Dynamic Multi-Resolution Data Storage
Dynamic Multi-Resolution Data Storage
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DOI：
10.1145/3352460.3358282
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Yu-Ching Hu;Murtuza Lokhandwala;Te I;Hung-Wei Tseng
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文献类型：
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作者：
Yu-Ching Hu;Murtuza Lokhandwala;Te I;Hung-Wei Tseng
但是，在较少精确的数据上起作用的计算会导致计算内核的显着性能和能量成本减少。当前的Varifocal Storage，这是一种动态的多分辨率存储系统，可应对支持潜水员应用的计算机系统的性能，质量，灵活性和成本挑战需求。各种数据集。系统通过（1）将原始数据集保存在存储设备中，（2）对现有SSD控制器的功率进行动态生成较低分辨率数据集的靶向操作员。通过我们的原型SSD，结果表明，与传统的程序相比，Varifocal存储可以加快数据分辨率的调整近似计算的体系结构，VarifoCal存储将整体执行时间提高1.52倍。
Approximate computing that works on less precise data leads to significant performance gains and energy-cost reductions for compute kernels. However, without leveraging the full-stack design of computer systems, modern computer architectures undermine the potential of approximate computing. In this paper, we present Varifocal Storage, a dynamic multi-resolution storage system that tackles challenges in performance, quality, flexibility and cost for computer systems supporting diverse application demands. Varifocal Storage dynamically adjusts the dataset resolution within a storage device, thereby mitigating the performance bottleneck of exchanging/preparing data for approximate compute kernels. Varifocal Storage introduces Autofocus and iFilter mechanisms to provide quality control inside the storage device and make programs more adaptive to diverse datasets. Varifocal Storage also offers flexible, efficient support for approximate and exact computing without exceeding the costs of conventional storage systems by (1) saving the raw dataset in the storage device, and (2) targeting operators that complement the power of existing SSD controllers to dynamically generate lower-resolution datasets. We evaluate the performance of Varifocal Storage by running applications on a heterogeneous computer with our prototype SSD. The results show that Varifocal Storage can speed up data resolution adjustments by 2.02× or 1.74× without programmer input. Compared to conventional approximate-computing architectures, Varifocal Storage speeds up the overall execution time by 1.52×.