Instruction-set customization for multi-tasking embedded systems

Ph.DDOCTOR OF PHILOSOPH

Optimizing the MapReduce Framework on Intel Xeon Phi Coprocessor

With the ease-of-programming, flexibility and yet efficiency, MapReduce has become one of the most popular frameworks for building big-data applications. MapReduce was originally designed for distributed-computing, and has been extended to various architectures, e,g, multi-core CPUs, GPUs and FPGAs. In this work, we focus on optimizing the MapReduce framework on Xeon Phi, which is the latest product released by Intel based on the Many Integrated Core Architecture. To the best of our knowledge, this is the first work to optimize the MapReduce framework on the Xeon Phi. In our work, we utilize advanced features of the Xeon Phi to achieve high performance. In order to take advantage of the SIMD vector processing units, we propose a vectorization friendly technique for the map phase to assist the auto-vectorization as well as develop SIMD hash computation algorithms. Furthermore, we utilize MIMD hyper-threading to pipeline the map and reduce to improve the resource utilization. We also eliminate multiple local arrays but use low cost atomic operations on the global array for some applications, which can improve the thread scalability and data locality due to the coherent L2 caches. Finally, for a given application, our framework can either automatically detect suitable techniques to apply or provide guideline for users at compilation time. We conduct comprehensive experiments to benchmark the Xeon Phi and compare our optimized MapReduce framework with a state-of-the-art multi-core based MapReduce framework (Phoenix++). By evaluating six real-world applications, the experimental results show that our optimized framework is 1.2X to 38X faster than Phoenix++ for various applications on the Xeon Phi

Probabilistic Multilevel Clustering via Composite Transportation Distance

We propose a novel probabilistic approach to multilevel clustering problems based on composite transportation distance, which is a variant of transportation distance where the underlying metric is Kullback-Leibler divergence. Our method involves solving a joint optimization problem over spaces of probability measures to simultaneously discover grouping structures within groups and among groups. By exploiting the connection of our method to the problem of finding composite transportation barycenters, we develop fast and efficient optimization algorithms even for potentially large-scale multilevel datasets. Finally, we present experimental results with both synthetic and real data to demonstrate the efficiency and scalability of the proposed approach.Comment: 25 pages, 3 figure

Neural Sinkhorn Topic Model

In this paper, we present a new topic modelling approach via the theory of optimal transport (OT). Specifically, we present a document with two distributions: a distribution over the words (doc-word distribution) and a distribution over the topics (doc-topic distribution). For one document, the doc-word distribution is the observed, sparse, low-level representation of the content, while the doc-topic distribution is the latent, dense, high-level one of the same content. Learning a topic model can then be viewed as a process of minimising the transportation of the semantic information from one distribution to the other. This new viewpoint leads to a novel OT-based topic modelling framework, which enjoys appealing simplicity, effectiveness, and efficiency. Extensive experiments show that our framework significantly outperforms several state-of-the-art models in terms of both topic quality and document representations

Topic Modelling Meets Deep Neural Networks: A Survey

Topic modelling has been a successful technique for text analysis for almost twenty years. When topic modelling met deep neural networks, there emerged a new and increasingly popular research area, neural topic models, with over a hundred models developed and a wide range of applications in neural language understanding such as text generation, summarisation and language models. There is a need to summarise research developments and discuss open problems and future directions. In this paper, we provide a focused yet comprehensive overview of neural topic models for interested researchers in the AI community, so as to facilitate them to navigate and innovate in this fast-growing research area. To the best of our knowledge, ours is the first review focusing on this specific topic.Comment: A review on Neural Topic Model

Visual and biochemical evidence of glycocalyx disruption in human dengue infection, and association with plasma leakage severity

Background: Dengue is the most common arboviral infection globally; a minority of patients develop shock due to profound plasma leak through a disrupted endothelial barrier. Understanding of the pathophysiology underlying plasma leak is incomplete, but emerging evidence indicates a key role for degradation of the endothelial glycocalyx. Methods: We conducted an observational study in Vietnam to evaluate the sublingual microcirculation using sidestream darkfield imaging in (1) outpatients with confirmed dengue (2) patients hospitalized with dengue and (3) outpatients with other febrile illness (OFI). We estimated the glycocalyx degradation by measuring the perfused boundary region (PBR hf) and an overall microvascular health score (MVHS) with the software application GlycoCheckTM at enrolment, 48 h later and hospital discharge/defervescence. We measured plasma syndecan1 and endocan at the same time-points. We compared PBR hf, MVHS, syndecan1 and endocan, between (1) outpatients with confirmed dengue vs. OFI and (2) patients with dengue subdivided by clinical severity of plasma leak. Results: We included 75 patients with dengue (41 outpatients, 15 inpatients, 19 in intensive care) and 12 outpatients with OFI. Images from 45 patients were analyzed using GlycoCheckTM. There was no significant difference in PBR hf or MVHS between outpatients with dengue and OFI. Median plasma syndecan1 was not significantly different in outpatients with dengue vs. OFI, while median plasma endocan was significantly lower among patients with dengue vs. OFI during the critical phase. In patients with dengue, PBR hf was higher in patients with Grade 2 vs. Grade 0 plasma leakage during the critical phase (PBR hf 1.96 vs. 1.36 μm for Grade 2 vs. Grade 0 plasma leakage on days 4–6, respectively, p < 0.001). Median levels of plasma syndecan1 and endocan were higher in Grade 2 vs. Grade 0 plasma leakage, especially during the critical phase (Syndecan1 2,613.8 vs. 125.9 ng/ml for Grade 2 vs. Grade 0 plasma leakage on days 4–6, respectively, p < 0.001, and endocan 3.21 vs. 0.16 ng/ml for Grade 2 vs. Grade 0 plasma leakage on days 4–6, respectively). Conclusions: We present the first human in vivo evidence of glycocalyx disruption in dengue, with worse visual glycocalyx damage and higher plasma degradation products associated with more severe plasma leak