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 isalmost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU
Current database management systems use Graphic Processing Units (GPUs) as dedicated accelerators to...
While GPU query processing is a well-studied area, real adoption is limited in practice as typically...
Relational join processing is one of the core functionalities in database management systems. Implem...
We engineer a GPU implementation of a B-Tree that supports concurrent queries (point, range, and suc...
Latent Semantic Analysis (LSA) is a method that allows us to automatically index and retrieve inform...
Latent Semantic Analysis (LSA) aims to reduce the dimensions of large term-document datasets using S...
We present a fast dynamic graph data structure for the GPU. Our dynamic graph structure uses one has...
Sorting is a primitive operation that is a building block for countless algorithms. As such, it is i...
Query co-processing on graphics processors (GPUs) has become an effective means to improve the perfo...
Building efficient concurrent data structures that scale to the level of GPU parallelism is a challe...
The general-purpose computing capabilities of the Graphics Processing Unit (GPU) have recently been ...
This paper introduces the development of a new GPU-based database to accelerate data retrieval. The ...
We design a high-performance parallel merge sort for highly parallel systems. Our merge sort is desi...
We describe the design of high-performance parallel radix sort and merge sort routines for manycore ...
To meet the high throughput requirement of communication systems, the design of high-throughput low-...
Current database management systems use Graphic Processing Units (GPUs) as dedicated accelerators to...
While GPU query processing is a well-studied area, real adoption is limited in practice as typically...
Relational join processing is one of the core functionalities in database management systems. Implem...
We engineer a GPU implementation of a B-Tree that supports concurrent queries (point, range, and suc...
Latent Semantic Analysis (LSA) is a method that allows us to automatically index and retrieve inform...
Latent Semantic Analysis (LSA) aims to reduce the dimensions of large term-document datasets using S...
We present a fast dynamic graph data structure for the GPU. Our dynamic graph structure uses one has...
Sorting is a primitive operation that is a building block for countless algorithms. As such, it is i...
Query co-processing on graphics processors (GPUs) has become an effective means to improve the perfo...
Building efficient concurrent data structures that scale to the level of GPU parallelism is a challe...
The general-purpose computing capabilities of the Graphics Processing Unit (GPU) have recently been ...
This paper introduces the development of a new GPU-based database to accelerate data retrieval. The ...
We design a high-performance parallel merge sort for highly parallel systems. Our merge sort is desi...
We describe the design of high-performance parallel radix sort and merge sort routines for manycore ...
To meet the high throughput requirement of communication systems, the design of high-throughput low-...
Current database management systems use Graphic Processing Units (GPUs) as dedicated accelerators to...
While GPU query processing is a well-studied area, real adoption is limited in practice as typically...
Relational join processing is one of the core functionalities in database management systems. Implem...