35 research outputs found

    Exploring Multi-Level Parallelism For Graph-Based Applications Via Algorithm And System Co-Design

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    Graph processing is at the heart of many modern applications where graphs are used as the basic data structure to represent the entities of interest and the relationships between them. Improving the performance of graph-based applications, especially using parallelism techniques, has drawn significant interest both in academia and industry. On the one hand, modern CPU architectures are able to provide massive computational power by using sophisticated memory hierarchy and multi-level parallelism, including thread-level parallelism, data-level parallelism, etc. On the other hand, graph processing workloads are notoriously challenging for achieving high performance due to their irregular computation pattern and unpredictable control flow. Therefore, how to accelerate the performance of graph-based applications using parallelism is still an open question. This dissertation focuses on providing high performance for graph-based applications. To take full advantage of multi-level parallelism resources provided by CPUs, this dissertation studies the characteristics of graph-based applications and matches their parallel solutions with the underlying hardware via algorithm and system co-design. This dissertation divides graph-based applications into three categories: typical graph algorithms, sequential graph-based applications, and applications with graph-based solutions. The first category comprises typical graph algorithms with available parallel solutions. This dissertation proposes GraphPhi as a new approach to graph processing on emerging Intel Xeon Phi-like architectures. The second category includes specialized graph applications without nontrivial parallel solutions. This dissertation studies a state-of-the-art 2-hop labeling approach named Pruned Landmark Labeling (PLL). This dissertation proposes Batched Vertex-Centric PLL (BVC-PLL), which breaks PLL\u27s inherent dependencies and parallelizes it in a scalable way. The third category includes applications that rely on graph-based solutions. This dissertation studies the sequential search algorithm for the graph-based indexing methods used for the Approximate Nearest Neighbor Search (ANNS) problem. This dissertation proposes Speed-ANN, a parallel similarity search algorithm that reveals hidden intra-query parallelism to accelerate the search speed while fulfilling the high accuracy requirement. Moreover, this dissertation further explores the optimization opportunities for computational graph-based deep neural network inference running on tiny devices, specifically microcontrollers (MCUs). Altogether, this dissertation studies graph-based applications and improves their performance by providing solutions of multi-level parallelism via algorithm and system co-design to match them with the underlying multi-core CPU architectures

    Reducing the burden of parallel loop schedulers for many-core processors

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    Funder: FP7 People: Marie‐Curie Actions; Id: http://dx.doi.org/10.13039/100011264; Grant(s): 327744Summary: As core counts in processors increases, it becomes harder to schedule and distribute work in a timely and scalable manner. This article enhances the scalability of parallel loop schedulers by specializing schedulers for fine‐grain loops. We propose a low‐overhead work distribution mechanism for a static scheduler that uses no atomic operations. We integrate our static scheduler with the Intel OpenMP and Cilkplus parallel task schedulers to build hybrid schedulers. Compiler support enables efficient reductions for Cilk, without changing the programming interface of Cilk reducers. Detailed, quantitative measurements demonstrate that our techniques achieve scalable performance on a 48‐core machine and the scheduling overhead is 43% lower than Intel OpenMP and 12.1× lower than Cilk. We demonstrate consistent performance improvements on a range of HPC and data analytics codes. Performance gains are more important as loops become finer‐grain and thread counts increase. We observe consistently 16%–30% speedup on 48 threads, with a peak of 2.8× speedup

    University of Maine Undergraduate Catalog, 2020-2021, part 2

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    The second part (of two) of the undergraduate catalog for the 2020-2021 academic year includes an introduction, the academic calendars, general information about the university, and sections on attending, facilities and centers, and colleges and academic programs including the Colleges of Business, Public Policy and Health, Education and Development, Engineering, Liberal Arts and Sciences, and Natural Sciences, Forestry and Agriculture

    EVAQ: Person-Specific Evacuation Simulation for Large Crowd Egress Analysis

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    Timely crowd evacuation in life-threatening situations such as fire emergency or terrorist attack is a significant concern for authorities and first responders. An individual’s fate in this kind of situation is highly dependent on a host of factors, especially (i) agent dynamics: how the individual selects and executes an egress strategy, (ii) hazard dynamics: how hazards propagate (e.g., fire and smoke spread, lone wolf attacker moves) and impair the surrounding environment with time, (iii) intervention dynamics: how first responders intervene (e.g., firefighters spread repellents) to recover environment. This thesis presents EVAQ, a simulation modeling framework for evaluating the impact of these factors on the likelihood of survival in an emergency evacuation. The framework captures the effect of personal traits and physical habitat parameters on occupants’ decision-making. In particular, personal (i.e., age, gender, disability) and interpersonal (i.e., agent-agent interactions) attributes, as well as an individual’s situational awareness are parameterized in a deteriorating environment considering different exit layouts and physical constraints. Further, the framework supports a variety of hazard propagation schemes (e.g., fire spreading in a given direction, lone wolf attacker targeting individuals), and intervene schemes (e.g., firefighters spreading repellents, police catch the attacker) to support a wide range of emergency evacuation scenarios. The application of EVAQ to crowd egress planning in an airport terminal and a shopping mall in the fire emergency is presented in this thesis, and results are discussed. Result shows that the likelihood of survival decreases with a decrease in availability of the nearest exits and a resulting increase in congestions in the environment. Also, it is observed that the incorporation of group behavior increases the likelihood of survival for children, as well as elderly and disabled people. In addition, several verifications and validation tests are performed to assess the reliability and integrity of EVAQ in comparison with existing evacuation modeling tools. As personalized sensing and information delivery platforms are becoming more ubiquitous, findings of this work are ultimately sought to assist in developing and executing more robust and adaptive emergency mapping and evacuation plans, ultimately aimed at promoting people’s lives and wellbeing

    Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable

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    There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 20 important graph problems. We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We have made the implementations developed in this work publicly-available as the Graph-Based Benchmark Suite (GBBS).Comment: This is the full version of the paper appearing in the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 201

    High-Performance and Power-Aware Graph Processing on GPUs

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    Graphs are a common representation in many problem domains, including engineering, finance, medicine, and scientific applications. Different problems map to very large graphs, often involving millions of vertices. Even though very efficient sequential implementations of graph algorithms exist, they become impractical when applied on such actual very large graphs. On the other hand, graphics processing units (GPUs) have become widespread architectures as they provide massive parallelism at low cost. Parallel execution on GPUs may achieve speedup up to three orders of magnitude with respect to the sequential counterparts. Nevertheless, accelerating efficient and optimized sequential algorithms and porting (i.e., parallelizing) their implementation to such many-core architectures is a very challenging task. The task is made even harder since energy and power consumption are becoming constraints in addition, or in same case as an alternative, to performance. This work aims at developing a platform that provides (I) a library of parallel, efficient, and tunable implementations of the most important graph algorithms for GPUs, and (II) an advanced profiling model to analyze both performance and power consumption of the algorithm implementations. The platform goal is twofold. Through the library, it aims at saving developing effort in the parallelization task through a primitive-based approach. Through the profiling framework, it aims at customizing such primitives by considering both the architectural details and the target efficiency metrics (i.e., performance or power)
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