Add to ORKGConventional OLTP systems are slow in performance for analytical queries. In the existing heterogeneous architecture OLAP database management systems, no system distributes work using machine learning. In this study, the DOLAP architecture, which is a high-performance column-based database management system developed for shared memory architectures, is explained. Also, job distribution algorithms based on heuristic and machine learning methods have been developed for computing hardware with different characters such as CPU and GPU on the server on which the database is running, and their performance has been analyze
We report on the improvements. that can be achieved by applying machine learning techniques, in part...
Database engines are starting to incorporate machine learning (ML) functionality as part of their re...
Load balancing (LB) is the process of distributing the workload fairly across the servers within the...
For dynamic and continuous data analysis, conventional OLTP systems are slow in performance. Today's...
The increasing popularity of advanced data analytics workloads combined with the stagnation of trans...
Conventional compute and memory systems scaling to achieve higher performance and lower cost and pow...
In the heterogeneous computing environment, programmers map the applications either on CPUs or GPUs....
International audienceThe performance of irregular scientific applications can be easily affected by...
The memory requirements of emerging applications, especially in the domain of machine learn- ing wor...
Artificial Intelligence workloads have grown in popularity over the last decade, but database query ...
Background: Heterogeneous parallel computing systems utilize the combination of different resources ...
GPU has been considered as one of the next-generation platforms for real-time query processing datab...
International audienceNetwork load balancers are important components in data centers to provide sca...
The resurgence of machine learning since the late 1990s has been enabled by significant advances in ...
The past years saw the emergence of highly heterogeneous server architectures that feature multiple ...
We report on the improvements. that can be achieved by applying machine learning techniques, in part...
Database engines are starting to incorporate machine learning (ML) functionality as part of their re...
Load balancing (LB) is the process of distributing the workload fairly across the servers within the...
For dynamic and continuous data analysis, conventional OLTP systems are slow in performance. Today's...
The increasing popularity of advanced data analytics workloads combined with the stagnation of trans...
Conventional compute and memory systems scaling to achieve higher performance and lower cost and pow...
In the heterogeneous computing environment, programmers map the applications either on CPUs or GPUs....
International audienceThe performance of irregular scientific applications can be easily affected by...
The memory requirements of emerging applications, especially in the domain of machine learn- ing wor...
Artificial Intelligence workloads have grown in popularity over the last decade, but database query ...
Background: Heterogeneous parallel computing systems utilize the combination of different resources ...
GPU has been considered as one of the next-generation platforms for real-time query processing datab...
International audienceNetwork load balancers are important components in data centers to provide sca...
The resurgence of machine learning since the late 1990s has been enabled by significant advances in ...
The past years saw the emergence of highly heterogeneous server architectures that feature multiple ...
We report on the improvements. that can be achieved by applying machine learning techniques, in part...
Database engines are starting to incorporate machine learning (ML) functionality as part of their re...
Load balancing (LB) is the process of distributing the workload fairly across the servers within the...