept MATLAB code for the evaluation of the proposed SOA-based localization algorithm. A performance evaluation, which involved the use of computational simulations on a wide variety of antennas, clearly shows that the SOA achieves a higher localization accuracy in much shorter time, while consuming less power compared to other methods such as PSO, GA, or hybrid methods. The stability is one more strongest side of proposed algorithm which has been confirmed by robustness analysis, so it can be effectively used in dynamic underwater environment. The current research is not only useful in showing how the SOA can be used for the UWSN localization in specific domains, but also valuable to the development of the new UWSN localization method that is efficient and accurate, for the further expansion of the UWSN in the vital marine sphere.

关键词： Location awareness Wireless sensor networks Accuracy Heuristic algorithms Computational modeling Signal processing algorithms Robustness Service-oriented architecture Telecommunication computing Thermal stability

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A Two-layer Partitioning for Non-point Spatial Data 37

A Two-layer Partitioning for Non-point Spatial Data

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37th IEEE international conference on Data Engineering (IEEE ICDE)

作者： Tsitsigkos, Dimitrios Lampropoulos, Konstantinos Bouros, Panagiotis Mamoulis, Nikos Terrovitis, Manolis Univ Ioannina Ioannina Greece Johannes Gutenberg Univ Mainz Mainz Germany Athena Res Ctr Maroussi Greece

ISBN: (纸本)9781728191843

Non-point spatial objects (e.g., polygons, linestrings, etc.) are ubiquitous and their effective management is always timely. We study the problem of indexing non-point objects in memory. We propose a secondary partitioning technique for space-oriented partitioning indices (e.g., grids), which improves their performance significantly, by avoiding the generation and elimination of duplicate results. Our approach is novel and of a high impact, as (i) it is extremely easy to implement and (ii) it can be used by any space-partitioning index. We show how our approach can be used to boost the performance of spatial range queries. We also show how we can avoid performing the expensive refinement step of a range query for the majority of objects and study the efficient processing of numerous queries in batch and in parallel. Extensive experiments on real datasets confirm the superiority of space-oriented partitioning over data-oriented partitioning and the advantage of our approach against alternative duplicate elimination techniques.

关键词： Filtering Query processing Scalability conferences distributed databases Data engineering Spatial databases

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