GPUs For General Purpose Computation Research Articles

Hiding the Data Using Hybrid of LSB and AES in Encrypted Image


 
 
 
 Abstract
 In this technique, which is known as "Reserving Room before Encryption," we first allotted some space for data embedding. Then, the picture was encrypted using the private key, which helps to safeguard the encryption process. Using a different private key, the data is then securely hidden in an encrypted image. Following points, we receive two private keys from the transmitter side, which increased the encryption and decryption processes' security. The data hiding the encrypted image and the two private keys are now sent to the receiver. Receivers can only retrieve data and images if they have access to the encryption key and the data concealing private key, respectively. Thus, the image and data were not easily recovered by the hackers. Because of this, despite the AES algorithm's mathematical soundness, the primary underlying issues with it, like as speed and the use of subpar or tiny prime integers that have resulted in severe security breaches, may be resolved. A basic and commonly utilised technology worldwide is public key cryptography. The majority of public key algorithms, including AES, are based on modular arithmetic. As a result, performing public key encryption and decryption requires performing several modular multiplications on very large values. As a result, symmetric key algorithms are known to be faster than public key algorithms. It has recently become increasingly commonplace to use GPUs for general-purpose computing. The extremely parallel features have been used to significantly improve the performance of many computer problems.
 
 
 

A survey on techniques for cooperative CPU-GPU computing

Graphical Processing Unit provides massive parallelism due to the presence of hundreds of cores. Usage of GPUs for general purpose computation (GPGPU) has resulted in execution speedup and energy efficiency. In addition, the modern CPUs also possess multiple cores, with enormous computational power. In general, the current programming frameworks for CPU-GPU heterogeneous computing system do not facilitate the efficient utilization of computational resources so as to further improve the performance and reduce the energy consumption. Several researches have been carried out to improve the resource utilization and reduce the energy consumption of heterogeneous systems by cooperatively performing the computation on both multicore CPUs and GPUs. In this survey paper we review the various techniques available for CPU-GPU cooperative computing.

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

The use of GPUs for general purpose computation has increased dramatically in the past years due to the rising demands of computing power and their tremendous computing capacity at low cost. Hence, new programming models have been developed to integrate these accelerators with high-level programming languages, giving place to heterogeneous computing systems. Unfortunately, this heterogeneity is also exposed to the programmer complicating its exploitation. This paper presents a new technique to automatically rewrite sequential programs into a parallel counterpart targeting GPU-based heterogeneous systems. The original source code is analyzed through domain-independent computational kernels, which hide the complexity of the implementation details by presenting a non-statement-based, high-level, hierarchical representation of the application. Next, a locality-aware technique based on standard compiler transformations is applied to the original code through OpenHMPP directives. Two representative case studies from scientific applications have been selected: the three-dimensional discrete convolution and the simple-precision general matrix multiplication. The effectiveness of our technique is corroborated by a performance evaluation on NVIDIA GPUs.