59results about How to "Improve execution time" patented technology

A bytecode execution system and method for increasing the execution speed of invoking and returning from Methods while minimizing the memory footprint required to support this. The system includes a virtual machine with a class loader and an interpreter or, alternatively, a hardware accelerator. Speed and memory enhancements are realized by establishing an activation stack frame template with a set of criteria. Each Method from subject code is examined to determine whether the Method conforms to the criteria of the stack frame template. If the Method conforms, an activation frame for that Method based on the activation stack frame template is created when the Method is invoked. Otherwise, an activation frame based on standard virtual machine criteria is created. An access structure is associated with each Method and a Method routing structure is created for each class.

The invention discloses a difference privacy protection K-means cluster method based on a profile coefficient; the method comprises the following steps: 1, normalizing all data in a data set D; 2, averagely dividing the data set D with N records into K sets; 3, solving an attribute vector sum (sum <o><k>) and a record number (num <0><k>) of all records in a set Ck; 4, calculating the distance between each record (ai) to the center (uk) of K clusters; 5, calculating the recording number (num) of the cluster and the attribute vector (sum) of all records; 6, calculating a profile coefficient Sk of the k clusters, and adding random noises (noise <t><k>) onto numk and sumk; 7, calculating a new cluster center; 8, calculating the distance between the new cluster center and the cluster center ofthe previous iteration, and finishing the algorithm if the distance is smaller than a threshold. An existing method in the big data cluster analysis under a distributed environment is severe in information leakage; the difference privacy protection K-means cluster method can solve said problems.

The invention relates to a flow line task self-adaptation parallelization method which includes flow line initializing setting and task self-adaptation disintegration. The method has the advantages that the tasks long in execution time is disintegrated into small tasks according to the execution time of each task; a configuration file is used to configure each task of a flow line from starting to ending, each task can have many pre-tasks and subsequent tasks, and different tasks control task contents and task completing progress through messages; the method is achieved through a parallelization distribution processing algorithm, overall software execution speed is conveniently and effectively increased by self-adaptation task disintegration, and user waiting time is reduced.

A non-accurate task parallel processing method based on MapReduce comprises the steps that (1) M Map subtask executing nodes and a Check node are selected, and the Check node stores logic for judging whether a task to be executed with non-accuracy computing feature is completed; (2) the task is divided into N Map subtasks to be distributed to the M executing nodes for executing; (3) after each Map subtask is executed, task executing-finishing information and result information related to judging logic are sent to the Check node; (4) each time the Map subtask executing-finishing information is received, the information is accumulated into the previous result by the Check node until the accumulated result meets the requirement of judging logic; (5) Map subtasks in which the executing-finishing information is not received by the Check node are ended, a Map task executing process is finished, and a Reduce subtask is carried out synchronously; and (6) the Reduce task executing result is obtained, and whole task executing is finished.

The invention discloses a protein rapid detection method based on a matched microalgae protein characteristics sequence label and a system thereof; the method includes steps of collecting a microalgae sample, and acquiring its protein sequence set, converting it to a standard protein sequence set, performing splitting and configuration treatment, and converting the protein sequence set to a key/value pair list specific to a given sequence segment; after summarizing the key/value pair list, performing an intersection operation; when the result is not empty, confirming that one data of the data set contains the characteristic protein sequence. The system mainly includes a detection data input module, a key/value pair list module, a detection identification module, a characteristic value intersection operation module, and an output module. Through indexing the protein sequence, the method and the system judge the protein homology and similarity level; by comparing with the traditional method of comparing data one by one, the algorithmic identification accuracy is and efficiency are significantly improved, the indexing efficiency is greatly promoted; the method and system have good practicability, and the algorithmic time complexity is low, the performance is high; therefore, the method and the system are significant to study proteomics.

The invention discloses a secure cloud data deduplication method and system based on trusted hardware. Comprising the following steps: firstly, performing data encryption and label generation on an original file; sending the encrypted file and a corresponding label to a cloud storage platform; and enabling the cloud storage platform to store the to-be-encrypted file without the same copy after repeatedly detecting the label, and assigning the file permission. A secure trusted hardware area TH is configured in a cloud storage platform, all algorithms and operations related to user file privacy are realized in trusted hardware, and a code running in the trusted hardware is called CEnclave. Before a user communicates with a cloud storage platform, remote authentication needs to be carried out on the user and the TH to ensure secure transmission of data, and after the TH receives the data transmitted by the user, ORAM implicit reading is carried out on the TH and a cloud storage server CSS to realize a secure de-duplication process.

The invention relates to an optimized NURBS and DDA curve two level interpolation method which is composed of a track generation phase, a conversion phase and a DDA interpolation phase. The track generation phase mainly executes NURBS interpolation algorithm, a position of a corresponding point of the next interpolation period is generated according to the track generation algorithm. The DDA interpolation phase mainly generates corresponding servo burst according to the position increment of each interpolation period to transmit to a servo system. The conversion phase mainly converts the position of each interpolation period in the NURBS interpolation phase into a position increment of the DDA phase. Each phase is connected through fifo. The optimized NURBS and DDA curve two level interpolation method has the advantages that the system communication delay is low, the efficiency is high, the portability is good, and the program is friendly to Cache.

A method of reordering a sequence of code for processing by a target data processor in order to reduce an execution time for said code on said target data processor is disclosed. The method comprises the steps of: in response to a request to execute said sequence of code, loading said sequence of code into a volatile data store associated with said target data processor; analyzing said sequence of code in relation to properties of said target data processor; identifying interlocks within said sequence of code when executing on said target data processor, in which a portion of code would be stalled while waiting for an earlier portion to complete; reordering said sequence of code to remove at least some of said interlocks; and executing said reordered sequence of code; wherein said steps of analyzing, identifying, reordering and executing are performed by said target data processor.

The invention discloses a sample importance resample particle filter hardware implementation method based on an FPGA. The method includes the following steps that firstly, a particle generation module is used for receiving input vectors to generate particles and then outputting the particles to a particle renewing module and a resample module; secondly, the particle renewing module is used for conducting renewing, namely, weight calculation and weight normalization, on the particles generated in the first step and then outputting the particles to the resample module; thirdly, the resample module conducts resample and state renewing on the particles renewed in the second step or the particles generated in the first step and then feeding the particles back to the particle generation module in the first step; fourthly, an output generation module conducts data generation and output on the particles renewed in the second step or the particles generated in the first step.

The invention relates to a data processing method and apparatus. The method comprises: setting a dependency relationship between at least one upstream tool and a downstream tool; triggering the execution of the upstream tool; judging whether the at least one upstream tool is fully executed or not and an execution result meets a dependency condition of the downstream tool or not; and if the upstream tools are all executed and the execution result meets the dependency condition of the downstream tool, triggering the execution of the downstream tool. In the prior art, a downstream tool is triggered without judging whether an execution result is successful or failed after an upstream tool is executed. According to the data processing method provided by an embodiment, the dependency condition of the downstream tool on the upstream tool can be set, so that unnecessary triggering of the downstream tool, unnecessary execution or running errors of the tool, prolonging of execution time and resource wastes are avoided.

In a multifunction peripheral, image data generated by a scanner section is transferred to an image processor via a data bus. If it is determined that an image processing and another image processing are not executed in parallel, the transfer rate of image data is set to a first transfer rate, whereas if it is determined that the image processing and the other image processing are executed in parallel, one of a plurality of different transfer rates lower than the first transfer rate is selected based on a result of determination of whether or not settings associated with the image processing are predetermined settings and a result of determination of whether or not settings associated with the other image processing are other predetermined settings, and the transfer rate of image data is set to the selected one.

The invention relates to the technical field of lasers, and provides a laser machining method based on EBL and an application method thereof. The machining method includes: determining a related parameter of a first reflection peak corresponding to a grating region structure of a uniform grating laser; obtaining a related parameter interval of the grating region structure which is allowed to be fabricated by the laser; calculating a maximum coupling coefficient capable of being obtained by a uniform grating and a related parameter value of the correspondingly fabricated grating region structure according to the related parameter interval of the grating region structure which is allowed to be fabricated; calculating a minimum grating region length of a sampling grating according to the related parameter of the first reflection peak and the maximum coupling coefficient; and regarding the minimum grating region length of the sampling grating and the related parameter value of the grating region structure as input parameters for fabrication of the grating region of the laser. According to the method, the conventional uniform grating structure is replaced by the sampling grating containing the minimum grating region length, the total amount of lithography can be reduced, the complexity of the machining process is improved, and the production efficiency of laser chips is improved.

The present invention aims to precisely detect an image flow generation level and reliably recover image flow. An image forming apparatus is provided with an image forming part forming a color toner image on a photoreceptor drum; an intensity sensor detecting the intensity of the color toner image of the photoreceptor drum; and a control part for determining the surface state of the photoreceptor drum according to the detecting result of the intensity sensor, wherein the image forming part forms a first pattern image and a second pattern image different from the first pattern image on the surface of the photoreceptor drum, and the control unit determines the intensity of the first pattern image and the second pattern image formed on the surface of the photoreceptor drum and determines the surface state of the photoreceptor drum according to the detecting results of the intensity sensor.

Business personnel are helped to find frequent processes in a business log by means of a sequence mining method, and a process may be further improved in the aspects of execution time and efficiency by improving frequent execution actions; besides, according to abnormal actions occurring at low frequency, an abnormal point mining method is designed to help the business personnel to find the processes. A log mining system framework graph is shown in the figure.