摘要：
In the environment of cloud computing, the data produced by massive users form a data stream and need to be protected by encryption for maintaining confidentiality. Traditional serial encryption algorithms are poor in performance and consume more energy without considering the property of streams. Therefore, we propose a velocity-aware parallel encryption algorithm with low energy consumption (LECPAES) for streams in cloud computing. The algorithm parallelizes Advanced Encryption Standard (AES) based on heterogeneous many-core architecture, adopts a sliding window to stabilize burst flows, senses the velocity of streams using the thresholds of the window computed by frequency ratios, and dynamically scales the frequency of Graphics Processing Units (GPUs) to lower down energy consumption. The experiments for streams at different velocities and the comparisons with other related algorithms show that the algorithm can reduce energy consumption, but only slightly increases retransmission rate and slightly decreases throughput. Therefore, LECPAES is an excellent algorithm for fast and energy-saving stream encryption.
摘要：
Encryption plays an important role in protecting data, especially data transferred on the Internet. However, encryption is computationally expensive and this leads to high energy costs. Parallel encryption solutions using more CPU/GPU cores can achieve high performance. If we consider energy efficiency to be cost effective using parallel encryption solutions at the same time, this problem can be alleviated effectively. Because many CPU/GPU cores and encryption are pervasive currently, saving energy cost by parallel encrypting has become an unavoidable problem. In this paper, we propose an energy-efficient parallel Advance Encryption Standard (AES) algorithm for CPU-GPU heterogeneous platforms. These platforms, such as the Green 500 computers, are popular in both high performance and general computing. Parallelizing AES algorithm, using both GPUs and CPUs, balances the workload between CPUs and GPUs based on their computing capacities. This approach also uses the Nvidia Management Library (NVML) to adjust GPU frequencies, overlaps data transfers and computation, and fully utilizes GPU computing resources to reduce energy consumption as much as possible. Experiments conducted on a platform with one K20M GPU and two Xeon E5-2640 v2 CPUs show that this approach can reduce energy consumption by 74% compared to CPU-only parallel AES algorithm and 21% compared to GPU-only parallel AES algorithm on the same platform. Its energy efficiency is 4.66 MB/Joule on average higher than both CPU-only parallel AES algorithm (1.15 MB/Joule) and GPU-only parallel AES algorithm (3.65 MB/Joule). As an energy-efficient parallel AES algorithm solution, it can be used to encrypt data on heterogeneous platforms to save energy, especially for the computers with thousands of heterogeneous nodes. (C) 2020 Elsevier B.V. All rights reserved.
摘要：
Encryption plays an important role in protecting data, especially data transferred on the Internet. However, encryption is computationally expensive and this leads to high energy costs. Parallel encryption solutions using more CPU/GPU cores can achieve high performance. If we consider energy efficiency to be cost effective using parallel encryption solutions at the same time, this problem can be alleviated effectively. Because many CPU/GPU cores and encryption are pervasive currently, saving energy cost by parallel encrypting has become an unavoidable problem. In this paper, we propose an energy-efficient parallel Advance Encryption Standard (AES) algorithm for CPU-GPU heterogeneous platforms. These platforms, such as the Green 500 computers, are popular in both high performance and general computing. Parallelizing AES, using both GPUs and CPUs, balances the workload between CPUs and GPUs based on their computing capacities. This approach also uses the Nvidia Management Library (N-VML) to adjust GPU frequencies, overlaps data transfers and computation, and fully utilizes GPU computing resources to reduce energy consumption as much as possible. Experiments conducted on a platform with one K20M GPU and two Xeon E5-2640 v2 CPUs show that this approach can reduce energy consumption by 74% compared to CPU-only parallel AES and 21% compared to GPU-only parallel AES on the same platform. Its energy efficiency is 4.66 MB/Joule on average higher than both CPU-only parallel AES (1.15 MB/Joule) and GPU-only parallel AES (3.65 MB/Joule). As an energy-efficient parallel AES solution, it can be used to encrypt data on heterogeneous platforms to save energy, especially for the computers with thousands of heterogeneous nodes.
摘要：
For large-scale sparse matrices, SpMV cannot be processed on GPU using the common storage formats because of the memory limitation. In addition, the parallel effect is poor using general formats for the sparse matrices with extremely uneven distribution of non-zero elements, which leads to performance deterioration. This paper presents an optimal partitioning strategy based on the distribution of non-zero elements in a sparse matrix to improve the performance of SpMV, and uses a hybrid format, which mixes CSR and ELL formats, to store the blocks partitioned from the sparse matrix. The hybrid blocked format has better compression effect and more uniform distribution of non-zero elements, which can be suitable for more types of sparse matrices. Our partitioning strategy is proven to be optimal, which can yield the minimum parallel execution time on GPU. We develop an optimal partitioning strategy to improve the performance of SpMV.We present a reordering algorithm in which the time complexity is only O(Nlog2k).We employ a hybrid format to store a blocked sparse matrix partitioned by our optimal partitioning strategy.
摘要：
Sparse matrix-vector multiplication (SpMV) is an important issue in scientific computing and engineering applications. The performance of SpMV can be improved using parallel computing. The implementation and optimization of SpMV on GPU are research hotspots. Due to some irregularities of sparse matrices, the use of a single compression format is not satisfactory. The hybrid storage format can expand the range of adaptation of the compression algorithms. However, because of the imbalance of non-zero elements, the parallel computing capability of a GPU cannot be fully utilized. The parallel computing capability of a CPU is also rising due to increased number of cores in CPU. However, when a GPU is computing, the CPU controls the process instead of contributing to the computational work. It leads to under-utilization of the computing power of CPU. Due to the characteristics of the sparse matrices, the data can be split into two parts using the hybrid storage format to be allocated to CPU and GPU for simultaneous computing. In order to take full advantage of computing resources of CPU and GPU, the CPU-GPU heterogeneous computing model is adopted in this paper to improve the performance of SpMV. With analysis of the characteristics of CPU and GPU, an optimization strategy of sparse matrix partitioning using a distribution function is proposed to improve the computing performance of SpMV on the heterogeneous computing platform. The experimental results on two test machines demonstrate noticeable performance improvement. (C) 2017 Elsevier Inc. All rights reserved.
