Abstract: Disclosed herein are a parameter server and a method for sharing distributed deep-learning parameters using the parameter server. The method for sharing distributed deep-learning parameters using the parameter server includes initializing a global weight parameter in response to an initialization request by a master process; performing an update by receiving a learned local gradient parameter from the worker process, which performs deep-learning training after updating a local weight parameter using the global weight parameter; accumulating the gradient parameters in response to a request by the master process; and performing an update by receiving the global weight parameter from the master process that calculates the global weight parameter using the accumulated gradient parameters of the one or more worker processes.

Abstract: Disclosed herein are a parameter server and a method for sharing distributed deep-learning parameters using the parameter server. The method for sharing distributed deep-learning parameters using the parameter server includes initializing a global weight parameter in response to an initialization request by a master process; performing an update by receiving a learned local gradient parameter from the worker process, which performs deep-learning training after updating a local weight parameter using the global weight parameter; accumulating the gradient parameters in response to a request by the master process; and performing an update by receiving the global weight parameter from the master process that calculates the global weight parameter using the accumulated gradient parameters of the one or more worker processes.

Abstract: A machine learning method for incremental learning builds a model by using training data and incrementally updates the built model by using only a new weight generated based on new training data.

Abstract: Disclosed herein are a parameter server and a method for sharing distributed deep-learning parameters using the parameter server. The method for sharing distributed deep-learning parameters using the parameter server includes initializing a global weight parameter in response to an initialization request by a master process; performing an update by receiving a learned local gradient parameter from the worker process, which performs deep-learning training after updating a local weight parameter using the global weight parameter; accumulating the gradient parameters in response to a request by the master process; and performing an update by receiving the global weight parameter from the master process that calculates the global weight parameter using the accumulated gradient parameters of the one or more worker processes.

Abstract: Disclosed herein are an apparatus and method for interfacing with common memory. The apparatus for interfacing with common memory includes a computer-input/output (I/O)-interface-protocol-processing unit for receiving a packet for accessing common memory from a computer; a direct memory access unit for transforming the packet into an instruction for performing any one of reading from and writing to the common memory; and a common memory interface unit for transmitting the instruction to the common memory and receiving information about whether execution of the instruction is completed from the common memory.

Abstract: A parameter-sharing apparatus includes a memory allocation unit for managing allocation of a memory area, in which a parameter is to be stored, to a memory box, and updating a mapping table stored in the memory box based on allocation management of the memory area, and an operation processing unit for providing the memory allocation unit with parameter information required for the allocation management of the memory area in which the parameter is to be stored and sharing the parameter stored in the memory box.

Abstract: Disclosed herein are an apparatus and method for interfacing with common memory. The apparatus for interfacing with common memory includes a computer-input/output (I/O)-interface-protocol-processing unit for receiving a packet for accessing common memory from a computer; a direct memory access unit for transforming the packet into an instruction for performing any one of reading from and writing to the common memory; and a common memory interface unit for transmitting the instruction to the common memory and receiving information about whether execution of the instruction is completed from the common memory.

Abstract: Disclosed herein are a parameter server and a method for sharing distributed deep-learning parameters using the parameter server. The method for sharing distributed deep-learning parameters using the parameter server includes initializing a global weight parameter in response to an initialization request by a master process; performing an update by receiving a learned local gradient parameter from the worker process, which performs deep-learning training after updating a local weight parameter using the global weight parameter; accumulating the gradient parameters in response to a request by the master process; and performing an update by receiving the global weight parameter from the master process that calculates the global weight parameter using the accumulated gradient parameters of the one or more worker processes.

Abstract: Disclosed herein are an apparatus and method for sharing memory between computers. The apparatus for sharing memory between computers includes multiple memory adapters, installed in corresponding ones of multiple computers, for receiving an address corresponding to an instruction from the computers and transforming the received address into an instruction in the form of a packet; and shared memory for transforming the instruction in the form of the packet, received from the multiple memory adapters, into an address and performing an operation corresponding to the instruction for a memory cell corresponding to the address.

Abstract: Disclosed herein are a parameter-sharing apparatus and method. The parameter-sharing apparatus includes a memory allocation unit for managing allocation of a memory area, in which a parameter is to be stored, to a memory box, and updating a mapping table stored in the memory box based on allocation management of the memory area, and an operation processing unit for providing the memory allocation unit with parameter information required for the allocation management of the memory area in which the parameter is to be stored and sharing the parameter stored in the memory box.

Abstract: Provided is a method of dynamically controlling power in a multicore environment including a multicore processor which includes a plurality of cores and a scheduler. The method includes determining whether a management policy is set, collecting frequency change information used to change frequencies of the plurality of cores when it is determined that the management policy is set, calculating an average load of each core on a basis of the frequency change information, calculating an average frequency of each core according to the calculated average load of each core, comparing the average frequency of each core and a predetermined threshold value, and setting a next frequency of each core according to the comparison result.

Abstract: Provided is a method of dynamically controlling power in a multicore environment including a multicore processor which includes a plurality of cores and a scheduler. The method includes determining whether a management policy is set, collecting frequency change information used to change frequencies of the plurality of cores when it is determined that the management policy is set, calculating an average load of each core on a basis of the frequency change information, calculating an average frequency of each core according to the calculated average load of each core, comparing the average frequency of each core and a predetermined threshold value, and setting a next frequency of each core according to the comparison result.

Abstract: A computing system for virtualization-based resource management includes a plurality of physical machines, a plurality of virtual machines and a management virtual machine. The virtual machines are configured by virtualizing each of the plurality of physical machines. The management virtual machine is located at any one of the plurality physical machines. The management virtual machine monitors amounts of network resources utilized by the plurality of physical machines and time costs of the plurality of virtual machines, and performs a resource reallocation and a resource reclamation.

Abstract: An apparatus and method for measuring power using a sensing tag are provided. The power measuring apparatus includes a power sensing tag and a power measurement control system. The power sensing tag is installed in a line along which power is supplied to a load of a target device, and measures power consumed by the load. The power measurement control system receives power information measured by the power sensing tag from the power sensing tag, and determines power consumed by the load by using the measured power information.

Abstract: Provided are a virtualization based high availability cluster system and a method for managing failures in a virtualization based high availability cluster system. The high availability cluster system includes a plurality of virtual nodes, and a plurality of physical nodes each including a message generator for generating a message denoting that the virtual nodes are in a normal state and transmitting the generated message to virtual nodes in a same physical node. One of the virtual nodes not included in a first physical node among the plurality of the physical nodes takes over resources related to a service if a failure is generated in one of virtual nodes included in the first physical node.

Abstract: A computing system for virtualization-based resource management includes a plurality of physical machines, a plurality of virtual machines and a management virtual machine. The virtual machines are configured by virtualizing each of the plurality of physical machines. The management virtual machine is located at any one of the plurality physical machines. The management virtual machine monitors amounts of network resources utilized by the plurality of physical machines and time costs of the plurality of virtual machines, and performs a resource reallocation and a resource reclamation.

Abstract: Provided are a virtualization based high availability cluster system and a method for managing failures in a virtualization based high availability cluster system. The high availability cluster system includes a plurality of virtual nodes, and a plurality of physical nodes each including a message generator for generating a message denoting that the virtual nodes are in a normal state and transmitting the generated message to virtual nodes in a same physical node.