Benchmarks, scripts and simulator source code for the publication 'Graph Prefetching Using Data Structure Knowledge'.This work was supported by the EPSRC [grant numbers EP/M506485/1 and EP/K026399/1]
With the user manual provided at the end of the research manuscript, and the Graph Input Data Exampl...
This dataset contains data that was presented and analyzed in our paper "Scalable Knowledge-Graph An...
By analyzing graphs using specialized algorithms, complex relationships and deeper insight can be ex...
Searches on large graphs are heavily memory latency bound, as a result of many high latency DRAM acc...
Mining large graphs has now become an important aspect of mul-tiple diverse applications and a numbe...
Web prefetching is an effective technique used to mitigate the user perceived latency by making pred...
This deliverable introduces the data pre-processing that is necessary to be carried out at data prov...
Datasets used for "Benchmarking graph representation learning algorithms for detecting modules in mo...
Abstract. We present a preconditioning technique, called support-graph preconditioning, and use it t...
In this age of information, data gathering has become a new growing trend. Social networking sites, ...
Both static and streaming graph processing are central in data analytics scenarios such as recommend...
Includes replication data and code for paper Diagnosing Multicollinearity in Exponential Random Grap...
Source code for the LLVM passes for automating programmable prefetching, as well as code modificatio...
keywords include: knowledge graph, embedding, dataset, relational pattern benchmarkin
Graph processing applications are severely bottlenecked by memory system performance due to low data...
With the user manual provided at the end of the research manuscript, and the Graph Input Data Exampl...
This dataset contains data that was presented and analyzed in our paper "Scalable Knowledge-Graph An...
By analyzing graphs using specialized algorithms, complex relationships and deeper insight can be ex...
Searches on large graphs are heavily memory latency bound, as a result of many high latency DRAM acc...
Mining large graphs has now become an important aspect of mul-tiple diverse applications and a numbe...
Web prefetching is an effective technique used to mitigate the user perceived latency by making pred...
This deliverable introduces the data pre-processing that is necessary to be carried out at data prov...
Datasets used for "Benchmarking graph representation learning algorithms for detecting modules in mo...
Abstract. We present a preconditioning technique, called support-graph preconditioning, and use it t...
In this age of information, data gathering has become a new growing trend. Social networking sites, ...
Both static and streaming graph processing are central in data analytics scenarios such as recommend...
Includes replication data and code for paper Diagnosing Multicollinearity in Exponential Random Grap...
Source code for the LLVM passes for automating programmable prefetching, as well as code modificatio...
keywords include: knowledge graph, embedding, dataset, relational pattern benchmarkin
Graph processing applications are severely bottlenecked by memory system performance due to low data...
With the user manual provided at the end of the research manuscript, and the Graph Input Data Exampl...
This dataset contains data that was presented and analyzed in our paper "Scalable Knowledge-Graph An...
By analyzing graphs using specialized algorithms, complex relationships and deeper insight can be ex...
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