Abstract: A computing method for ridesharing paths, a computing apparatus and a recording medium using the same are provided. This invention finds the ridesharing paths out from the target trajectory which have ridesharing relationship with a plurality of candidate trajectories. People on the ridesharing paths can share a ride on a part of or whole target trajectory. In the computing method, a trajectory is transformed into a sequence of blocks. After target trajectory and a plurality of the candidate trajectories are transformed, it is determined whether each of the blocks of the candidate trajectories overlaps with at least one of the blocks of the target trajectory. Then, ridesharing paths are calculated according the screened candidate trajectories. This method is many-to-one method which compares a plurality of the candidate trajectories to one target trajectory at one time.

Abstract: A computing method for ridesharing paths, a computing apparatus and a recording medium using the same are provided. This invention finds the ridesharing paths out from the target trajectory which have ridesharing relationship with a plurality of candidate trajectories. People on the ridesharing paths can share a ride on a part of or whole target trajectory. In the computing method, a trajectory is transformed into a sequence of blocks. After target trajectory and a plurality of the candidate trajectories are transformed, it is determined whether each of the blocks of the candidate trajectories overlaps with at least one of the blocks of the target trajectory. Then, ridesharing paths are calculated according the screened candidate trajectories. This method is many-to-one method which compares a plurality of the candidate trajectories to one target trajectory at one time.

Abstract: The present disclosure provides a direct memory access method, system and host module for a virtual machine. The direct memory access method comprises the following steps. Firstly, a host virtual memory address is acquired according to a guest physical memory address. Next, a shared-memory is configured to access data corresponding to the host virtual memory address. Then, the shared-memory is mapped to a continuous memory having a plurality of continuous memory addresses. Finally, the continuous memory is utilized to provide an apparatus driver to access the data corresponding to the host virtual memory address.

Abstract: Mapping tasks to physical processors in parallel computing system may include partitioning tasks in the parallel computing system into groups of tasks, the tasks being grouped according to their communication characteristics (e.g., pattern and frequency); mapping, by a processor, the groups of tasks to groups of physical processors, respectively; and fine tuning, by the processor, the mapping within each of the groups.

Abstract: A data processing apparatus and a data processing method thereof are provided. The data processing apparatus includes a register and a processor electrically connected to the register. The register is stored with a plurality of data. The plurality of data each includes a first sub-datum and a second sub-datum. The plurality of first sub-data corresponds to a first column and the plurality of second sub-data corresponds to a second column. The processor compresses the first sub-data by a first compression algorithm according to a first characteristic of the plurality of first sub-data and compresses the second sub-data by a second compression algorithm according to a second characteristic of the plurality of second sub-data.

Abstract: A data processing apparatus and a data processing method thereof are provided. The data processing apparatus includes a register and a processor electrically connected to the register. The register is stored with a plurality of data. The plurality of data each includes a first sub-datum and a second sub-datum. The plurality of first sub-data corresponds to a first column and the plurality of second sub-data corresponds to a second column. The processor compresses the first sub-data by a first compression algorithm according to a first characteristic of the plurality of first sub-data and compresses the second sub-data by a second compression algorithm according to a second characteristic of the plurality of second sub-data.

Abstract: Mapping tasks to physical processors in parallel computing system may include partitioning tasks in the parallel computing system into groups of tasks, the tasks being grouped according to their communication characteristics (e.g., pattern and frequency); mapping, by a processor, the groups of tasks to groups of physical processors, respectively; and fine tuning, by the processor, the mapping within each of the groups.

Abstract: Mapping tasks to physical processors in parallel computing system may include partitioning tasks in the parallel computing system into groups of tasks, the tasks being grouped according to their communication characteristics (e.g., pattern and frequency); mapping, by a processor, the groups of tasks to groups of physical processors, respectively; and fine tuning, by the processor, the mapping within each of the groups.