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Graph computing is widely applied in a large number of big data applications. Despite its importance, high performance graph computing remains a challenge, especially for large-scale graphs. In this paper, by analyzing from the architectural perspective, we study computational behaviors of graph computing in real-world use cases. We benchmark a set of representative graph algorithms implemented on...
Today, there is a steep rise in the amount of data being collected from diverse applications. Consequently, data analytic workloads are gaining popularity to gain insight that can benefit the application, e.g., financial trading, social media analysis. To study the architectural behavior of the workloads, architectural simulation is one of the most common approaches. However, because of the long-running...
With the emergence of data science, graph computing has become increasingly important these days. Unfortunately, graph computing typically suffers from poor performance when mapped to modern computing systems because of the overhead of executing atomic operations and inefficient utilization of the memory subsystem. Meanwhile, emerging technologies, such as Hybrid Memory Cube (HMC), enable the processing-in-memory...
Graph technologies have been widely utilized for building big data analytics systems. Since those systems are typically wrapped as service providers in industry, it is critical to handle concurrent queries at runtime by incorporating a set of parallel processing units. In many cases, such queries result in local subgraph traversals, which essentially require an efficient scheduling scheme to explore...
With the emergence of data science, graph computing is becoming a crucial tool for processing big connected data. Although efficient implementations of specific graph applications exist, the behavior of full-spectrum graph computing remains unknown. To understand graph computing, we must consider multiple graph computation types, graph frameworks, data representations, and various data sources in...
Many Big Data analytics essentially explore the relationship among interconnected entities, which are naturally represented as graphs. However, due to the irregular data access patterns in the graph computations, it remains a fundamental challenge to deliver highly efficient solutions for large scale graph analytics. Such inefficiency restricts the utilization of many graph algorithms in Big Data...
Efficient image query is a fundamental challenge in many large scale multimedia applications, especially when handling many queries concurrently. In this paper, we proposed a novel approach called graph local random walk for high performance concurrent image query. Specifically, we organize the massive images set into a large scale graph using graph database, according to the similarity between images...
Architecture simulation for GPGPU kernels can take a significant amount of time, especially for large-scale GPGPU kernels. This paper presents TBPoint, an infrastructure based on profiling-based sampling for GPGPU kernels to reduce the cycle-level simulation time. Compared to existing approaches, TBPoint provides a flexible and architecture-independent way to take samples. For the evaluated 12 kernels,...
Conflict detection and resolution are among the most fundamental issues in transactional memory systems. Hardware transactional memory (HTM) systems such as AMD's Advanced Synchronization Facility (ASF) employ inherent cache coherence protocol messages to perform conflict detection among transactions. Such an implementation has the advantage of design simplicity, nonetheless, it also generates false...
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