48 results about "Program optimization" patented technology

The invention relates to a control method for an electroplating production line with an optimized PLC program. A multi-dimensional data analysis model is adopted, key parameters such as electroplating time, hanger circulation speed and bath occupation condition are comprehensively considered, and a process time relevance matrix is constructed; self-adaptive rhythm adjustment is carried out in the PLC, and the transmission rhythm is dynamically adjusted to enable circulation among the process nodes to be balanced; a multi-channel data acquisition system is connected to the PLC end, and the temperature, the current density, the voltage fluctuation, the plating solution concentration and the pH value key variables of the plating solution are monitored; a Bayesian optimization algorithm is adopted, electroplating process parameters are calculated based on historical data and real-time data, and an output control value of a PLC is dynamically adjusted; carrying out sampling analysis on the current consumption mode of each electroplating bath by adopting a PLC (Programmable Logic Controller), and establishing a power demand prediction model; current output is distributed through a dynamic power distribution algorithm, so that power consumption is kept in an optimal state; and in combination with a peak load reduction strategy, power supply is optimized in low load.

The invention relates to the technical field of industrial internet, in particular to an automatic scheduling system for multi-chip parallel burning, which is characterized in that a program optimization and deployment module dynamically adapts a basic program according to hardware resources of a target equipment cluster and process parameters of chips, generates an efficient optimized burning program, and sends the efficient optimized burning program to a server; verifying the digital digest through an integrated block chain network and performing safe deployment; the intelligent scheduling decision module utilizes a dynamic digital twinborn model to perceive the equipment state and deployed optimization program characteristics in real time, and intelligently generates a global burning task optimal allocation scheme and a scheduling instruction; the cooperative control module compares actual efficiency with expected efficiency in real time, quantifies deviation and then feeds back the deviation to the reinforcement learning scheduling engine, and the engine automatically adjusts and continuously iteratively optimizes the overall scheduling strategy. According to the invention, the scheduling efficiency of multi-chip parallel burning can be improved.

The invention provides a GPU program optimization method in a parallel environment, a plurality of target models are configured in a GPU cluster for parallel training, and the method comprises the following steps: performing strategy configuration and memory bottleneck pre-judgment on parallel training configuration of the target models; carrying out throughput optimization through parallel strategy combination and instruction-level performance monitoring by utilizing a pre-judgment result; kernel re-optimization is carried out on an instruction level bottleneck appearing in the optimization process, so that the model training efficiency and the GPU instruction execution efficiency are synchronously improved.

The embodiment of the invention provides an application program optimization method and device, electronic equipment and a storage medium. The method comprises the following steps: collecting resource calling data of an application program on user equipment; identifying a to-be-optimized resource set based on the resource calling data; executing optimization processing on the to-be-optimized resource set to obtain an optimized target application program; wherein the optimization processing comprises resource removal or resource relocation. Compared with a traditional static analysis method, the method has the advantages that through a data analysis mode driven by user behaviors, the risk of function missing possibly caused by blind deletion of resources is avoided, and more accurate and safer application program optimization is realized. Besides, for the resources which are used at low frequency but are still necessary to be reserved, the optimization of the initial packet volume is realized while the functional integrity is maintained through a processing mode of resource relocation instead of simple deletion, and the relationship between the user experience and the resource efficiency is balanced.

The invention relates to a digital twin virtual debugging method for jet printing manufacturing control optimization, and belongs to the technical field related to intelligent manufacturing and industrial control. Comprising the following steps: configuring a control logic program for each equipment sub-module in a jet printing manufacturing equipment twin model through an equipment master controller, and writing the control logic program into a corresponding virtual controller; establishing communication connection between each virtual controller and the twin model, verifying a control logic program in the virtual controller, and verifying and optimizing the control logic program through general control; and inputting each optimized control logic program into the entity controller of the corresponding equipment sub-module, sending a control instruction to the entity controller corresponding to the equipment sub-module of which the current motion is to be verified through general control, driving the equipment sub-module to execute an action, optimizing the control logic program in the current entity controller through the general control, and verifying the current motion of the equipment sub-module. And through multi-round program optimization, a control program of the jet printing manufacturing equipment is obtained. The debugging time and cost of the ink-jet printing equipment can be reduced.

The invention discloses a GPU resource aware matrix multiplication parallel performance analysis model construction method, which relates to the technical field of high-performance calculation, depends on the Roofline principle, combines a bandwidth model, a resource load model and an instruction dependence model to construct a performance analysis model, and is successfully applied to a matrix multiplication scene. And the parallel performance quantification under the matrix multiplication application of resource awareness is realized. The method is not limited by a platform, resource awareness can be achieved, maximized resource use and parameter setting of optimized performance can be distinguished through a performance analysis model in a matrix multiplication scene, and a foundation is laid for follow-up parallel program optimization work; the hardware parameters used in the method are completely based on the GPU hardware parameters which can be obtained publicly, so that non-GPU professionals can also use the model to evaluate the performance of the program.

The invention discloses a program compiling device and method, and belongs to the technical field of computers. The device comprises a receiving module used for receiving a log collected by an instrumentation program in the running process of a target program, the instrumentation program is a part of the target program, and the log comprises popularity data of at least one function in the target program; and the compiling module is used for performing biased compiling on the target program based on the popularity data of the at least one function to obtain an updated target program, and the biased compiling comprises compiling of a biased performance optimization operation or compiling of a biased code area compression operation. In this way, the program can be optimized and upgraded better, and user requirements are met.

The invention provides a dynamic instruction replacement method and device, equipment and a storage medium, and the method comprises the steps: determining a to-be-optimized instruction group according to instruction operation information of a plurality of instruction groups, and obtaining a replacement instruction group corresponding to the to-be-optimized instruction group, the performance of the replacement instruction group is better than that of the to-be-optimized instruction group, and the performance of the replacement instruction group is better than that of the to-be-optimized instruction group; and storing the replacement instruction group to a pre-occupied virtual address field, if the to-be-processed current instruction stream comprises the to-be-optimized instruction group, skipping to the virtual address field to execute the replacement instruction group when executing to the to-be-optimized instruction group in the current instruction stream, and skipping to the current instruction stream after the execution of the replacement instruction group is finished, and executing instructions behind the instruction group to be optimized. According to the method and the device, function-level instruction replacement is realized, the program optimization cost is reduced, and the program running efficiency can be effectively improved.

The invention provides a particle program optimization method based on a Shenwei architecture. The method comprises the following steps: analyzing program hotspots, finding and calculating a hotspot function, and optimizing the function by using five means of particle control, step number parameter control, data classification transmission, function expansion and function inline by utilizing the advantages of an SW26010pro architecture; the execution efficiency of the particle program is improved, the computing performance of the Shenwei architecture is fully played, and rapid and effective operation of the large-scale particle computing program is achieved.

The application relates to an automatic program optimization method and device based on a flow network generation model and a storage medium, wherein the method comprises the following steps: S1, an initial tensor program is acquired, a plurality of candidate tensor programs with the same logical function are obtained through calculation graph extraction, subgraph segmentation and transformation based on the acquired initial tensor program, and a data set composed of a plurality of samples is constructed based on the obtained candidate tensor programs, wherein the sample comprises a binary tuple composed of a calculation subgraph and a candidate tensor program, and the hardware execution time corresponding to the binary tuple; S2, a plurality of samples are selected from the data set, and a GFlowNet sampling model is trained offline; and S3, the trained GFlowNet sampling model is used to optimize a program to be optimized. Compared with the prior art, the GFlowNet adopts a non-iterative probabilistic sampling strategy, generates a plurality of high-performance programs, apportions the sampling overhead from one mode to another mode, and accelerates the convergence speed.

The application relates to the technical field of program optimization, and particularly discloses a small program code volume reduction method and device, a storage medium and a computer device. The method comprises the following steps: in response to a small program code volume reduction instruction, determining a target small program to be subjected to volume reduction; acquiring a global configuration file of the target small program, and parsing declared valid page paths from the global configuration file; scanning an engineering directory corresponding to the target small program, and identifying a plurality of page directories contained in the engineering directory; comparing each page directory with the declared valid page paths respectively, so as to screen out redundant page directories not contained in the valid page paths from the plurality of page directories; and deleting the redundant page directories and all files under the redundant page directories, so as to obtain a target small program after code reduction.

The invention discloses a converged media program list arrangement system and a generation method, and belongs to the technical field of media content management and intelligent scheduling. The system comprises a content acquisition module, a feature extraction module, a user portrait and platform model module, a program optimization and arrangement module, an intelligent conflict detection module, a self-adaptive generation module and a feedback optimization module. Unified management of program resources is realized through multi-source content collection and semantic tagging analysis; a multi-objective optimization model is constructed based on the user portrait and the platform strategy, and program lists adapted to different terminals are automatically generated; and dynamically adjusting a programming result by utilizing a conflict detection and self-learning feedback mechanism. According to the method, the intelligent level of convergence media program management can be remarkably improved, the cross-platform content distribution efficiency is optimized, and automatic generation and accurate pushing of the program list are realized.

Embodiments of the invention disclose an instruction fusion method and apparatus, an electronic device, a storage medium and a program product. The method comprises the steps of obtaining a to-be-optimized program; when it is determined that all instructions in the to-be-optimized program belong to the same basic block and the instructions with the dependency relationship in the to-be-optimized program are adjacently arranged, all continuous instruction sequences in the to-be-optimized program are obtained, to-be-fused instruction sequences are determined in all the continuous instruction sequences, and each set of to-be-fused instruction sequences is replaced with a corresponding fusion instruction; when it is determined that all the instructions in the to-be-optimized program do not belong to the same basic block or the instructions with the dependency relationship in the to-be-optimized program are not adjacently arranged, obtaining candidate instructions in the to-be-optimized program, determining to-be-fused instructions in the candidate instructions according to the fixed value-use chains corresponding to the candidate instructions, and fusing the to-be-fused instructions in the to-be-optimized program according to the determined to-be-fused instructions in the to-be-optimized program. And the target instruction sequence corresponding to each instruction to be fused is replaced with the corresponding fusion instruction, so that the program optimization efficiency and the program optimization capability are balanced.

The invention relates to an automatic program optimization method and system for synthesizing and eliminating an intermediate data structure by utilizing an induction program. The method comprises the following steps: acquiring a low-efficiency program which is input by a user and contains an intermediate data structure, and appointing the intermediate data structure which needs to be eliminated; a specified intermediate data structure is replaced by a scalar type, and related program fragments in the original program are rewritten using efficient program fragments of constant time complexity. The invention provides a novel induction program synthesis method aiming at the problem of eliminating an intermediate data structure, the induction program synthesis problem can be efficiently decomposed into simpler subtasks, so that the confronted efficiency problem is overcome, and the method has stronger expression ability than an existing fusion method, and can be widely applied to the field of data fusion. And the time for a programmer to carry out efficient system development can be greatly reduced.

According to an implementation, a computer-implemented method to determine an optimal configuration of a benchmark program is proposed. The method includes iteratively selecting a unique parameter set from a parameter space, each parameter set corresponding to a unique configuration of the benchmark program; running the benchmark program as configured for each parameter set at least once; determining an evaluation score for each parameter set from one or more runs using an evaluation heuristic; recording an optimal parameter set having a best evaluation score; and configuring the benchmark program based on the optimal parameter set.

The invention relates to an automatic program optimization method and device for an application program, computer equipment and a storage medium. The automatic program optimization method comprises the following steps: acquiring execution information and initial performance data of each instruction in a plurality of instructions when the application program runs; determining a hotspot instruction according to the execution information of each instruction in the plurality of instructions; obtaining an instruction type of the hotspot instruction, judging an instruction optimization mode corresponding to the instruction type, and if the instruction optimization mode is judged to be based on the configuration template, obtaining a matched target optimization template from a preset template library according to the instruction type; performing code optimization on the hotspot instruction according to an optimization strategy and a code mode configured in the target optimization template to generate an optimization code of the hotspot instruction; and performing program optimization on the application program according to the optimization code and the initial performance data of the application program. According to the method, the accuracy, the automation degree and the efficiency of software performance optimization are improved.

The invention discloses a real-time monitoring system for a dry net cleaning vacuum valve. The real-time monitoring system comprises a detection module, an alarm module, a feedback module, a program optimization module, a bracket structure and a data storage module, according to the invention, a dry net cleaning program is optimized by using off-machine limiting and self-processing of the fixing bracket, and the real-time use condition of the vacuum valve is efficiently detected without cost. And the conditions of wastage reduction and dry screen printing caused by blockage of the vacuum valve are effectively avoided, and the quality of finished paper is improved. Meanwhile, by adding real-time feedback and alarm functions on a DCS picture, the abnormity of the vacuum valve can be found in time, the failure rate is reduced, the workload of personnel is reduced, and the market competitiveness of a company is further improved. In addition, the detachable support facilitates later maintenance, the troubleshooting time is shortened, and the data storage module facilitates later analysis and summarization of equipment operation conditions.

The invention discloses a tensor program functionalization method, tensor program functionalization equipment and a tensor program product. The method comprises the following steps: acquiring a tensor program, and constructing a graph-level intermediate representation program of the tensor program; performing mutation rewriting and tensor version relation labeling on the graph-level intermediate representation program based on a memory dependency graph and an immutable operator of the graph-level intermediate representation program to obtain a rewritten graph-level intermediate representation program; the immutable operator is used for replacing a mutation operator in a graph-level intermediate representation program, and a new version tensor output by the immutable operator and an input source tensor have independent memories; and performing program optimization on the rewritten graph-level intermediate representation program to obtain a functional tensor intermediate representation program based on the static single assignment. The Graph-Level IR program is combined with the static single assignment SSA, so that the side effect of tensor mutation in the Graph-Level IR program is thoroughly eliminated, and the optimization performance of a deep learning compiler on the tensor program is improved.

Various embodiments of the present technology generally relate to systems and methods for providing a framework for optimizing configuration settings of application instances. In certain embodiments, a method may comprise operating an optimizer service to implement an application optimizer process to improve performance of an application instance. The process may include receiving a plurality of checks from an application development system for the application instance, the plurality of checks including scans and fixes for configuration settings of the application instance. The process may further include executing the scans on the application instance, determining a selection of fixes configured to improve the performance of the application instance in response to a result of the scans, providing a notification to the application instance recommending user implementation of the selection of fixes, and providing analytics data corresponding to the user implementation of the selection of fixes to the application development system.

In variants, a program optimization method can include: receiving a program; determining a set of proxy inputs for the program; generating a set of intermediate traces for the program; and generating a set of execution traces for the program, wherein the set of execution traces can include executor fusions associated with device kernels for hardware accelerators. During runtime, program results can be computed by executing an execution trace instead of executing the program.

The embodiment of the invention discloses a program tuning code marking method and device, a terminal and a medium, and the method comprises the steps: marking an optimization code in a code editor; obtaining a first program performance information file corresponding to the to-be-optimized code; obtaining a second program performance information file corresponding to the modified and optimized code; comparing the first program performance information file with the second program performance information file to obtain a difference function between the first program performance information file and the second program performance information file; and searching the difference function by utilizing a code editor difference tool, and determining a modified source code according to the mark. The change condition of the program performance in the whole source code modification process can be recorded, and a developer can carry out optimization, backtracking and iteration on the program performance conveniently.

A method for controlling machining defects on the thin-walled edges of deformed TiAl alloy blades belongs to the field of aeroengine machining technology. The method includes the following steps: first, designing a process table based on the blade structure, using this process table as a unified machining benchmark and clamping location; dividing the thin-walled blade areas prone to chipping and falling into vulnerable areas; during milling, controlling these vulnerable areas, employing a variable parameter programming strategy, and programming the blade using numerical control programming software; outputting the program's cutting force values; applying maximum cutting force limits to program segments, and outputting the optimized program; clamping the deformed TiAl alloy blade's ends in a machining fixture via the process table, installing the fixture on a machining machine; and inputting the optimized program output into the machining machine to machine the blade. This method effectively addresses difficult machining issues such as surface chipping and falling during milling of TiAl alloy workpieces.

The invention discloses a program optimization method based on an LLVM framework and SMT / ILP solution, which realizes efficient program synthesis under hardware resource constraint through two stages of intermediate code generation and compilation optimization. The front end divides transaction statement blocks and optimizes dependence, and the rear end generates P4 codes adaptive to the target platform through mathematical modeling and solving, so that resource occupation and debugging cost are remarkably reduced, and the method is suitable for program development of programmable network equipment.

The invention belongs to the technical field of application program optimization, and particularly relates to an Android application self-starting detection method and device and a computer program product. The method comprises the following steps: acquiring a dynamic registration request of an application program to a broadcast receiver; in response to the request, capturing broadcast parameters using a code instrumentation technique; and simulating to send the broadcast represented by the broadcast parameter, and detecting the self-starting behavior of the application program caused by the broadcast. The invention provides a scheme capable of detecting the self-starting behavior of the Android application based on the dynamic broadcast.

According to an implementation, a computer-implemented method to determine an optimal configuration of a benchmark program is proposed. The method includes iteratively selecting a unique parameter set from a parameter space, each parameter set corresponding to a unique configuration of the benchmark program; running the benchmark program as configured for each parameter set at least once; determining an evaluation score for each parameter set from one or more runs using an evaluation heuristic; recording an optimal parameter set having a best evaluation score; and configuring the benchmark program based on the optimal parameter set.

The invention discloses a program optimization method and device, a program product and electronic equipment, and relates to the field of financial science and technology, the method comprises the steps that static characteristics and dynamic characteristics of a target program are collected, the static characteristics are at least used for representing control flow information, data dependence information and function call information of the target program, and the dynamic characteristics are at least used for representing control flow information of the target program; the dynamic characteristics are at least used for representing function execution information in the target program; performing feature fusion on the static features and the dynamic features to obtain target features of the target program; based on the target feature, determining a program label of the target program, the program label being used for representing a program type of the target program; and performing performance optimization on the target program based on the target feature through a preset model corresponding to the program tag. The technical problem that in the prior art, the program optimization effect is poor due to the fact that the program is optimized only based on the static scanning result of the program is solved.

None of the existing quantum programming languages provide specialized support for programming patterns such as conditional adjoint or conjugate adjoint. As a result, the compilers of these languages cannot take advantage of the optimization opportunities mentioned in this disclosure. In addition, none of the available quantum programming languages support the automatic conversion of circuits using clean qubits to circuits using idle qubits. Consequently, the resulting circuits typically use more qubits than necessary. Thus, embodiments of the techniques of this disclosure allow one to run the above circuits on smaller quantum devices. Previous multiplication circuits used (expensive) controlled addition. Embodiments of the techniques of this disclosure employ multipliers that work using conditional adjoint addition, which are cheaper to implement on near-term and large-scale quantum hardware. For a large number of qubits, the circuit depth is saved by a factor of 1.5 to 2.

The invention provides a runtime program optimization method and device oriented to pipeline parallelism, and the method comprises the steps: recognizing a time window in a heterogeneous parallel computing process, the time window being an idle time period generated between accelerator equipment and a host processor due to task dependence or data synchronization; within the time window, collecting runtime information in a computing process from an accelerator device; dynamically selecting a compilation optimization strategy according to the characteristics of the time window and the runtime information; and based on the selected compilation optimization strategy, executing compilation optimization in the time window and generating an optimization code. According to the method, the idle time window in the calculation process can be effectively identified and utilized, and real-time performance optimization is achieved.

The invention provides a program optimization method and device, electronic equipment and a storage medium, and relates to the technical field of computers, in particular to the field of program page loading. According to the specific implementation scheme, the method comprises the following steps: counting completion time consumption and midway exit rate of each initialization loading operation in a plurality of serial initialization loading operations in a page loading process of a first program; and under the condition that the completion time consumption and the midway exit rate of each initialization loading operation do not meet preset conditions, combining and optimizing the subprograms corresponding to the plurality of initialization loading operations in the first program to obtain a second program, the second program comprises a subprogram corresponding to a target initialization loading operation obtained by combining and optimizing a plurality of initialization loading operations.

This system program optimization system is provided with an information processing device and a system program optimization device. The information processing device receives a setting of a constant value with respect to an external input signal inputted from an external device connected to a numerical control device, and outputs the constant value to a system program optimization device. The system program optimization device acquires the constant value set to the external input signal inputted from the external device connected to the numerical control device, compiles a source code of a system program into an object program which can be executed by the numerical control device, and optimizes the system program by the constant value of the external input signal in the compilation.