Accelerating NTRUEncrypt for in-browser cryptography utilising graphical processing units and WebGL

One of the challenges encryption faces is it is computationally intensive and therefore slow, it is vital to find faster methods to accelerate modern encryption algorithms to keep performance high whilst also preserving information security. Users often do not want to wait for applications to become responsive, applications on limited devices such as mobiles often compromise security in order to keep execution times quick. Often they use algorithms and key sizes which are not considered cryptographically secure in order to maintain a smooth user experience. Emerging approaches have begun using a devices Graphics Processing Unit (GPU) to offload some of the computational burden from the Central Processing Unit (CPU) in an effort to parallelize and accelerate the encryption algorithms. Programming for a GPU often involves the use of CUDA or OpenCL programming, however these approaches are platform dependant. This research focuses on utilizing a GPU to perform in-browser cryptography using WebGL and JavaScript. This allows any GPU-enabled device capable of launching an OpenGL compatible browser to perform GPU accelerated cryptography. A GPU based implementation of the NTRUEncrypt algorithm was created and tested against a CPU based version on a range of hardware devices with results, challenges and limitations discussed

A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest `link' in the security `chain'. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and differences. More than informing users and researchers about GPU security techniques, this survey aims to increase their awareness about GPU security vulnerabilities and potential countermeasures

Survey and future trends of efficient cryptographic function implementations on GPGPUs

Many standard cryptographic functions are designed to benefit from hardware specific implementations. As a result, there have been a large number of highly efficient ASIC and FPGA hardware based implementations of standard cryptographic functions. Previously, hardware accelerated devices were only available to a limited set of users. General Purpose Graphic Processing Units (GPGPUs) have become a standard consumer item and have demonstrated orders of magnitude performance improvements for general purpose computation, including cryptographic functions. This paper reviews the current and future trends in GPU technology, and examines its potential impact on current cryptographic practice

GPUs as Storage System Accelerators

Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context, we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing, and introduce techniques to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201

Super Calculator using Compute Unified Device Architecture (CUDA)

Scientific computation requires a great amount of computing power especially in floating-point operation but a high-end multi-cores processor is currently limited in terms of floating point operation performance and parallelization. Recent technological advancement has made parallel computing technically and financially feasible using Compute Unified Device Architecture (CUDA) developed by NVIDIA. This research focuses on measuring the performance of CUDA and implementing CUDA for a scientific computation involving the process of porting the source code from CPU to GPU using direct integration technique. The ported source code is then optimized by managing the resources to achieve performance gain over CPU. It is found that CUDA is able to boost the performance of the system up to 69 times in Parboil Benchmark Suite. Successful attempt at porting Serpent encryption algorithm and Lattice Boltzmann Method provided up to 7 times throughput performance gain and up to 10 times execution time performance gain respectively over the CPU. Direct integration guideline for porting the source code is then produced based on the two implementations