Existing GPU graph analytics frameworks are typically built from specialized, bottom-up implementations of graph operators that are customized to graph computation. In this work we describe Mini-Gunrock, a lightweight graph analytics framework on the GPU. Unlike existing frameworks, Mini-Gunrock is built from graph operators implemented with generic transform-based data-parallel primitives. Using this method to bridge the gap between programmability and high performance for GPU graph analytics, we demonstrate operator performance on scale-free graphs with an average 1.5x speedup compared to Gunrock’s corresponding operator performance. Mini-Gunrock’s graph operators, optimizations, and applications code have 10x smaller code size and compar...
Graphs are the de facto data structures for many applications, and efficient graph processing is a m...
Graphs are de facto data structures for many applications, and efficient graph processing is a must ...
Graph algorithms are becoming increasingly important for analyzing large datasets in many fields. Re...
Existing GPU graph analytics frameworks are typically built from specialized, bottom-up implementati...
For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and th...
For large-scale graph analytics on the GPU, the irregularity of dataaccess/control flow and the comp...
We identify several factors that are critical to high-performance GPU graph analytics: efficient bui...
We present a plan to develop the “Gunrock” programmable, high-performance graph analytics library fo...
The growing use of graph in many fields has sparked a broad interest in developing high-level graph ...
We present a single-node, multi-GPU programmable graph processing library that allows programmers to...
High-performance implementations of graph algorithms are challenging to implement on new parallel ha...
Data analysis is a rising field of interest for computer science research due to the growing amount ...
Abstract—Graphs are common data structures for many applications, and efficient graph processing is ...
Graph analytics is fundamental in unlocking key insights by mining large volumes of highly connected...
Presented on April 16, 2019 at 3:00 p.m. in the Jesse W. Mason Building, Room 2117.Oded Green is a S...
Graphs are the de facto data structures for many applications, and efficient graph processing is a m...
Graphs are de facto data structures for many applications, and efficient graph processing is a must ...
Graph algorithms are becoming increasingly important for analyzing large datasets in many fields. Re...
Existing GPU graph analytics frameworks are typically built from specialized, bottom-up implementati...
For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and th...
For large-scale graph analytics on the GPU, the irregularity of dataaccess/control flow and the comp...
We identify several factors that are critical to high-performance GPU graph analytics: efficient bui...
We present a plan to develop the “Gunrock” programmable, high-performance graph analytics library fo...
The growing use of graph in many fields has sparked a broad interest in developing high-level graph ...
We present a single-node, multi-GPU programmable graph processing library that allows programmers to...
High-performance implementations of graph algorithms are challenging to implement on new parallel ha...
Data analysis is a rising field of interest for computer science research due to the growing amount ...
Abstract—Graphs are common data structures for many applications, and efficient graph processing is ...
Graph analytics is fundamental in unlocking key insights by mining large volumes of highly connected...
Presented on April 16, 2019 at 3:00 p.m. in the Jesse W. Mason Building, Room 2117.Oded Green is a S...
Graphs are the de facto data structures for many applications, and efficient graph processing is a m...
Graphs are de facto data structures for many applications, and efficient graph processing is a must ...
Graph algorithms are becoming increasingly important for analyzing large datasets in many fields. Re...