Information literacy has gradually become one of the necessary qualities in current and future safety practices. The calculation and assessment of information literacy of safety professionals is an effective way to understand their information literacy level. This paper, therefore, aims to evaluate the information literacy level of safety management personnel, for improving their ability to comprehend safety language/technology/information. Based on the theory of safety information systems and the characteristics of safety professionals, this study develops an index system to assess the information literacy level of safety professionals. The index system consists of five indexes: safety information demand consciousness, safety information acquisition ability, safety information evaluation ability, safety information utilization ability, and information ethics. According to the analytic hierarchy process method, the weight of the index can be determined. This developed method was implemented to evaluate the safety information literacy level of 40 safety professionals from four different corporations. The quantitative results of the fuzzy evaluation are in good agreement with the qualitative analysis results, indicating that the index system has excellent applicability and can be applied to the evaluation of the information literacy level of safety professionals. Besides, recommendations are put forward to improve the information literacy of safety professionals.@en

Graph processing is increasingly used in a variety of domains, from engineering to logistics and from scientific computing to online gaming. To process graphs efficiently, GPU-enabled graph-processing systems such as TOTEM and Medusa exploit the GPU or the combined CPU+GPU capabilities of a single machine. Unlike scalable distributed CPU-based systems such as Pregel and GraphX, existing GPU-enabled systems are restricted to the resources of a single machine, including the limited amount of GPU memory, and thus cannot analyze the increasingly large-scale graphs we see in practice. To address this problem, we design and implement three families of distributed heterogeneous graph-processing systems that can use both the CPUs and GPUs of multiple machines. We further focus on graph partitioning, for which we compare existing graph-partitioning policies and a new policy specifically targeted at heterogeneity. We implement all our distributed heterogeneous systems based on the programming model of the single-machine TOTEM, to which we add (1) a new communication layer for CPUs and GPUs across multiple machines to support distributed graphs, and (2) a workload partitioning method that uses offline profiling to distribute the work on the CPUs and the GPUs. We conduct a comprehensive real-world performance evaluation for all three families. To ensure representative results, we select 3 typical algorithms and 5 datasets with different characteristics. Our results include algorithm run time, performance breakdown, scalability, graph partitioning time, and comparison with other graph-processing systems. They demonstrate the feasibility of distributed heterogeneous graph processing and show evidence of the high performance that can be achieved by combining CPUs and GPUs in a distributed environment.@en

Graph processing is increasingly popular in a variety of scientific and engineering domains. Consequently, graphs and graph-processing algorithms have become increasingly more diverse. Following the big data trend in every computer-related domain, graphs have also become increasingly larger. Processing graphs is requiring more sophisticated computer systems. Important for this thesis, graph-processing systems now need to combine scalability (a grand challenge in computer science) and raw processing power (an endless race), with efficiency especially in cost and energy requirements (a difficult to define and ensure non-functional property for computer systems). New trade-offs between these elements are offered by two important trends in computer systems. First, distributed systems have grown in popularity and cost-efficiency. Second, GPUs offer an excellent performance-energy ratio and are included in most modern computers. By combining distributed CPU-based systems and non-distributed GPU-enabled systems into distributed heterogeneous systems, large-scale graph processing may become possible and efficient. However, many challenges still exist in the area of graph processing before distributed heterogeneous systems can be well understood. In this thesis, we conduct fundamental and applied research to address three major challenges in three research directions of graph processing: application (understanding new data characteristics and sharing graphs), knowledge (evaluating and comparing the performance of various graphprocessing systems), and design (designing new partitioning policies and entire graphprocessing systems that can use both CPUs and GPUs on multiple machines).@en

In recent years, many distributed graph-processing systems have been designed and developed to analyze large-scale graphs. For all distributed graph-processing systems, partitioning graphs is a key part of processing and an important aspect to achieve good processing performance. To keep low the overhead of partitioning graphs, even when processing the ever-increasing modern graphs ,many previous studies use lightweight streaming graph-partitioning policies. Although many such policies exist, currently there is no comprehensive study of their impact on load balancing and communication overheads, and on the overall performance of graph-processing systems. This relative lack of understanding hampers the development and tuning of new streaming policies, and could limit the entire research community to the existing classes of policies. We address these issues in this work. We begin by modeling the execution time of distributed graph-processing systems. By analyzing this model under the load of realistic graph-data characteristics, we propose a method to identify important performance issues and then design new streaming graph-partitioning policies to address them. By using three typical large-scale graphs and three popular graph-processing algorithms, we conduct comprehensive experiments to study the performance of our and of many alternative streaming policies on a real distributed graph-processing system. We also explore the impact on performance of using different real-world networks and of other real-world technical details. We further discuss how to use our results, the coverage of our model and method, and the design of future partitioning policies.@en

In recent years, many distributed graph-processing systems have been designed and developed to analyze large-scale graphs. For all distributed graph-processing systems, partitioning graphs is a key part of processing and an important aspect to achieve good processing performance. To keep low the overhead of partitioning graphs, even when processing the ever-increasing modern graphs ,many previous studies use lightweight streaming graph-partitioning policies. Although many such policies exist, currently there is no comprehensive study of their impact on load balancing and communication overheads, and on the overall performance of graph-processing systems. This relative lack of understanding hampers the development and tuning of new streaming policies, and could limit the entire research community to the existing classes of policies. We address these issues in this work. We begin by modeling the execution time of distributed graph-processing systems. By analyzing this model under the load of realistic graph-data characteristics, we propose a method to identify important performance issues and then design new streaming graph-partitioning policies to address them. By using three typical large-scale graphs and three popular graph-processing algorithms, we conduct comprehensive experiments to study the performance of our and of many alternative streaming policies on a real distributed graph-processing system. We also explore the impact on performance of using different real-world networks and of other real-world technical details. We further discuss how to use our results, the coverage of our model and method, and the design of future partitioning policies.@en

Graph processing is increasingly used in knowledge economies and in science, in advanced marketing, social networking, bioinformatics, etc. A number of graph-processing systems, including the GPU-enabled Medusa and Totem, have been developed recently. Understanding their performance is key to system selection, tuning, and improvement. Previous performance evaluation studies have been conducted for CPU-based graph-processing systems, such as Graph and GraphX. Unlike them, the performance of GPU-enabled systems is still not thoroughly evaluated and compared. To address this gap, we propose an empirical method for evaluating GPU-enabled graph-processing systems, which includes new performance metrics and a selection of new datasets and algorithms. By selecting 9 diverse graphs and 3 typical graph-processing algorithms, we conduct a comparative performance study of 3 GPU-enabled systems, Medusa, Totem, and MapGraph. We present the first comprehensive evaluation of GPU-enabled systems with results giving insight into raw processing power, performance breakdown into core components, scalability, and the impact on performance of system-specific optimization techniques and of the GPU generation. We present and discuss many findings that would benefit users and developers interested in GPU acceleration for graph processing.@en

Cloud computing is a new paradigm for using ICT services—only when needed and for as long as needed, and paying only for service actually consumed. Benchmarking the increasingly many cloud services is crucial for market growth and perceived fairness, and for service design and tuning. In this work, we propose a generic architecture for benchmarking cloud services. Motivated by recent demand for data-intensive ICT services, and in particular by processing of large graphs, we adapt the generic architecture to Graphalytics, a benchmark for distributed and GPU-based graph analytics platforms. Graphalytics focuses on the dependence of performance on the input dataset, on the analytics algorithm, and on the provisioned infrastructure. The benchmark provides components for platform configuration, deployment, and monitoring, and has been tested for a variety of platforms. We also propose a new challenge for the process of benchmarking data-intensive services, namely the inclusion of the data-processing algorithm in the system under test; this increases significantly the relevance of benchmarking results, albeit, at the cost of increased benchmarking duration.@en

Cloud computing is a new paradigm for using ICT services—only when needed and for as long as needed, and paying only for service actually consumed. Benchmarking the increasingly many cloud services is crucial for market growth and perceived fairness, and for service design and tuning. In this work, we propose a generic architecture for benchmarking cloud services. Motivated by recent demand for data-intensive ICT services, and in particular by processing of large graphs, we adapt the generic architecture to Graphalytics, a benchmark for distributed and GPU-based graph analytics platforms. Graphalytics focuses on the dependence of performance on the input dataset, on the analytics algorithm, and on the provisioned infrastructure. The benchmark provides components for platform configuration, deployment, and monitoring, and has been tested for a variety of platforms. We also propose a new challenge for the process of benchmarking data-intensive services, namely the inclusion of the data-processing algorithm in the system under test; this increases significantly the relevance of benchmarking results, albeit, at the cost of increased benchmarking duration.@en

Cloud computing is a new paradigm for using ICT services—only when needed and for as long as needed, and paying only for service actually consumed. Benchmarking the increasingly many cloud services is crucial for market growth and perceived fairness, and for service design and tuning. In this work, we propose a generic architecture for benchmarking cloud services. Motivated by recent demand for data-intensive ICT services, and in particular by processing of large graphs, we adapt the generic architecture to Graphalytics, a benchmark for distributed and GPU-based graph analytics platforms. Graphalytics focuses on the dependence of performance on the input dataset, on the analytics algorithm, and on the provisioned infrastructure. The benchmark provides components for platform configuration, deployment, and monitoring, and has been tested for a variety of platforms. We also propose a new challenge for the process of benchmarking data-intensive services, namely the inclusion of the data-processing algorithm in the system under test; this increases significantly the relevance of benchmarking results, albeit, at the cost of increased benchmarking duration.@en

Cloud computing is a new paradigm for using ICT services—only when needed and for as long as needed, and paying only for service actually consumed. Benchmarking the increasingly many cloud services is crucial for market growth and perceived fairness, and for service design and tuning. In this work, we propose a generic architecture for benchmarking cloud services. Motivated by recent demand for data-intensive ICT services, and in particular by processing of large graphs, we adapt the generic architecture to Graphalytics, a benchmark for distributed and GPU-based graph analytics platforms. Graphalytics focuses on the dependence of performance on the input dataset, on the analytics algorithm, and on the provisioned infrastructure. The benchmark provides components for platform configuration, deployment, and monitoring, and has been tested for a variety of platforms. We also propose a new challenge for the process of benchmarking data-intensive services, namely the inclusion of the data-processing algorithm in the system under test; this increases significantly the relevance of benchmarking results, albeit, at the cost of increased benchmarking duration.@en