公开(公告)号：US20230350677A1

公开(公告)日：2023-11-02

申请号：US18165589

申请日：2023-02-07

Applicant: UNIVERSITY OF SOUTH FLORIDA

Inventor： Yicheng Tu ,  Napath Pitaksirianan

IPC: G06F9/30

CPC classification number: G06F9/30098 ,  G06F9/30043

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. Although 2-BS problems share the same core computations, each 2-BS problem however carries its own characteristics that calls for different strategies in code optimization. Accordingly, various embodiments of the present disclosure involve a software framework that automatically generates high-performance GPU code based on a few parameters and short primer code input.

公开(公告)号：US20220270203A1

公开(公告)日：2022-08-25

申请号：US17733217

申请日：2022-04-29

Applicant: University of South Florida

Inventor： Yicheng Tu ,  Ran Rui

IPC: G06T1/60 ,  G06T1/20

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.

公开(公告)号：US20210133917A1

公开(公告)日：2021-05-06

申请号：US16629458

申请日：2018-04-12

Applicant: UNIVERSITY OF SOUTH FLORIDA

Inventor： Yicheng Tu ,  Ran Rui

IPC: G06T1/60 ,  G06T1/20

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.

公开(公告)号：US12147806B2

公开(公告)日：2024-11-19

申请号：US18165589

申请日：2023-02-07

Applicant: UNIVERSITY OF SOUTH FLORIDA

Inventor： Yicheng Tu ,  Napath Pitaksirianan

IPC: G06F9/30

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. Although 2-BS problems share the same core computations, each 2-BS problem however carries its own characteristics that calls for different strategies in code optimization. Accordingly, various embodiments of the present disclosure involve a software framework that automatically generates high-performance GPU code based on a few parameters and short primer code input.

公开(公告)号：US11526960B2

公开(公告)日：2022-12-13

申请号：US17733217

申请日：2022-04-29

Applicant: University of South Florida

Inventor： Yicheng Tu ,  Ran Rui

IPC: G06T1/20 ,  G06T1/60 ,  G06F9/48

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.

公开(公告)号：US11361401B2

公开(公告)日：2022-06-14

申请号：US16629458

申请日：2018-04-12

Applicant: UNIVERSITY OF SOUTH FLORIDA

Inventor： Yicheng Tu ,  Ran Rui

IPC: G06T1/20 ,  G06T1/60 ,  G06F9/48

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.

公开(公告)号：US11573797B2

公开(公告)日：2023-02-07

申请号：US17474828

申请日：2021-09-14

Applicant: UNIVERSITY OF SOUTH FLORIDA

Inventor： Yicheng Tu ,  Napath Pitaksirianan

IPC: G06F17/13 ,  G06F9/30

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. Although 2-BS problems share the same core computations, each 2-BS problem however carries its own characteristics that calls for different strategies in code optimization. Accordingly, various embodiments of the present disclosure involve a software framework that automatically generates high-performance GPU code based on a few parameters and short primer code input.

公开(公告)号：US20220075620A1

公开(公告)日：2022-03-10

申请号：US17474828

申请日：2021-09-14

Applicant: UNIVERSITY OF SOUTH FLORIDA

Inventor： Yicheng Tu ,  Napath Pitaksirianan

IPC: G06F9/30

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. Although 2-BS problems share the same core computations, each 2-BS problem however carries its own characteristics that calls for different strategies in code optimization. Accordingly, various embodiments of the present disclosure involve a software framework that automatically generates high-performance GPU code based on a few parameters and short primer code input.

公开(公告)号：US11176631B1

公开(公告)日：2021-11-16

申请号：US16451295

申请日：2019-06-25

Applicant: UNIVERSITY OF SOUTH FLORIDA

Inventor： Yicheng Tu ,  Zhila Nouri Lewis

IPC: G06T1/20 ,  G06F9/38 ,  G06F9/50 ,  G06T17/00

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.

公开(公告)号：US11119771B2

公开(公告)日：2021-09-14

申请号：US16521852

申请日：2019-07-25

Applicant: UNIVERSITY OF SOUTH FLORIDA

Inventor： Yicheng Tu ,  Napath Pitaksirianan

IPC: G06F17/18 ,  G06F17/10 ,  G06F9/30

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. Although 2-BS problems share the same core computations, each 2-BS problem however carries its own characteristics that calls for different strategies in code optimization. Accordingly, various embodiments of the present disclosure involve a software framework that automatically generates high-performance GPU code based on a few parameters and short primer code input.