Distributed GraphLab: A Framework for Machine Learning in the Cloud

Low, Yucheng; Kyrola, Aapo; Bickson, Danny; Guestrin, Carlos; Hellerstein, Joseph M.

doi:10.48550/arxiv.1204.6078

Cited by 9 publications

(12 citation statements)

References 17 publications

(22 reference statements)

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“…Incorporating graph technology and the abundance of dissimilar graph datasets have assisted in building quite sophisticated graph analytics tools. Despite the effectiveness of the conventional graph analysis approaches, such as Graphx [30], Gephi [31], GraphLab [32] to name a few, graph embedding has notably improved the efficiency of conducting graph analytics aby converting the graph to a low semantic dimensional space, thus information can be represented as vectors leading to computational efficiency. Several efforts have been conducted to incorporate KG Embeddings to address numerous NLP challenges.…”

Section: Nlp Applications Using Kgementioning

confidence: 99%

Relational Learning Analysis of Social Politics using Knowledge Graph Embedding

Abu-Salih

Al-Tawil

Aljarah

et al. 2021

Data Min Knowl Disc

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Knowledge Graphs (KGs) have gained considerable attention recently from both academia and industry. In fact, incorporating graph technology and the copious of various graph datasets have led the research community to build sophisticated graph analytics tools. Therefore, the application of KGs has extended to tackle a plethora of real-life problems in dissimilar domains. Despite the abundance of the currently proliferated generic KGs, there is a vital need to construct domain-specific KGs. Further, quality and credibility should be assimilated in the process of constructing and augmenting KGs, particularly those propagated from mixed-quality resources such as social media data. This paper presents a novel credibility domain-based KG Embedding framework. This framework involves capturing a fusion of data obtained from heterogeneous resources into a formal KG representation depicted by a domain ontology. The proposed approach makes use of various knowledge-based repositories to enrich the semantics of the textual contents, thereby facilitating the interoperability of information. The proposed framework also embodies a credibility module to ensure data quality and trustworthiness. The constructed KG is then embedded in a low-dimension semantically-continuous space using several embedding techniques. The utility of the constructed KG and its embeddings is demonstrated and substantiated on link prediction, clustering, and visualisation tasks.

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Section: Nlp Applications Using Kgementioning

confidence: 99%

Relational Learning Analysis of Social Politics using Knowledge Graph Embedding

Abu-Salih

Al-Tawil

Aljarah

et al. 2021

Data Min Knowl Disc

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“…Many distributed graph processing systems such as Pregel [25], GraphLab [24], and PowerGraph [17], have been proposed to…”

Section: Graph Preprocessing and Transformationmentioning

confidence: 99%

Fast and Efficient Parallel Breadth-First Search with Power-law Graph Transformation

Jiang¹,

Liu²,

Zhang³

et al. 2020

Preprint

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In the big data era, graph computing is widely used to exploit the hidden value in real-world graphs in various scenarios such as social networks, knowledge graphs, web searching, and recommendation systems. However, the random memory accesses result in inefficient use of cache and the irregular degree distribution leads to substantial load imbalance. Breadth-First Search (BFS) is frequently utilized as a kernel for many important and complex graph algorithms. In this paper, we describe a preprocessing approach using Reverse Cuthill-Mckee (RCM) algorithm to improve data locality and demonstrate how to achieve an efficient load balancing for BFS. Computations on RCM-reordered graph data are also accelerated with SIMD executions. We evaluate the performance of the graph preprocessing approach on Kronecker graphs of the Graph500 benchmark and real-world graphs. Our BFS implementation on RCM-reordered graph data achieves 326.48 MTEPS/W (mega TEPS per watt) on an ARMv8 system, ranking 2nd on the Green Graph500 list in June 2020 (the 1st rank uses GPU acceleration).

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“…Sparsity is a key enabler of future Artificial Intelligence [3][4][5]. Sparsity enables fast and energy-efficient training and inference in various domains [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

Dai¹,

Huang²,

Shang³

et al. 2022

Preprint

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Sparse Matrix-Matrix Multiplication (SpMM) has served as fundamental components in various domains. Many previous studies exploit GPUs for SpMM acceleration because GPUs provide high bandwidth and parallelism. We point out that a static design does not always improve the performance of SpMM on different input data (e.g., >85% performance loss with a single algorithm). In this paper, we consider the challenge of input dynamics from a novel autotuning perspective, while following issues remain to be solved: (1) Orthogonal design principles considering sparsity. Orthogonal design principles for such a sparse problem should be extracted to form different algorithms, and further used for performance tuning.(2) Nontrivial implementations in the algorithm space. Combining orthogonal design principles to create new algorithms needs to tackle with new challenges like thread race handling. (3) Heuristic adaptability to input dynamics. The heuristic adaptability is required to dynamically optimize code for input dynamics.To tackle these challenges, we first propose a novel three-loop model to extract orthogonal design principles for SpMM on GPUs. The model not only covers previous SpMM designs, but also comes up with new designs absent from previous studies. We propose techniques like conditional reduction to implement algorithms missing in previous studies. We further propose DA-SpMM, a Data-Aware heuristic GPU kernel for SpMM. DA-SpMM adaptively optimizes code considering input dynamics. Extensive experimental results show that, DA-SpMM achieves 1.26×∼1.37× speedup compared with the best NVIDIA cuSPARSE algorithm on average, and brings up to 5.59× end-to-end speedup to applications like Graph Neural Networks. * Example performance (GFLOP/S) ** Normalized performance to the algorithm with the best performance * * * Geometric mean of all normalized performance for different matrices **** Upper bound if we use the best algorithm for each matrix posteriorly

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Distributed GraphLab: A Framework for Machine Learning in the Cloud

Cited by 9 publications

References 17 publications

Relational Learning Analysis of Social Politics using Knowledge Graph Embedding

Relational Learning Analysis of Social Politics using Knowledge Graph Embedding

Fast and Efficient Parallel Breadth-First Search with Power-law Graph Transformation

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

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