Abstract: Systems, devices, methods, and computer-readable media are disclosed for utilizing group theoretic techniques to enable data exchange between a supervisory central processing unit (CPU) and a group of graphical processing units (GPUs). The CPU may be configured to utilize a tabu search metaheuristic to explore a solution space to determine an optimal solution to an optimization problem. More specifically, the CPU may determine a fragmentation of a solution space that yields multiple partitions of the solution space and may assign each partition to a respective GPU configured to calculate a computational result. The CPU may then determine a new fragmentation of the solution space based on the computational results received from the GPUs that yields new partitions of the solution space and may assign each new partition to a respective GPU configured to again generate a computational result based on its assigned new partition.

Abstract: Systems, methods, and computer-readable media are disclosed for an improved database. The systems, methods, and computer-readable media described herein may enhance the response time of databases and improve user experiences. In an example method described herein, a database management system may store, at a first database, a first data block. The first data block may be stored in association with one or more identifiers. The one or more identifiers may include an item identifier for an item associated with the first data block and at least one of a first identifier designating at least a portion of the first data block as public data or privileged data and a second identifier designating at least a portion of the first data block as optional data or mandatory data.

Abstract: Systems, devices, methods, and computer-readable media are disclosed for utilizing group theoretic techniques to enable data exchange between a supervisory central processing unit (CPU) and a group of graphical processing units (GPUs). The CPU may be configured to utilize a tabu search metaheuristic to explore a solution space to determine an optimal solution to an optimization problem. More specifically, the CPU may determine a fragmentation of a solution space that yields multiple partitions of the solution space and may assign each partition to a respective GPU configured to calculate a computational result. The CPU may then determine a new fragmentation of the solution space based on the computational results received from the GPUs that yields new partitions of the solution space and may assign each new partition to a respective GPU configured to again generate a computational result based on its assigned new partition.

Abstract: Systems, methods, and computer-readable media are disclosed for an improved database. The systems, methods, and computer-readable media described herein may enhance the response time of databases and improve user experiences. In an example method described herein, a database management system may store, at a first database, a first data block. The first data block may be stored in association with one or more identifiers. The one or more identifiers may include an item identifier for an item associated with the first data block and at least one of a first identifier designating at least a portion of the first data block as public data or privileged data and a second identifier designating at least a portion of the first data block as optional data or mandatory data.

Abstract: Systems, devices, methods, and computer-readable media are disclosed for utilizing group theoretic techniques to enable data exchange between a supervisory central processing unit (CPU) and a group of graphical processing units (GPUs). The CPU may be configured to utilize a tabu search metaheuristic to explore a solution space to determine an optimal solution to an optimization problem. More specifically, the CPU may determine a fragmentation of a solution space that yields multiple partitions of the solution space and may assign each partition to a respective GPU configured to calculate a computational result. The CPU may then determine a new fragmentation of the solution space based on the computational results received from the GPUs that yields new partitions of the solution space and may assign each new partition to a respective GPU configured to again generate a computational result based on its assigned new partition.