swTVM: Exploring the Automated Compilation for Deep Learning on Sunway Architecture

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the exiting deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific and deep learning applications. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient code for deep learning application on Sunway. The experimental results show the ability of swTVM to automatically generate code for various deep neural network models on Sunway. The performance of automatically generated code for AlexNet and VGG-19 by swTVM achieves 6.71x and 2.45x speedup on average than hand-optimized OpenACC implementations on convolution and fully connected layers respectively. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and high performance architecture particularly with productivity and efficiency in mind. We would like to open source the implementation so that more people can embrace the power of deep learning compiler and Sunway many-core processor

LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection

The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as high-dimensional and noisy log data, class imbalance, generalization, and model interpretability. Recently, ChatGPT has shown promising results in various domains. However, there is still a lack of study on the application of ChatGPT for log-based anomaly detection. In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By leveraging the ChatGPT's language interpretation capabilities, LogGPT aims to explore the transferability of knowledge from large-scale corpora to log-based anomaly detection. We conduct experiments to evaluate the performance of LogGPT and compare it with three deep learning-based methods on BGL and Spirit datasets. LogGPT shows promising results and has good interpretability. This study provides preliminary insights into prompt-based models, such as ChatGPT, for the log-based anomaly detection task

Intelligent-Unrolling: Exploiting Regular Patterns in Irregular Applications

Modern optimizing compilers are able to exploit memory access or computation patterns to generate vectorization codes. However, such patterns in irregular applications are unknown until runtime due to the input dependence. Thus, either compiler's static optimization or profile-guided optimization based on specific inputs cannot predict the patterns for any common input, which leads to suboptimal code generation. To address this challenge, we develop Intelligent-Unroll, a framework to automatically optimize irregular applications with vectorization. Intelligent-Unroll allows the users to depict the computation task using \textit{code seed} with the memory access and computation patterns represented in \textit{feature table} and \textit{information-code tree}, and generates highly efficient codes. Furthermore, Intelligent-Unroll employs several novel optimization techniques to optimize reduction operations and gather/scatter instructions. We evaluate Intelligent-Unroll with sparse matrix-vector multiplication (SpMV) and graph applications. Experimental results show that Intelligent-Unroll is able to generate more efficient vectorization codes compared to the state-of-the-art implementations

An animal model of SARS produced by infection of Macaca mulatta with SARS coronavirus.

A new SARS animal model was established by inoculating SARS coronavirus (SARS-CoV) into rhesus macaques (Macaca mulatta) through the nasal cavity. Pathological pulmonary changes were successively detected on days 5-60 after virus inoculation. All eight animals showed a transient fever 2-3 days after inoculation. Immunological, molecular biological, and pathological studies support the establishment of this SARS animal model. Firstly, SARS-CoV-specific IgGs were detected in the sera of macaques from 11 to 60 days after inoculation. Secondly, SARS-CoV RNA could be detected in pharyngeal swab samples using nested RT-PCR in all infected animals from 5 days after virus inoculation. Finally, histopathological changes of interstitial pneumonia were found in the lungs during the 60 days after viral inoculation: these changes were less marked at later time points, indicating that an active healing process together with resolution of an acute inflammatory response was taking place in these animals. This animal model should provide insight into the mechanisms of SARS-CoV-related pulmonary disease and greatly facilitate the development of vaccines and therapeutics against SARS

Prognostic value of the FUT family in acute myeloid leukemia

Genetic abnormalities are more frequently viewed as prognostic markers in acute myeloid leukemia (AML) in recent years. Fucosylation, catalyzed by fucosyltransferases (FUTs), is a post-translational modification that widely exists in cancer cells. However, the expression and clinical implication of the FUT family (FUT1-11) in AML has not been investigated. From the Cancer Genome Atlas database, a total of 155 AML patients with complete clinical characteristics and FUT1-11 expression data were included in our study. In patients who received chemotherapy alone showed that high expression levels of FUT3, FUT6, and FUT7 had adverse effects on event-free survival (EFS) and overall survival (OS) (all P <0.05), whereas high FUT4 expression had favorable effects on EFS and OS (all P <0.01). However, in the allogeneic hematopoietic stem cell transplantation (allo-HSCT) group, we only found a significant difference in EFS between the high and low FUT3 expression subgroups (P = 0.047), while other FUT members had no effect on survival. Multivariate analysis confirmed that high FUT4 expression was an independent favorable prognostic factor for both EFS (HR = 0.423, P = 0.001) and OS (HR = 0.398, P <0.001), whereas high FUT6 expression was an independent risk factor for both EFS (HR = 1.871, P = 0.017) and OS (HR = 1.729, P = 0.028) in patients who received chemotherapy alone. Moreover, we found that patients with low FUT4 and high FUT6 expressions had the shortest EFS and OS (P <0.05). Our study suggests that high expressions of FUT3/6/7 predict poor prognosis, high FUT4 expression indicates good prognosis in AML; FUT6 and FUT4 have the best prognosticating profile among them, but their effects could be neutralized by allo-HSCT