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The general-purpose computing on graphic processing units (GPGPUs) becomes increasingly popular due to its high computational throughput for data parallel applications. Modern GPU architectures have limited capability for error detection and fault tolerance since they are originally designed for graphics processing. However, the rigorous execution correctness is required for general-purpose applications, which makes reliability a growing concern in the GPGPU architecture design. With CMOS processing technologies continuously scaling down to the nano-scale, on-chip soft error rate (SER) has been predicted to increase exponentially. GPGPUs with hundreds of cores integrated into a single chip are prone to manifest high SER. This paper explores a first step to model and characterize GPGPU reliability in light of soft errors. We develop GPGPU-SODA (GPGPU SOftware Dependability Analysis), a framework to estimate the soft-error vulnerability of GPGPU microarchitecture. By using GPGPU-SODA, we observe that several microarchitecture structures in GPGPUs exhibit high soft-error susceptibility, and the structure vulnerability is sensitive to the workload characteristics (e.g. branch divergences, memory access pattern). We further investigate the impact of several architectural optimizations on GPU soft-error robustness. For example, we find that increasing the number of threads supported by GPU significantly affects the GPGPU soft-error robustness. However, changing the warp scheduling policy has little impact on the structure vulnerability. The observations made in this study provide designers the useful guidance to build resilient GPGPUs: a comprehensive resiliency solution for GPGPUs should consider the entire GPGPU design instead of solely focusing on a particular structure.  相似文献   
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《Microelectronics Reliability》2014,54(11):2621-2628
Given their high computational power, General Purpose Graphics Processing Units (GPGPUs) are increasingly adopted: GPGPUs have begun to be preferred to CPUs for several computationally intensive applications, not necessarily related to computer graphics. However, their sensitivity to radiation still requires to be fully evaluated. In this context, GPGPU data caches and shared memory have a key role since they allow to increase performance by sharing data between the parallel resources of a GPGPU and minimizing the memory accesses overhead. In this paper we present three new algorithms designed to support radiation experiments aimed at evaluating the radiation sensitivity of GPGPU data caches and shared memory. We also report the cross-section and Failure In Time results from neutron testing experiments performed on a commercial-off-the-shelf GPGPU using the proposed algorithms, with particular emphasis on the shared memory and on the L1 and L2 data caches.  相似文献   
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使用计算流体力学(Computational Fluid Dynamics,CFD)数值方法对熔盐堆堆芯的流动和热传导等相关物理问题进行模拟求解,需要大量的计算时间。利用图形处理器(Graphics Processing Unit,GPU)加速技术对开源CFD软件Code_Saturne进行二次开发,研究求解熔盐堆堆芯流场的GPU并行算法。采用OpenACC语言在GPU上实现了向量运算、矩阵向量相乘等基本线性代数运算,从而实现预处理共轭梯度法(Preconditioned Conjugate Gradients,PCG)的GPU并行算法,并使用该算法求解压力状态方程。模拟了方腔驱动流模型及带下降段的熔盐堆堆芯模型的流场分布。结果表明,GPU加速后的软件与原版软件的结果一致,但计算时间更少,证明了GPU算法的正确性及有效的加速性。  相似文献   
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Osteoporosis is a disease that affects a growing number of people by increasing the fragility of their bones. To improve the understanding of the bone quality, large scale computer simulations are applied. A fast, scalable and memory efficient solver for such problems is ParOSol. It uses the preconditioned conjugate gradient algorithm with a multigrid preconditioner. A modification of ParOSol is presented that profits from the exorbitant compute capabilities of recent general-purpose graphics processing units (GPGPUs). Adaptations of data structures for the GPGPU are discussed. The fastest implementation on a GPGPU achieves a speedup of more than five compared with the CPU implementation and scales from 1 to at least 256 GPGPUs.  相似文献   
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Graphics processing units (GPUs) have an SIMD architecture and have been widely used recently as powerful general-purpose co-processors for the CPU. In this paper, we investigate efficient GPU-based data cubing because the most frequent operation in data cube computation is aggregation, which is an expensive operation well suited for SIMD parallel processors. H-tree is a hyper-linked tree structure used in both top-k H-cubing and the stream cube. Fast H-tree construction, update and real-time query response are crucial in many OLAP applications. We design highly efficient GPU-based parallel algorithms for these H-tree based data cube operations. This has been made possible by taking effective methods, such as parallel primitives for segmented data and efficient memory access patterns, to achieve load balance on the GPU while hiding memory access latency. As a result, our GPU algorithms can often achieve more than an order of magnitude speedup when compared with their sequential counterparts on a single CPU. To the best of our knowledge, this is the first attempt to develop parallel data cubing algorithms on graphics processors.  相似文献   
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