A lightweight incremental analysis and profiling framework for embedded devices

International audienceEmbedded systems such as mobile devices are currently ubiquitous. The performance potential of these devices is rapidly improving by incorporating multi-core and GPU technologies, and is rapidly catching up with the workstation platforms. Nevertheless, the heterogeneity of the underlying hardware as well as the low-power constraints severely limit performance portability. In this paper we consider the case of leveraging JIT compilers to provide portable parallelization while hiding the corresponding expensive runtime analysis. We propose a novel lightweight JIT framework that exploits the device idle time and the large storage space generally available on these devices. The framework performs 'incremental' analysis while the processor is idle (such as during charging time), and exploits the storage space to cache intermediate analysis results. Such approach requires reengineering existing complex optimization analysis methods. For this paper, we focus on the traditional loop parallelization analysis, and implement a working prototype into the LLVM framework, integrating a lightweight dynamic profiling method to identify hotspots. Initial results demonstrate the low overhead of our method for parallelizing simple loops on an embedded GPU

Bio-diagnostic performances of microRNAs set related to DNA damage response pathway among hepatitis C virus-associated hepatocellular carcinoma patients

Abstract Background Up to date, a well-defined microRNAs (miRNAs) profile involved in hepatocellular carcinoma (HCC) pathogenesis remains indecisive. Thus, employing miRNAs for HCC diagnosis is demanded for early therapeutic interventions. We aimed to evaluate the usage of miRNAs set related to the SuperPath: miRNAs involved in DNA damage response pathway as effective biomarkers for HCV-related HCC diagnosis. Results The study enrolled 97 patients with HCV-related HCC, 84 with hepatitis C virus (HCV), 97 with liver cirrhosis (LC), and 84 healthy individuals. Serum miRNA-23a, miRNA-203, miRNA-100-5p, and miRNA-16 were quantified using qRT-PCR experiments, AFP and routine LFTs were estimated via standard techniques. Pathway enrichment analysis along with the construction of miRNAs regulatory network were performed. With respect to healthy individuals, miRNA-203, miRNA-100-5p, and miRNA-16 were significantly downregulated in HCC, HCV, and LC groups, while miRNA-23a showed significant upregulation (p  5 cm. Additionally, the diagnostic performance of miRNA-23a expression level at a selected cut-off value of 3.99 overtakes AFP, while expressions of miR-203, miRNA-100-5p, and miRNA-16 represent poor diagnostic outcomes. Conclusions Keeping in mind the individual variability and high level of heterogeneity in HCC, our data revealed the diagnostic value of miRNA-23a expression in HCV-related HCC patients. Further extra in silico HCC-specific microRNAs sets are demanded in diagnosis