Abstract: Disclosed are various embodiments for computing 2-body statistics on graphics processing units (GPUs). Various types of two-body statistics (2-BS) are regarded as essential components of data analysis in many scientific and computing domains. However, the quadratic complexity of these computations hinders timely processing of data. According, various embodiments of the present disclosure involve parallel algorithms for 2-BS computation on Graphics Processing Units (GPUs). Although the typical 2-BS problems can be summarized into a straightforward parallel computing pattern, traditional wisdom from (general) parallel computing often falls short in delivering the best possible performance. Therefore, various embodiments of the present disclosure involve techniques to decompose 2-BS problems and methods for effective use of computing resources on GPUs. We also develop analytical models that guide users towards the appropriate parameters of a GPU program.

Abstract: Disclosed are various embodiments for computing 2-body statistics on graphics processing units (GPUs). Various types of two-body statistics (2-BS) are regarded as essential components of data analysis in many scientific and computing domains. However, the quadratic complexity of these computations hinders timely processing of data. According, various embodiments of the present disclosure involve parallel algorithms for 2-BS computation on Graphics Processing Units (GPUs). Although the typical 2-BS problems can be summarized into a straightforward parallel computing pattern, traditional wisdom from (general) parallel computing often falls short in delivering the best possible performance. Therefore, various embodiments of the present disclosure involve techniques to decompose 2-BS problems and methods for effective use of computing resources on GPUs. We also develop analytical models that guide users towards the appropriate parameters of a GPU program.

Abstract: Disclosed are various embodiments for performing a join operation using a graphics processing unit (GPU). The GPU can receive input data including sequences or tuples. The GPU can initialize a histogram in a memory location shared by threads. The GPU can build the histogram of hash values for the sequences. The GPU can reorder the sequences based on the histogram. The GPU can probe partitions and store the results in a buffer pool. The GPU can output the results of the join.

Abstract: Disclosed are various embodiments for performing a join operation using a graphics processing unit (GPU). The GPU can receive input data including sequences or tuples. The GPU can initialize a histogram in a memory location shared by threads. The GPU can build the histogram of hash values for the sequences. The GPU can reorder the sequences based on the histogram. The GPU can probe partitions and store the results in a buffer pool. The GPU can output the results of the join.

Abstract: Disclosed are various embodiments for batched query processing and optimization in database management systems. A single algebraic expression is generated based at least in part on applying equivalence rules to algebraic expressions for a plurality of database queries of a database comprising a set of relations. The equivalence rules involve relational operators comprising Psi (?) operators. The database can be queried using a single database query to create a result that is equivalent to the plurality of database queries.

Abstract: Disclosed are various embodiments for performing a join operation using a graphics processing unit (GPU). The GPU can receive input data including sequences or tuples. The GPU can initialize a histogram in a memory location shared by threads. The GPU can build the histogram of hash values for the sequences. The GPU can reorder the sequences based on the histogram. The GPU can probe partitions and store the results in a buffer pool. The GPU can output the results of the join.

Abstract: Disclosed are various embodiments for computing 2-body statistics on graphics processing units (GPUs). Various types of two-body statistics (2-BS) are regarded as essential components of data analysis in many scientific and computing domains. However, the quadratic complexity of these computations hinders timely processing of data. According, various embodiments of the present disclosure involve parallel algorithms for 2-BS computation on Graphics Processing Units (GPUs). Although the typical 2-BS problems can be summarized into a straightforward parallel computing pattern, traditional wisdom from (general) parallel computing often falls short in delivering the best possible performance. Therefore, various embodiments of the present disclosure involve techniques to decompose 2-BS problems and methods for effective use of computing resources on GPUs. We also develop analytical models that guide users towards the appropriate parameters of a GPU program.

Abstract: Disclosed are various embodiments for GPU-based parallel indexing for concurrent spatial queries. A number of nodes in a tree to be partitioned is determined. The tree is then iteratively partitioned with the GPU. Nodes are created with the GPU. Finally, a point insertion is performed using the GPU.

Abstract: Disclosed are various embodiments for computing 2-body statistics on graphics processing units (GPUs). Various types of two-body statistics (2-BS) are regarded as essential components of data analysis in many scientific and computing domains. However, the quadratic complexity of these computations hinders timely processing of data. According, various embodiments of the present disclosure involve parallel algorithms for 2-BS computation on Graphics Processing Units (GPUs). Although the typical 2-BS problems can be summarized into a straightforward parallel computing pattern, traditional wisdom from (general) parallel computing often falls short in delivering the best possible performance. Therefore, various embodiments of the present disclosure involve techniques to decompose 2-BS problems and methods for effective use of computing resources on GPUs. We also develop analytical models that guide users towards the appropriate parameters of a GPU program.

Abstract: Disclosed are various embodiments for performing a join operation using a graphics processing unit (GPU). The GPU can receive input data including sequences or tuples. The GPU can initialize a histogram in a memory location shared by threads. The GPU can build the histogram of hash values for the sequences. The GPU can reorder the sequences based on the histogram. The GPU can probe partitions and store the results in a buffer pool. The GPU can output the results of the join.

Abstract: Disclosed are various embodiments for computing 2-body statistics on graphics processing units (GPUs). Various types of two-body statistics (2-BS) are regarded as essential components of data analysis in many scientific and computing domains. However, the quadratic complexity of these computations hinders timely processing of data. According, various embodiments of the present disclosure involve parallel algorithms for 2-BS computation on Graphics Processing Units (GPUs). Although the typical 2-BS problems can be summarized into a straightforward parallel computing pattern, traditional wisdom from (general) parallel computing often falls short in delivering the best possible performance. Therefore, various embodiments of the present disclosure involve techniques to decompose 2-BS problems and methods for effective use of computing resources on GPUs. We also develop analytical models that guide users towards the appropriate parameters of a GPU program.