GPU-accelerated high-performance encoding and decoding of hierarchical RAID in virtual machines

Song, Tae-Geon; Pirahandeh, Mehdi; Ahn, Cheongjin; Kim, Deok‐Hwan

doi:10.1007/s11227-017-1969-y

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…In-Memory Data Scheduler Key-value (KV) stores are suitable for latency-sensitive internet services, and they have been widely used in large-scale data-intensive internet applications [17]. High demand from users has increased the need for fast read/write performance in accessing databases [18][19][20]. Sensor-based key-value data consist of many small files, which create a latency bottleneck from low I/O performance in the system [17,21].…”

Section: Edge-based Data Scheduling For Fcn-lstm Driver Profilingmentioning

confidence: 99%

A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms

2021

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The gradual increase in latency-sensitive, real-time applications for embedded systems encourages users to share sensor data simultaneously. Streamed sensor data have deficient performance. In this paper, we propose a new edge-based scheduling method with high-bandwidth for decreasing driver-profiling latency. The proposed multi-level memory scheduling method places data in a key-value storage, flushes sensor data when the edge memory is full, and reduces the number of I/O operations, network latency, and the number of REST API calls in the edge cloud. As a result, the proposed method provides significant read/write performance enhancement for real-time embedded systems. In fact, the proposed application improves the number of requests per second by 3.5, 5, and 4 times, respectively, compared with existing light-weight FCN-LSTM, FCN-LSTM, and DeepConvRNN Attention solutions. The proposed application also improves the bandwidth by 5.89, 5.58, and 4.16 times respectively, compared with existing light-weight FCN-LSTM, FCN-LSTM, and DeepConvRNN Attention solutions.

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Section: Edge-based Data Scheduling For Fcn-lstm Driver Profilingmentioning

confidence: 99%

A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms

2021

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“…Song et al [21] proposed new high-performance encoding and decoding for hierarchical RAID accelerated by the GPU in a multiple VM environment. For each VM, pass-through GPU technology was used to provide dedicated access to GPU cores.…”

Section: Related Workmentioning

confidence: 99%

On Construction and Performance Evaluation of a Virtual Desktop Infrastructure With GPU Accelerated

et al. 2020

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More engaging and accessible solutions that offer outstanding user experience are needed. The virtual desktop infrastructure (VDI) is the first user access part of the cloud. Therefore, the performance of the VDI has a significant influence on the network. The graphics processing unit (GPU) can accelerate performance by assigning each virtual machine a dedicated GPU for performance-boosting to improve the user experience. In this paper, we propose a high-performance VDI using an integration of a GPU on OpenStack with a PCI pass-through. It might have certain benefits over using virtualized hardware, such as lower latency, better performance, and more functionality. The evaluation and comparison of the computer processing unit (CPU) and GPU are presented among the different benchmark tools.

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A globally shared resource paradigm for encoded storage systems in the public cloud

Zhang

2024

Fundamental Research

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GPU-accelerated high-performance encoding and decoding of hierarchical RAID in virtual machines

Cited by 8 publications

References 29 publications

A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms

A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms

On Construction and Performance Evaluation of a Virtual Desktop Infrastructure With GPU Accelerated

A globally shared resource paradigm for encoded storage systems in the public cloud

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