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1. GPU LSM: A Dynamic Dictionary Data Structure for the GPU

   https://doi.org/10.1109/IPDPS.2018.00053  

   Ashkiani, Saman; Li, Shengren; Farach-Colton, Martin; Amenta, Nina; Owens, John D. (May 2018, Proceedings of the 31st IEEE International Parallel and Distributed Processing Symposium)

   We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA K40c GPU has an average update (insertion or deletion) rate of 225 M elements/s, 13.5x faster than merging items into a sorted array. The GPU LSM supports the retrieval operations of lookup, count, and range query operations with an average rate of 75 M, 32 M and 23 M queries/s respectively. The trade-off for the dynamic updates is that the sorted array is almost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU. 

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2. GPU LSM: A Dynamic Dictionary Data Structure for the GPU

   Ashkiani, S.; Li, S.; Farach-Colton, M; Amenta, N.; Owens, J (January 2018, IPDPS .... [proceedings])

   We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA K40c GPU has an average update (insertion or deletion) rate of 225 M elements/s, 13.5x faster than merging items into a sorted array. The GPU LSM supports the retrieval operations of lookup, count, and range query operations with an average rate of 75 M, 32 M and 23 M queries/s respectively. The trade-off for the dynamic updates is that the sorted array is almost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU. 

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3. Graph Coloring on the GPU

   https://doi.org/10.1109/IPDPSW.2019.00046  

   Osama, Muhammad; Truong, Minh; Yang, Carl; Buluc, Aydin; Owens, John (May 2019, Proceedings of the Workshop on Graphs, Architectures, Programming, and Learning)

   We design and implement parallel graph coloring algorithms on the GPU using two different abstractions—one datacentric (Gunrock), the other linear-algebra-based (GraphBLAS). We analyze the impact of variations of a baseline independent-set algorithm on quality and runtime. We study how optimizations such as hashing, avoiding atomics, and a max-min heuristic affect performance. Our Gunrock graph coloring implementation has a peak 2x speed-up, a geomean speed-up of 1.3x and produces 1.6x more colors over previous hardwired state-of-theart implementations on real-world datasets. Our GraphBLAS implementation of Luby’s algorithm produces 1.9x fewer colors than the previous state-of-the-art parallel implementation at the cost of 3x extra runtime, and 1.014x fewer colors than a greedy, sequential algorithm with a geomean speed-up of 2.6x. 

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4. Graph Coloring on the GPU

   Osama, Muhammad; Truong, Minh; Yang, Carl; Buluç, Aydın; Owens, John D. (May 2019, Proceedings of the Workshop on Graphs, Architectures, Programming, and Learning)

   We design and implement parallel graph coloring algorithms on the GPU using two different abstractions—one data-centric (Gunrock), the other linear-algebra-based (GraphBLAS). We analyze the impact of variations of a baseline independent-set algorithm on quality and runtime. We study how optimizations such as hashing, avoiding atomics, and a max-min heuristic affect performance. Our Gunrock graph coloring implementation has a peak 2x speed-up, a geomean speed-up of 1.3x and produces 1.6x more colors over previous hardwired state-of-the-art implementations on real-world datasets. Our GraphBLAS implementation of Luby's algorithm produces 1.9x fewer colors than the previous state-of-the-art parallel implementation at the cost of 3x extra runtime, and 1.014x fewer colors than a greedy, sequential algorithm with a geomean speed-up of 2.6x. 

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5. Dynamic Graphs on the GPU

   https://doi.org/10.1109/IPDPS47924.2020.00081  

   Awad, Muhammad A; Ashkiani, Saman; Porumbescu, Serban D.; Owens, John D. (May 2020, Proceedings of the 34th IEEE International Parallel and Distributed Processing Symposium)

   We present a fast dynamic graph data structure for the GPU. Our dynamic graph structure uses one hash table per vertex to store adjacency lists and achieves 3.4–14.8x faster insertion rates over the state of the art across a diverse set of large datasets, as well as deletion speedups up to 7.8x. The data structure supports queries and dynamic updates through both edge and vertex insertion and deletion. In addition, we define a comprehensive evaluation strategy based on operations, workloads, and applications that we believe better characterize and evaluate dynamic graph data structures. 

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