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In the past, Graphics Processing Units (GPUs) were mainly used for graphics rendering. In the past 10 years, they have been redesigned and are used to accelerate a wide range of applications, including deep neural networks, image reconstruction, and cryptographic algorithms. Despite being the accelerator of choice in a number of important application domains, today's GPUs receive little attention on their security, especially their vulnerability to realistic and practical threats, such as sidechannel attacks. In this work, we present our study of side-channel vulnerability targeting a general purpose GPU. We propose and implement a side-channel power analysis methodology to extract all the last round key bytes of an AES (Advanced Encryption Standard) implementation on an NVIDIA TESLA GPU. We first analyze the challenges of capturing GPU power traces due to the degree of concurrency and underlying architectural features of a GPU, and propose techniques to overcome these challenges. We then construct an appropriate power model for the GPU. We describe effective methods to process the GPU power traces and launch a correlation power attack (CPA) on the processed data. We carefully consider the scalability of the attack with increasing degrees of parallelism, a key challenge on the GPU. Both our empirical and theoretical results show that parallel computing hardware systems such as a GPU are vulnerable to power analysis side-channel attacks, and need to be hardened against such threats.

BackgroundBiomedical image reconstruction applications require producing high fidelity images in or close to real-time. We have implemented reconstruction of three dimensional conebeam computed tomography(CBCT) with two dimensional projections. The algorithm takes slices of the target, weights and filters them to backproject the data, then creates the final 3D volume. We have implemented the algorithm using several hardware and software approaches and taken advantage of different types of parallelism in modern processors. The two hardware platforms used are a Central Processing Unit (CPU) and a heterogeneous system with a combination of CPU and GPU. On the CPU we implement serial MATLAB, parallel MATLAB, C and parallel C with OpenMP extensions. These codes are compared against the heterogeneous versions written in CUDA-C and OpenCL.FindingsOur results show that GPUs are particularly well suited to accelerating CBCT. Relative performance was evaluated on a mathematical phantom as well as on mouse data. Speedups of up to 200x are observed by using an AMD GPU compared to a parallel version in C with OpenMP constructs.ConclusionsIn this paper, we have implemented the Feldkamp-Davis-Kress algorithm, compatible with Fessler’s image reconstruction toolbox and tested it on different hardware platforms including CPU and a combination of CPU and GPU. Both NVIDIA and AMD GPUs have been used for performance evaluation. GPUs provide significant speedup over the parallel CPU version.

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Heterogeneous systems, that marry CPUs and GPUs together in a range of configurations, are quickly becoming the design paradigm for today's platforms because of their impressive parallel processing capabilities. However, in many existing heterogeneous systems, the GPU is only treated as an accelerator by the CPU, working as a slave to the CPU master. But recently we are starting to see the introduction of a new dass of deviees and changes to the system runtime model, which enable accelerators to be treated as first-dass computing deviees. To support programmability and efficiency of heterogeneous programming, the HSA foundation introduced the Heterogeneous System Arcbitecture (HSA), which defines a platform and run time architecture that provides rieh support tor OpenCL 2.0 features induding shared virtual memory, dynamie parallelism, and improved atomic operations.

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