Fast Parallel Network Packet Filter System based on CUDA

The considerable move towards the use of renewable energy resources has been provided by the digitization of energy systems with the help of virtual power plants (VPPs). However, due to the coincidence of this move with the introduction of new technologies in information and communications, joining these systems raises concerns about the privacy of personal data. The only real-world approach widely used in this case is to anonymize or pseudonymize the information associated with individuals in data received from distributed measurement devices. In this paper, we propose the method of classifying received data packets into different flows and assigning different access levels for each flow. This method makes data pseudonymous. Before this step, the received data, which has a different format, is unionized. To implement this idea, a tuple space flow classification algorithm is parallelized on a CPU cluster using MPI and OpenMP according to different scenarios. The CPU cluster consists of one head node and two computational nodes for packet classification operations. In this research, two scenarios have been used to run the CPU algorithm in parallel. The first scenario uses MPI and the second scenario uses a combination of MPI and OpenMP libraries. Also, the Tuple Space algorithm has been implemented on the computing systems using the mentioned libraries in the form of two scenarios using OpenMP and MPI. According to our results, the increase in the number of processor cores is linearly correlated with the increase in the speed of classification. Furthermore, while MPI uses more memory than OpenMP, it helps to achieve a higher speed of classification. In the combined method, the maximum speed of flow classification can be achieved if the number of processes and threads is equal to the number of processor cores. In other words, when the sum of processes and threads does not outnumber CPU cores, the least classification time and memory usage can be achieved.

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“…There are multiple methods for the parallel implementation of algorithms [19][20][21][22][23][24]. One common method is to use a CPU cluster.…”

Section: Rtu Rtumentioning

confidence: 99%

High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

et al. 2022

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“…There are 6 types of memory in a GPU that can be accessed by threads. Figure 3 shows the hierarchy of GPU memory and Table 2 shows the characteristics of each memory [29], [30], [31]. Register is mainly used to store variables which are frequently used by each thread [32].…”

Section: A Gpu Architecturementioning

confidence: 99%

A Parallelization Algorithm for Real-Time Path Shortening of High-DOFs Manipulator

Seo¹,

Lee

Kim

2021

IEEE Access

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The paths generated by sampling-based path planning are generally not smooth and often generate multiple unnecessary robot posture changes in the task space. To mitigate such issues with a planned path from sampling-based path planners, shortcut-based path shortening algorithms are commonly adopted in the field of robot manipulator path planning as a post-processing step. In this paper, we analyze shortcut-based algorithms and propose a new approach based on the idea of parallelism for faster path shortening so that it can be more applicable in environments where a path has to be generated as quickly as possible to avoid collisions with other moving objects around the manipulator. Through performance comparisons in simulations, it is shown that the proposed approach can obtain a well-shortened as well as much smooth path compared to the original path faster than conventional shortcut-based algorithms and an optimization-based approach developed for collision-free path generation.

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“…Bai, T. et al 12 use three strategies to accelerate Nth-degree truncated polynomial ring with GPUs and achieve high improvement on performance. Hung, C. et al 13 develop two efficient GPGPU-based parallel packet classification approaches to filter packets by leveraging thousands of threads.…”

Section: Related Workmentioning

confidence: 99%

Accelerated Diffusion-Based Recommendation Algorithm on Tripartite Graphs with GPU Clusters

Wang¹,

Huang²,

Li³

2016

IJNDC

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Exorbitant computation cost hinders the practical application of recommendation algorithm, especially in timecritical application scenario. Although experiments show that recommendation algorithm based on an integrated diffusion on user-item-tag tripartite graphs can significantly improve accuracy, diversification and novelty of recommendation, it is also very time-consuming. Therefore, a parallel solution is frequently needed to improve the execution speed of the algorithm. This paper explicitly presents the parallel implementation and optimizations of diffusion-based recommendation on weighted tripartite graphs algorithm using Compute Unified Device Architecture and related optimization solutions including accelerated memory access with shared memory, stream scheduling and GPU clusters optimization. Compared to the algorithm running on a single CPU core, the unoptimized GPU kernel can achieve 153.9 speedup on average with the input dataset consists of 30000 records on GTX 980. With shared memory applied, the time cost on device memory access saves about 50% on dataset of 90000 records and with 2-way streams scheduling, the kernel's performance improves about 7% ~ 13%. Based on the optimized GPU kernel, we evaluate the performance of the recommendation algorithm with customized socket communication mechanism on GPU clusters. And compared to a single GPU node, we achieve 7.55 speedup on clusters of 9 GPUs when recommending for 8000 users. Besides this, the speedup of GPU clusters is also 26.1 times of the speedup of our CPU clusters of 9 nodes and 1586.28 times of serial algorithm on one CPU core. It proves that GPU technology can dramatically improve the algorithm's performance.

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Fast Parallel Network Packet Filter System based on CUDA

Cited by 6 publications

References 35 publications

High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

A Parallelization Algorithm for Real-Time Path Shortening of High-DOFs Manipulator

Accelerated Diffusion-Based Recommendation Algorithm on Tripartite Graphs with GPU Clusters

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