Algorithms for processing large, unstructured data sets have shown great promise in implementations on modern graphics processors (GPUs), with many implementations reporting 20-70x speedup over comparable CPU-only versions of the same algorithms. In this senior project research, our goal is to implement an efficient, highly scalable SQLite database on GPU, test an optimized implementation of a data sorting algorithm like GPU-Quicksort, and demonstrate the speed potential of GPU-enhanced computation on a typical big-data search and aggregation algorithm like MapReduce
As of 2012, the world creates 2.5 quintillion bytes of data every day. Much of this data generated i...
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...
This paper introduces the development of a new GPU-based database to accelerate data retrieval. The ...
Big Data applications are trivially parallelizable because they typically consist of simple and stra...
Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)...
© 2020 Association for Computing Machinery. There has been significant amount of excitement and rece...
The general-purpose computing capabilities of the Graphics Processing Unit (GPU) have recently been ...
We present new algorithms for performing fast computa-tion of several common database operations on ...
Abstract. The vast amount of processing power and memory band-width provided by modern graphics card...
In this paper, we show how to employ Graphics Processing Units (GPUs) to provide an effcient and hig...
AbstractIn this paper, we show how to employ Graphics Processing Units (GPUs) to provide an effcient...
Graphics processors (GPUs) have recently emerged as powerful coprocessors for general purpose comput...
AbstractThis study is devoted to exploring possible applications of GPU technology for acceleration ...
Graphics processors (GPUs) have recently emerged as a powerful co-processor for general-purpose comp...
As of 2012, the world creates 2.5 quintillion bytes of data every day. Much of this data generated i...
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...
This paper introduces the development of a new GPU-based database to accelerate data retrieval. The ...
Big Data applications are trivially parallelizable because they typically consist of simple and stra...
Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)...
© 2020 Association for Computing Machinery. There has been significant amount of excitement and rece...
The general-purpose computing capabilities of the Graphics Processing Unit (GPU) have recently been ...
We present new algorithms for performing fast computa-tion of several common database operations on ...
Abstract. The vast amount of processing power and memory band-width provided by modern graphics card...
In this paper, we show how to employ Graphics Processing Units (GPUs) to provide an effcient and hig...
AbstractIn this paper, we show how to employ Graphics Processing Units (GPUs) to provide an effcient...
Graphics processors (GPUs) have recently emerged as powerful coprocessors for general purpose comput...
AbstractThis study is devoted to exploring possible applications of GPU technology for acceleration ...
Graphics processors (GPUs) have recently emerged as a powerful co-processor for general-purpose comp...
As of 2012, the world creates 2.5 quintillion bytes of data every day. Much of this data generated i...
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...