Doctor of Philosophy

dissertationAs the base of the software stack, system-level software is expected to provide ecient and scalable storage, communication, security and resource management functionalities. However, there are many computationally expensive functionalities at the system level, such as encryption, packet inspection, and error correction. All of these require substantial computing power. What's more, today's application workloads have entered gigabyte and terabyte scales, which demand even more computing power. To solve the rapidly increased computing power demand at the system level, this dissertation proposes using parallel graphics pro- cessing units (GPUs) in system software. GPUs excel at parallel computing, and also have a much faster development trend in parallel performance than central processing units (CPUs). However, system-level software has been originally designed to be latency-oriented. GPUs are designed for long-running computation and large-scale data processing, which are throughput-oriented. Such mismatch makes it dicult to t the system-level software with the GPUs. This dissertation presents generic principles of system-level GPU computing developed during the process of creating our two general frameworks for integrating GPU computing in storage and network packet processing. The principles are generic design techniques and abstractions to deal with common system-level GPU computing challenges. Those principles have been evaluated in concrete cases including storage and network packet processing applications that have been augmented with GPU computing. The signicant performance improvement found in the evaluation shows the eectiveness and eciency of the proposed techniques and abstractions. This dissertation also presents a literature survey of the relatively young system-level GPU computing area, to introduce the state of the art in both applications and techniques, and also their future potentials

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Pruning, Pushdown Exception-Flow Analysis

Statically reasoning in the presence of exceptions and about the effects of exceptions is challenging: exception-flows are mutually determined by traditional control-flow and points-to analyses. We tackle the challenge of analyzing exception-flows from two angles. First, from the angle of pruning control-flows (both normal and exceptional), we derive a pushdown framework for an object-oriented language with full-featured exceptions. Unlike traditional analyses, it allows precise matching of throwers to catchers. Second, from the angle of pruning points-to information, we generalize abstract garbage collection to object-oriented programs and enhance it with liveness analysis. We then seamlessly weave the techniques into enhanced reachability computation, yielding highly precise exception-flow analysis, without becoming intractable, even for large applications. We evaluate our pruned, pushdown exception-flow analysis, comparing it with an established analysis on large scale standard Java benchmarks. The results show that our analysis significantly improves analysis precision over traditional analysis within a reasonable analysis time.Comment: 14th IEEE International Working Conference on Source Code Analysis and Manipulatio

Genetic variation in eight Chinese cattle breeds based on the analysis of microsatellite markers

Genetic variability and genetic relationships were investigated among eight Chinese cattle breeds using 12 microsatellite markers. Three hundred and fifty-two alleles were detected and the average number of alleles per locus ranged from 8.33 ± 1.67 in the Jiaxian breed to 21.33 ± 5.60 in the Qinchuan breed with a mean value of 13.91. The total number of alleles per microsatellite ranged from 21 (INRA005, HEL1) to 40 (HEL13), with a mean of 29.33 per locus. The fixation indices at the 12 loci in the eight breeds were very low with a mean of 0.006. A principal components analysis and the construction of a neighborjoining tree showed that these eight Chinese cattle breeds cluster into three groups i.e. the Yanbian andChineseHolstein, theNanyang and Jiaxian, and the four remaining breeds.This clustering agrees with the origin and geographical distributions of these Chinese breeds

Road Side Unit-Assisted Learning-Based Partial Task Offloading for Vehicular Edge Computing System

The rapid development of vehicular networks creates diverse ultra-low latency constrained and computation-intensive applications, which bring challenges to both communication and computation capabilities of the vehicles and their transmission. By offloading tasks to the edge servers or vehicles in the neighbourhood, vehicular edge computing (VEC) provides a cost-efficient solution to this problem. However, the channel state information and network structure in the vehicular network varies fast because of the inherent mobility of vehicle nodes, which brings an extra challenge to task offloading. To address this challenge, we formulate the task offloading in vehicular network as a multi-armed bandit (MAB) problem and propose a novel road side unit (RSU)-assisted learning-based partial task offloading (RALPTO) algorithm. The algorithm enables vehicle nodes to learn the delay performance of the service provider while offloading tasks. Specifically, the RSU could assist the learning process by sharing the learning information among vehicle nodes, which improves the adaptability of the algorithm to the time-varying networks. Simulation results demonstrate that the proposed algorithm achieves lower delay and better learning performance compared with the benchmark algorithms

Fast Flow Analysis with Godel Hashes

Abstract—Flow analysis, such as control-flow, data-flow, and exception-flow analysis, usually depends on relational operations on flow sets. Unfortunately, set related operations, such as inclusion and equality, are usually very expensive. They can easily take more than 97 % of the total analyzing time, even in a very simple analysis. We attack this performance bottleneck by proposing Gödel hashes to enable fast and precise flow analysis. Gödel hashes is an ultra compact, partial-order-preserving, fast and perfect hashing mechanism, inspired by the proofs of Gödel’s incompleteness theorems. Compared with array-, tree-, traditional hash-, and bit vector-backed set implementations, we find Gödel hashes to be tens or even hundreds of times faster for performance in the critical operations of inclusion and equality. We apply Gödel hashes in real-world analysis for object-oriented programs. The instrumented analysis is tens of times faster than the one with original data structures on DaCapo benchmarks. I

Intrinsic Defects in LiMn2_{2}O4_{4}: First-Principles Calculations

Spinel LiMn$_{2}$O$_{4}$ has attracted wide attention due to its advantages of a high-voltage plateau, good capacity, environmental friendliness, and low cost. Due to different experimental synthesis methods and conditions, there are many intrinsic point defects in LiMn$_{2}$O$_{4}$. By means of first-principles calculations based on a reasonable magnetic configuration, we studied the formation energies, local structures, and charge compensation mechanism of intrinsic point defects in LiMn$_{2}$O$_{4}$. The formation energies of defects under the assumed O-rich equilibrium conditions were examined. It was found that O, Li, and Mn vacancies, Mn and Li antisites, and Li interstitial could appear in the lattice at some equilibrium conditions, but Mn interstitial is hard to form. The charge was compensated mainly by adjusting the oxidation state of Mn around the defect, except for the defects at the 8a Wyckoff site. The binding energies between point defects were calculated to shed light on the clustering of point defects. Furthermore, the diffusion of Li ions around the defects was discussed. Cation antisites led to a decrease of the Li diffusion barrier but O vacancy caused an increase of the barrier. This study provides theoretical support for understanding point defects in spinel LiMn$_{2}$O$_{4}$