Theoretical construction and application of fine-grained cycle sharing technique for GPU grid

我们开发了一个细粒度的周期共享技术的计算网格系统，共享网络上的计算资源。我们的技术是基于图形处理单元（GPU）的多任务机制。我们将我们的技术应用于生物信息学问题，称为同源性搜索，并证明在编辑电子文档时可以加速科学应用。建立了GPU的空闲周期模型，提出了一种根据空闲周期长度分布选择计算资源的方法。

We developed a fine-grained cycle sharing technique for computational grid systems, which share computational resources on the network. Our technique is based on a multitasking mechanism for graphics processing units (GPUs). We applied our technique to a bioinformatics problem, called homology search, and demonstrated that scientific applications can be accelerated while editing electronic documents. We also constructed an idle cycle model for GPUs and developed a method that selects computational resources according to the distribution of idle period lengths.