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Big data powered Deep Learning (DL) and its applications have blossomed in recent years, fueled by three technological trends: a large amount of digitized data openly accessible, a growing number of DL software frameworks in open source and commercial markets, and a selection of affordable parallel computing hardware devices. However, no single DL framework, to date, dominates in terms of performance and accuracy even for baseline classification tasks on standard datasets, making the selection of a DL framework an overwhelming task. This paper takes a holistic approach to conduct empirical comparison and analysis of four representative DL frameworks with three unique contributions. First, given a selection of CPU-GPU configurations, we show that for a specific DL framework, different configurations of its hyper-parameters may have a significant impact on both performance and accuracy of DL applications. Second, to the best of our knowledge, this study is the first to identify the opportunities for improving the training time performance and the accuracy of DL frameworks by configuring parallel computing libraries and tuning individual and multiple hyper-parameters. Third, we also conduct a comparative measurement study on the resource consumption patterns of four DL frameworks and their performance and accuracy implications, including CPU and memory usage, and their correlations to varying settings of hyper-parameters under different configuration combinations of hardware, parallel computing libraries. We argue that this measurement study provides in-depth empirical comparison and analysis of four representative DL frameworks, and offers practical guidance for service providers to deploying and delivering DL as a Service (DLaaS) and for application developers and DLaaS consumers to select the right DL frameworks for the right DL workloads.

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Fatty acid oxidation and subsequent ketogenesis is one of the major mechanisms to maintain hepatic lipid homeostasis under fasting conditions. Fasting hormone glucagon has been shown to stimulate ketone body production through activation of PPARα; however, the signal pathway linking glucagon to PPARα is largely undiscovered. Here we report that a SIK2-p300-PPARα cascade mediates glucagon’s effect on ketogenesis. p300 interacts with PPARα through a conserved LXXLL motif and enhances its transcriptional activity. SIK2 disrupts p300-PPARα interaction by direct phosphorylation of p300 at Ser89, which in turn decreases PPARα-mediated ketogenic gene expression. Moreover, SIK2 phosphorylation defective p300 (p300 S89A) shows increased interaction with PPARα and abolishes suppression of SIK2 on PPARα-mediated ketogenic gene expression in liver. Taken together, our results unveil the signal pathway that mediates fasting induced ketogenesis to maintain hepatic lipid homeostasis.

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