Implementation and Evaluation of CUDA-Unified Memory in Numba

Oden, Lena; Saidi, Tarek

doi:10.1007/978-3-030-71593-9_16

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Key in our research is that NumPy can perform operations on arrays of different but compatible dimensions. For example, we can add two arrays with dimensions 5x5 and 5: >>> arr1:np.ndarray = np.arange (1,26,1).reshape ((5, 5) >>> arr1 array([ [ 1,2,3,4,5], [ 6,7,8,9,10], [11,12,13,14,15], [16,17,18,19,20], [21,22,23,24,25]]) >>> arr2:np.ndarray = np.arange (1,5) >>> arr2 array( [1,2,3,4,5]) >>> np.add(arr1, arr2) array ([[ 2, 4, 6, 8, 10], [ 7,9,11,13,15], [12,14,16,18,20], [17,19,21,23,25]...…”

Section: >>> Npreshape(user_arr (-1 1)) Array([[1] [2] [3] [4] [5]])mentioning

confidence: 99%

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Performance Evaluation of Python Libraries for Multithreading Data Processing

Krivtsov,

Parfeniuk,

Bazilevych

et al. 2024

A.I.S.

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Topicality. The rapid growth of data in various domains has necessitated the development of efficient tools and libraries for data processing and analysis. Python, a popular programming language for data analysis, offers several libraries, such as NumPy and Numba, for numerical computations. However, there is a lack of comprehensive studies comparing the performance of these libraries across different tasks and data sizes. The aim of the study. This study aims to fill this gap by comparing the performance of Python, NumPy, Numba, and Numba.Cuda across different tasks and data sizes. Additionally, it evaluates the impact of multithreading and GPU utilization on computation speed. Research results. The results indicate that Numba and Numba.Cuda significantly optimizes the performance of Python applications, especially for functions involving loops and array operations. Moreover, GPU and multithreading in Python further enhance computation speed, although with certain limitations and considerations. Conclusion. This study contributes to the field by providing valuable insights into the performance of different Python libraries and the effectiveness of GPU and multithreading in Python, thereby aiding researchers and practitioners in selecting the most suitable tools for their computational needs.

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Section: >>> Npreshape(user_arr (-1 1)) Array([[1] [2] [3] [4] [5]])mentioning

confidence: 99%

“…Additionally, Numba. Cuda extends the capabilities of Numba by allowing developers to write CUDA code in Python, thereby enabling the utilization of NVIDIA GPUs for general-purpose processing [18]. This is a significant advancement as GPUs' parallel processing capabilities can significantly reduce computation time for specific tasks [19].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Python Libraries for Multithreading Data Processing

Krivtsov,

Parfeniuk,

Bazilevych

et al. 2024

A.I.S.

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show abstract

“…GPUs have a different computing architecture than CPUs or FPGAs, and their internal operations are mainly matrix operations. The CUDA interface in Python is implemented through the Numba on-the-fly compiler [37], [38]. In the kernel functions, variables are computed and transferred in the form of Numpy matrices, and the use of Numpy matrix variables in GPU kernel functions is somewhat different from that in CPUs, mainly in bitwise operations and value assignment.…”

Section: A Numba Parallel Library Featuresmentioning

confidence: 99%

A Controllable Pipeline Framework of Block Ciphers on GPU for Streaming Data

Xiao,

Zhao,

Zhang

et al. 2023

IEEE Access

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With the application and development of real-time multimedia information technology such as online meetings and short videos, the demand for secure transmission of streaming media data based on high throughput and low latency has increased significantly. Since servers must encrypt large amounts of data in real-time without compromising their service capabilities, Graphic Processing Units (GPUs) are considered the best candidates for cryptographic accelerators to handle large amounts of data in this situation. For the characteristics of streaming data, this paper proposes a GPU-based controllable pipelined encryption framework, which guarantees security during streaming data transmission while satisfying the requirements of high throughput and low latency. Firstly, in order to make the CPU in the traditional heterogeneous system understand the intermediate state of the GPU's operation in real-time, this paper adopts the cyclic checkpoint mechanism to realize the interrupt control; secondly, real-time signal feedback between the host and the device each other during the execution of the kernel function is realized by real-time monitoring and other modules; through the above ways, the CPU-GPU pipeline control framework is realized. Finally, based on AES and SM4 encryption algorithms, this paper designs and realizes the pipeline encryption and decryption process of streaming data based on CUDA in Python environment, and verifies the group automation pipeline framework based on interrupt monitoring and controlling through experiments; and under the condition of using 5 threads grouping, the data throughput rate reaches 1.01Gbps, which has a high throughput rate.INDEX TERMS Graphic Processing Units (GPUs), Pipeline computing, Block Cipher, CUDA

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“…Virtual memory, a widely used memory management concept, extends beyond the conventional memory structure found in desktop PCs and multicomputer systems, including shared virtual memory (SVM) systems. Additionally, it acts as a bridge between the CPU and GPU, as illustrated in the compute unified device architecture (CUDA) [3].…”

Section: Introductionmentioning

confidence: 99%

Random access memory page caching: a strategy for enhancing shared virtual memory multicomputer systems performance

Vyazigin,

Mansurova,

Malyshkin

et al. 2024

IJEECS

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This study examines a modified approach to optimizing the performance of support vector machine (SVM)-type multicomputer systems through a distinct type of caching method that allocates space in the random access memory (RAM) of a computing node for caching pages. The article extensively describes research on enhancing the performance of the SVM system through memory page caching in RAM at the hardware level by implementing the SVM system based on field-programmable gate arrays (FPGA). A systematic comparative evaluation highlights a discernible enhancement in system performance relative to systems not equipped with the revised caching algorithm. These findings could prove instrumental for subsequent studies focused on optimizing the performance of SVM systems, providing empirical data to inform future investigations and potential applications in multicomputer system performance enhancement.

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Implementation and Evaluation of CUDA-Unified Memory in Numba

Cited by 3 publications

References 13 publications

Performance Evaluation of Python Libraries for Multithreading Data Processing

Performance Evaluation of Python Libraries for Multithreading Data Processing

A Controllable Pipeline Framework of Block Ciphers on GPU for Streaming Data

Random access memory page caching: a strategy for enhancing shared virtual memory multicomputer systems performance

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