Browsing by Author "Guo, Yi"

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    A Scalable Locality-aware Adaptive Work-stealing Scheduler for Multi-core Task Parallelism
    (2011) Guo, Yi; Sarkar, Vivek
    Recent trend has made it clear that the processor makers are committed to the multicore chip designs. The number of cores per chip is increasing, while there is little or no increase in the clock speed. This parallelism trend poses a significant and urgent challenge on computer software because programs have to be written or transformed into a multi-threaded form to take full advantage of future hardware advances. Task parallelism has been identified as one of the prerequisites for software productivity. In task parallelism, programmers focus on decomposing the problem into subcomputations that can run in parallel and leave the compiler and runtime to handle the scheduling details. This separation of concerns between task decomposition and scheduling provides productivity to the programmer but poses challenges to the runtime scheduler. Our thesis is that work-stealing schedulers with adaptive scheduling policies and locality-awareness can provide a scalable and robust runtime foundation for multicore task parallelism. We evaluate our thesis using the new Scalable Locality-aware Adaptive Work-stealing (SLAW) runtime scheduler developed for the Habanero-Java programming language, a task-parallel variant of Java. SLAW's adaptive task scheduling is motivated by the study of two common scheduling policies in a work-stealing scheduler, specifically, the work-first and the help-first policy. Both policies exhibit limitations in performance and resource usage in different situations. The variances make it hard to determine the best policy a priori. SLAW addresses these limitations by supporting both policies simultaneously and selecting policies adaptively on a per-task basis at runtime. Our results show that SLAW achieves O.98x to 9.2x speedup over the help-first scheduler and O.97x to 4.5x speedup over the work-first scheduler. Further, for large irregular parallel computations, SLAW supports data sizes and achieves performance that cannot be delivered by the use of any single fixed policy. SLAW's locality-aware scheduling framework aims to overcome the cache unfriendliness of work-stealing due to randomized stealing. The SLAW scheduler is designed for programming models where locality hints are provided to the runtime by the programmer or compiler. Our results show that locality-aware scheduling can improve performance by increasing temporal data reuse for iterative data-parallel applications.
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    Dynamic response of concrete-faced rockfill dams in rectangular canyons
    (1997) Guo, Yi; Dakoulas, Panos C.
    An analytical closed-form solution has been developed for steady-state lateral response of concrete-face rockfill dams built in rectangular canyons. The canyon is assumed to be rigid, while the dam is idealized as a three-dimensional linearly-hysteretic elastic body deforming in shear and bending. Both free and base induced oscillations are studied for various canyon geometries. A parametric study of the effects of the canyon narrowness and the dam slope on the response is undertaken. Finally, a more rigorous numerical formulation is used for verification of the closed-form solution.
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    Exploring the Relation between Contextual Social Determinants of Health and COVID-19 Occurrence and Hospitalization
    (MDPI, 2024) Chen, Aokun; Zhao, Yunpeng; Zheng, Yi; Hu, Hui; Hu, Xia; Fishe, Jennifer N.; Hogan, William R.; Shenkman, Elizabeth A.; Guo, Yi; Bian, Jiang
    It is prudent to take a unified approach to exploring how contextual social determinants of health (SDoH) relate to COVID-19 occurrence and outcomes. Poor geographically represented data and a small number of contextual SDoH examined in most previous research studies have left a knowledge gap in the relationships between contextual SDoH and COVID-19 outcomes. In this study, we linked 199 contextual SDoH factors covering 11 domains of social and built environments with electronic health records (EHRs) from a large clinical research network (CRN) in the National Patient-Centered Clinical Research Network (PCORnet) to explore the relation between contextual SDoH and COVID-19 occurrence and hospitalization. We identified 15,890 COVID-19 patients and 63,560 matched non-COVID-19 patients in Florida between January 2020 and May 2021. We adopted a two-phase multiple linear regression approach modified from that in the exposome-wide association (ExWAS) study. After removing the highly correlated SDoH variables, 86 contextual SDoH variables were included in the data analysis. Adjusting for race, ethnicity, and comorbidities, we found six contextual SDoH variables (i.e., hospital available beds and utilization, percent of vacant property, number of golf courses, and percent of minority) related to the occurrence of COVID-19, and three variables (i.e., farmers market, low access, and religion) related to the hospitalization of COVID-19. To our best knowledge, this is the first study to explore the relationship between contextual SDoH and COVID-19 occurrence and hospitalization using EHRs in a major PCORnet CRN. As an exploratory study, the causal effect of SDoH on COVID-19 outcomes will be evaluated in future studies.
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    On accelerating the searches for compilation sequences in an adaptive compiler
    (2007) Guo, Yi; Cooper, Keith D.; Subramanian, Devika
    Recent research show that adaptive compiler can produce consistent improvement over a traditional fixed-sequence compiler by conducting feedback-directed searches for good compilation sequences for specific programs, machines and performance objectives. However, such improvement is usually achieved at very high search cost. This thesis proposes two approaches to accelerate the searches for a good compilation sequence in an adaptive compiler. First, a local search algorithm, Greedy Neighbor Exploration algorithm (GNE), is proposed. It uses optimistic greedy construction and cleanup procedures to generate a richer set of meaningful variations by randomized insertion and removal of transformations. Experimental results on a range of standard benchmark suites show that GNE finds better compilation sequences in less than a quarter of the evaluations required by current search algorithms, such as genetic and hill climbing algorithms. Second, code normalization techniques are developed to hash programs and detect equivalent code. This can avoid unnecessary runs of programs.
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    The role of the left fusiform gyrus in reading: An examination of Chinese character recognition
    (2009) Guo, Yi; Burgund, Darcy
    The left fusiform gyrus is hypothesized to be selectively involved in visual word processing. Nevertheless, the particular components of reading to which this area responds is the subject of much controversy. In Experiment 1, activity in the left fusiform gyrus was measured using functional magnetic resonance imaging (fMRI) while subjects performed a phonological task with regular and irregular Chinese characters. Results exhibited greater activity for irregular than regular characters in the left fusiform gyrus, suggesting that this region is involved in the direct route of the dual-route model. In Experiment 2, activity was measured using fMRI while subjects performed phonological, semantic, and orthographic tasks with irregular Chinese characters. The left fusiform gyrus exhibited greater activity during the orthographic task than during the phonological and semantic tasks, which did not differ, suggesting that this region is involved in orthographic processing to a greater extent than phonological or semantic access.
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    Work-First and Help-First Scheduling Policies for Terminally Strict Parallel Programs
    (2008-11-13) Barik, Rajkishore; Guo, Yi; Raman, Raghavan; Sarkar, Vivek
    Multiple programming models are emerging to address an increased need for dynamic task parallelism in applications for multicore processors and shared-address space parallel computing. Examples include OpenMP 3.0, Java Concurrency Utilities, Microsoft Task Parallel Library, Intel Thread Building Blocks, Cilk, X10, Chapel, and Fortress. Scheduling algorithms based on work stealing, as embodied in Cilk’s implementation of dynamic spawn-sync parallelism, are gaining in popularity but also have inherent limitations. In this paper, we address the problem of efficient and scalable implementation of X10’s terminally strict async-finish task parallelism, which is more general than Cilk’s fully strict spawn-sync parallelism. We introduce a new work-stealing scheduler with compiler support for async-finish task parallelism that can accommodate both work-first and help-first scheduling policies. Performance results on two different multicore SMP platforms show significant improvements due to our new work-stealing algorithm compared to the existing work-sharing scheduler for X10, and also provide insight on scenarios in which the help-first policy yields better results than the work-first policy and vice versa.