Recently, deep learning has been an area of intense researching. However, as a kind of computing intensive task, deep learning highly relies on the scale of GPU memory, which is usually prohibitive and scarce. Although there are some extensive works have been proposed for dynamic GPU memory management, they are hard to be applied to systems with multiple dynamic workloads, such as in-database machine learning system. In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, with taking the multiple dynamic workloads into consideration. As far as we know, TENSILE is the first method which is designed to manage multiple workloads GPU memory using. We implement TENSILE on a deep learning framework built by ourselves, and evaluated its performance. The experiment results show that TENSILE can save more GPU memory with less extra time overhead than prior works in both single and multiple dynamic workloads scenarios.
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