公开(公告)号：US11995087B2

公开(公告)日：2024-05-28

申请号：US18095936

申请日：2023-01-11

Applicant: SAP SE

Inventor： Dong Hun Lee ,  Minseon Ahn ,  Jungmin Kim ,  Kang Woo Choi ,  Oliver Rebholz

IPC: G06F16/2455 ,  G06F9/30 ,  G06F9/54 ,  G06F12/06 ,  G06F16/21 ,  G06F16/22 ,  G06F16/245

CPC classification number: G06F16/24561 ,  G06F9/30036 ,  G06F9/542 ,  G06F9/544 ,  G06F12/063 ,  G06F16/211 ,  G06F16/2282 ,  G06F16/24569

Abstract: Despite the increase of memory capacity and CPU computing power, memory performance remains the bottleneck of in-memory database management systems due to ever-increasing data volumes and application demands. Because the scale of data workloads has out-paced traditional CPU caches and memory bandwidth, one can improve data movement from memory to computing units to improve performance in in-memory database scenarios. A near-memory database accelerator framework offloads data-intensive database operations via or to a near-memory computation engine. The database accelerator's system architecture can include a database accelerator software module/driver and a memory module with a database accelerator engine. An application programming interface (API) can be provided to support database accelerator functionality. Memory of the database accelerator can be directly accessible by the CPU.

公开(公告)号：US20230161771A1

公开(公告)日：2023-05-25

申请号：US18095936

申请日：2023-01-11

Applicant: SAP SE

Inventor： Dong Hun Lee ,  Minseon Ahn ,  Jungmin Kim ,  Kang Woo Choi ,  Oliver Rebholz

IPC: G06F16/2455 ,  G06F16/22 ,  G06F16/21 ,  G06F9/30 ,  G06F9/54 ,  G06F12/06 ,  G06F16/245

CPC classification number: G06F16/24561 ,  G06F16/2282 ,  G06F16/211 ,  G06F9/30036 ,  G06F9/542 ,  G06F9/544 ,  G06F12/063 ,  G06F16/24569

Abstract: Despite the increase of memory capacity and CPU computing power, memory performance remains the bottleneck of in-memory database management systems due to ever-increasing data volumes and application demands. Because the scale of data workloads has out-paced traditional CPU caches and memory bandwidth, one can improve data movement from memory to computing units to improve performance in in-memory database scenarios. A near-memory database accelerator framework offloads data-intensive database operations via or to a near-memory computation engine. The database accelerator's system architecture can include a database accelerator software module/driver and a memory module with a database accelerator engine. An application programming interface (API) can be provided to support database accelerator functionality. Memory of the database accelerator can be directly accessible by the CPU.

公开(公告)号：US12045233B2

公开(公告)日：2024-07-23

申请号：US17979643

申请日：2022-11-02

Applicant: SAP SE

Inventor： Kang Woo Choi ,  Daeun Lee ,  Dong Hun Lee

IPC: G06F16/245 ,  G06F16/2453

CPC classification number: G06F16/24537 ,  G06F16/24545

Abstract: Mechanisms are disclosed for estimating cardinality of group-by queries. A probability of occurrence of values is obtained for columns that satisfy the query occurring in tables from a trained machine learning model. A range selectivity is calculated based on a conditional probability of occurrence of the values. A set of valid generated sample tuples is generated from the trained machine learning model. A group-by selectivity is calculated by keeping the conditional probability of occurrence to obtain probabilities that a result set will have specific group-by column values associated with the tables while proceeding with progressive sampling. A sampling probability is calculated by normalizing the group-by selectivity by dividing the group-by selectivity by the range selectivity. The samples are filtered such that the samples having a sampling probability below a sampling probability threshold are filtered out. A sampling-based estimator is applied to the filtered samples set to estimate the cardinality.

公开(公告)号：US20240143586A1

公开(公告)日：2024-05-02

申请号：US17979643

申请日：2022-11-02

Applicant: SAP SE

Inventor： Kang Woo Choi ,  Daeun Lee ,  Dong Hun Lee

IPC: G06F16/2453

CPC classification number: G06F16/24537 ,  G06F16/24545

Abstract: Mechanisms are disclosed for estimating cardinality of group-by queries. A probability of occurrence of values is obtained for columns that satisfy the query occurring in tables from a trained machine learning model. A range selectivity is calculated based on a conditional probability of occurrence of the values. A set of valid generated sample tuples is generated from the trained machine learning model. A group-by selectivity is calculated by keeping the conditional probability of occurrence to obtain probabilities that a result set will have specific group-by column values associated with the tables while proceeding with progressive sampling. A sampling probability is calculated by normalizing the group-by selectivity by dividing the group-by selectivity by the range selectivity. The samples are filtered such that the samples having a sampling probability below a sampling probability threshold are filtered out. A sampling-based estimator is applied to the filtered samples set to estimate the cardinality.

公开(公告)号：US11586630B2

公开(公告)日：2023-02-21

申请号：US16897138

申请日：2020-06-09

Applicant: SAP SE

Inventor： Dong Hun Lee ,  Minseon Ahn ,  Jungmin Kim ,  Kang Woo Choi ,  Oliver Rebholz

IPC: G06F16/2455 ,  G06F16/22 ,  G06F16/21 ,  G06F9/30 ,  G06F9/54 ,  G06F12/06 ,  G06F16/245

Abstract: Despite the increase of memory capacity and CPU computing power, memory performance remains the bottleneck of in-memory database management systems due to ever-increasing data volumes and application demands. Because the scale of data workloads has out-paced traditional CPU caches and memory bandwidth, one can improve data movement from memory to computing units to improve performance in in-memory database scenarios. A near-memory database accelerator framework offloads data-intensive database operations via or to a near-memory computation engine. The database accelerator's system architecture can include a database accelerator software module/driver and a memory module with a database accelerator engine. An application programming interface (API) can be provided to support database accelerator functionality. Memory of the database accelerator can be directly accessible by the CPU.

公开(公告)号：US20210271680A1

公开(公告)日：2021-09-02

申请号：US16897138

申请日：2020-06-09

Applicant: SAP SE

Inventor： Dong Hun Lee ,  Minseon Ahn ,  Jungmin Kim ,  Kang Woo Choi ,  Oliver Rebholz

IPC: G06F16/2455 ,  G06F16/21 ,  G06F16/22 ,  G06F12/06 ,  G06F9/30 ,  G06F9/54

Abstract: Despite the increase of memory capacity and CPU computing power, memory performance remains the bottleneck of in-memory database management systems due to ever-increasing data volumes and application demands. Because the scale of data workloads has out-paced traditional CPU caches and memory bandwidth, one can improve data movement from memory to computing units to improve performance in in-memory database scenarios. A near-memory database accelerator framework offloads data-intensive database operations via or to a near-memory computation engine. The database accelerator's system architecture can include a database accelerator software module/driver and a memory module with a database accelerator engine. An application programming interface (API) can be provided to support database accelerator functionality. Memory of the database accelerator can be directly accessible by the CPU.