1081 results about "Graphics processing unit" patented technology

The invention discloses a large-scene three-dimensional reconstruction method based on three-dimensional Gaussian sputtering, and relates to computer graphics. The method comprises the following steps: collecting a multi-view image set of a large scene; obtaining a scene sparse point cloud according to the multi-view image set; performing monocular depth estimation on the multi-view image by using a pre-trained depth prediction network to obtain monocular depth estimation priori; the method comprises the following steps of: performing global training on a scene by utilizing scene sparse point cloud and monocular depth estimation prior to obtain an initial three-dimensional Gaussian model, and performing space grid division on the initial three-dimensional Gaussian model to obtain a plurality of scene blocks with axis alignment bounding boxes; setting image view angle data of each scene block; performing deep supervised training on the Gaussian ellipsoids in the plurality of scene blocks by using a parallel GPU (Graphics Processing Unit); combining the trained scene blocks to obtain a final three-dimensional Gaussian model; in view of low geometric structure reconstruction precision caused by only depending on color information of a multi-view image in large-scene three-dimensional rendering, the method improves the reconstruction precision of large-scene rendering.

The invention provides a computing system and method for GPU computing power scheduling, and the method comprises the following steps: 1, obtaining computing task data, and carrying out the information collection of each task through receiving a to-be-processed computing task queue; according to the invention, through task information collection and deep analysis, and GPU resource omnibearing monitoring and workload prediction, deep understanding of tasks and GPU resources is realized, highly accurate computing power distribution can be carried out, resource waste is avoided, and the resource utilization rate is improved; advanced technologies such as a knowledge graph, mixed integer programming and deep reinforcement learning are fused through the intelligent scheduling model, the fusion mode is innovative in the GPU computing power scheduling field, the limitation of singleness of a traditional scheduling model is overcome, the method can better adapt to a complex and changeable computing environment, the task execution efficiency is optimized, and the task scheduling efficiency is improved. The adaptability and competitiveness of the whole system in a complex environment are remarkably improved, and an excellent solution is provided for the GPU computing power scheduling problem in the field of high-performance computing.

The invention discloses a resource scheduling method for dynamic rendering and related equipment, and relates to the technical field of dynamic rendering, and the method comprises the following steps: obtaining time sequence data of a user touch event; based on the time sequence data, predicting a user behavior state through a behavior prediction model, and determining a current resource demand level; dynamically adjusting a resource allocation strategy and a video memory management strategy of the graphic processing unit according to the resource demand level; and executing a rendering task based on the adjusted resource allocation strategy, and performing picture output by adopting a double-buffer architecture so as to reduce delay caused by time sequence mismatch. According to the method and the device, by establishing closed-loop control of a user behavior-resource demand-supply strategy, accurate scheduling and efficient utilization of resources are realized, the problems of time sequence mismatch, resource waste and jamming in the traditional technology are solved, the rendering efficiency and the user experience are improved, and technical support is provided for high-concurrency and low-delay scenes.

The invention discloses a hybrid expert model reasoning method based on cooperation of a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit), and belongs to the field of deep learning. According to the method, a CPU-GPU computing framework of a hybrid expert model is constructed, heterogeneous computing resource loads are effectively balanced, and the hardware utilization rate is remarkably increased; an intelligent cache management mechanism based on dynamic priority scores is provided, high-demand experts are reserved preferentially, and the transmission overhead caused by cache missing is reduced; through pipeline parallel design for separating calculation and transmission tasks, CPU calculation and PCIe transmission are overlapped in the GPU execution period, and delay is effectively hidden. In addition, in combination with a multi-layer expert activation prediction prospective prefetching mechanism, the expert cache hit rate is improved. The method is compatible with hybrid expert models of different scales and structures, and stable and efficient reasoning acceleration is realized on a resource-limited heterogeneous platform.

The invention provides a GPU resource scheduling optimization method and device based on a hybrid model and a medium, and the method comprises the steps: receiving a plurality of new tasks, and obtaining a target resource demand and a target priority of any new task; obtaining various current performance indexes of the GPU resources, and storing the various performance indexes as target time sequence data; calling a CNN-LSTM hybrid model deployed in a production environment, and performing prediction learning on the target time sequence data to obtain a prediction result; selecting the most suitable target scheduling strategy according to the prediction result, various performance indexes and the target priority of any new task; according to the target scheduling strategy and the target resource demand of any new task, GPU resource allocation is carried out on any new task; the task execution condition and the GPU resource use condition are monitored in real time, and a monitoring result is fed back; and according to a monitoring result, dynamically adjusting a currently used target scheduling strategy so as to realize adaptive optimization of GPU resources.

The invention discloses a GPU (Graphics Processing Unit) and parallel IO (Input / Output) collaborative optimization method based on mode heterogeneous computing, and particularly relates to the field of heterogeneous computing optimizing.The method comprises the following steps: monitoring mode characteristics during execution of a target computing task in real time through an instruction analyzer, and synchronously collecting dynamic parameters of a heterogeneous computing environment to generate a multi-dimensional resource situation matrix; based on the mode and the resource situation matrix, collaborative optimization operation of the binding equipment dimension and the scheduling time dimension is executed; and constructing a three-level adaptive transmission mechanism and periodically implementing feedback optimization according to an execution state of the three-level adaptive transmission mechanism. According to the GPU and parallel IO collaborative optimization method based on mode heterogeneous computing, the data prefetching hit rate is increased by dynamically sensing the matching relation between a heterogeneous computing mode and an I / O access mode; end-to-end delay is reduced and task contention conflicts are reduced through a dynamic matching strategy of an equipment capability portrait and a task type; and the heterogeneous storage bandwidth peak value utilization rate and the I / O throughput are improved by differentially utilizing the hierarchical characteristics of the multi-stage storage.

The invention discloses a power supply circuit, a server and a time sequence control method, relates to the field of server power supply, and solves the problem that the demand of loads such as a GPU (Graphics Processing Unit) and a CPU (Central Processing Unit) on short-time peak power is increasingly increased at present. According to the power supply circuit, the long-time power supply module and the peak value compensation module are introduced to work cooperatively, the long-time power supply module provides stable energy conversion, and the long-term stable power supply requirement of a load is met; and the peak compensation module dynamically adjusts power supply according to the load demand and the working state of the long-time power supply module, provides or recovers energy, and meets the instantaneous peak power demand, thereby avoiding the defect that the peak current cannot meet the requirement or needs a large-scale redundancy and high-cost capacitor row in the traditional scheme, and improving the reliability of the system. According to the design, power supply stability can be guaranteed, system cost and size are reduced, and overall power supply efficiency and response capability are improved.

The invention provides an intelligent computing center model development method and device oriented to the Plevatory computing power, and relates to the technical field of computing power infrastructure, the method comprises the following steps: S1, receiving first mirror image data of a to-be-developed model; s2, a first node is obtained, the first node is a node occupying graphics processing unit (GPU) resources of the intelligent computing center, and the first node comprises a debugging service component; s3, adjusting the first mirror image data based on the debugging service component; and S4, after the adjustment of the first mirror image data is completed, closing the first node, and releasing GPU resources occupied by the first node. According to the method, GPU resources can be prevented from being continuously occupied, the load of the intelligent computing center is reduced, the intelligent computing center can provide computing power service for more users, and the general application of computing power is realized.

The invention discloses an instruction execution acceleration method based on a GPGPU and a GPGPU architecture, and relates to the field of high-performance computation.The method comprises the steps that a thread execution task output by a host compiler is received, and the thread execution task is scheduled according to the current idle state of a computing unit; capturing instruction data output by the host compiler, and executing concurrent instruction decoding according to the corresponding instruction type to obtain a machine code corresponding to the instruction data; obtaining an operand corresponding to the instruction data, and generating a to-be-transmitted instruction based on the machine code corresponding to the instruction data and the operand after secondary verification; according to the calculation complexity, determining an optimal calculation unit, and sending the to-be-launched instruction to the optimal calculation unit; and receiving an execution result of the to-be-transmitted instruction, and returning the execution result to the terminal. The thread execution task is dynamically scheduled according to the idle state of the computing unit, so that computing resources can be fully utilized, and the operation efficiency is improved.

The invention discloses a speech recognition streaming feature extraction and batch reasoning method for a high-concurrency artificial intelligence real-time dialogue system. According to the method, a heterogeneous computing architecture in which a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit) cooperatively work is adopted, operations such as preprocessing, feature extraction and normalization of voice data are executed through the CPU, and batch feature reasoning is executed by utilizing the GPU; the method specifically comprises the following steps: after segmenting and preprocessing voice data at a CPU (Central Processing Unit) end, executing Mel filter bank feature extraction and cepstrum mean variance normalization to obtain a standardized acoustic feature vector; extracting feature vectors from the feature buffer area in batches at the GPU end, filling and aligning the feature vectors, and sending the feature vectors to a neural network model for parallel reasoning calculation; according to the method, streaming processing and batch reasoning in the speech recognition process are realized, and the concurrent processing capability and the real-time performance of the system are effectively improved.

Precise nerve detection during surgery is crucial for safe operational outcomes, which largely depend on the surgeon's skills. Therefore, automatic real-time identification of anatomical structures is of great importance. However, image processing times must be kept as low as possible to be a viable solution for real-time visual identification. By using a general purpose graphics processing unit (GPGPU) implementation for birefringence mapping, the process can achieve rates of 43 frames per second (FPS); a 118×gain from a CPU implementation. Furthermore, by including a deep learning network, the complete framework can automatically detect nerves close to 12 FPS. By leveraging GPU for the processing task, the framework can run on compact devices with NVIDIA modules, such as Jetson Xavier AGX, while still achieving reasonably fast nerve identification and visualization.

The invention discloses a cross-platform GPU support-free real-time digital human rendering system and method, and relates to the technical field of digital human rendering, and the system comprises an offline modeling module which constructs a mouth base space model through principal component analysis based on video data of a target person; the real-time driving module is used for mapping real-time voice features into dynamic coefficients of the mouth base space model; the image synthesis module is used for reconstructing a mouth image according to the dynamic coefficient and fusing the mouth image with a pre-stored reference face image; the cross-platform adaptation layer is configured to realize real-time rendering without GPU dependence through compiling, memory mapping and multi-thread scheduling technologies; and the dynamic resource manager is used for optimizing resource occupation and ensuring stable operation in a multi-platform environment. According to the technical scheme, dependence on high-computing-power hardware and an ideal acoustic environment can be broken through, and a complete theoretical framework and an engineering implementation path are provided for large-scale popularization of a digital human technology in a lightweight terminal.

The embodiment of the invention relates to the technical field of data processing, in particular to a data processing system and method, a product, equipment and a storage medium, and aims to improve the data transmission speed between a central processing unit and a graphics processing unit. The data processing system comprises a central processing unit cluster which comprises a plurality of central processing unit cores and a first cluster sharing cache; the graphics processor cluster comprises a plurality of graphics processor cores and a second cluster shared cache; a shared memory; each central processing unit core in the central processing unit cluster is provided with a secondary private cache; each graphics processor core in the graphics processor cluster is provided with a first-level private cache; and writing corresponding data into the second cluster shared cache by any central processing unit core in the central processing unit cluster under the condition that an address contained in a received data writing request is a graphics processor address.

One embodiment provides for a graphics processing unit (GPU) to accelerate machine learning operations, the GPU comprising an instruction cache to store a first instruction and a second instruction, the first instruction to cause the GPU to perform a floating-point operation, including a multi-dimensional floating-point operation, and the second instruction to cause the GPU to perform an integer operation; and a general-purpose graphics compute unit having a single instruction, multiple thread architecture, the general-purpose graphics compute unit to concurrently execute the first instruction and the second instruction.

The invention relates to the technical field of GPU (Graphics Processing Unit), in particular to a GPU program optimization method based on a CUDA (Compute Unified Device Architecture) parallel environment, which comprises the following steps: acquiring a task request sent by a target GPU program, and carrying out priority queue classification on the task request based on a priority coefficient of the task request so as to divide the task request into different priority queues; the activity coefficient of each priority queue is calculated based on the delays of the different priority queues, resource scheduling is carried out on each priority queue through each calculation node in the calculation cluster based on the activity coefficient of each priority queue, and each calculation node is composed of a CPU and a GPU. According to the method, the task requests are subjected to priority queue classification, so that the tasks with high priorities can be preferentially processed, the response speed and efficiency of the whole system are improved, the tasks with different priorities can be effectively managed through task priority queue classification, resource conflicts are avoided, and the processing capacity of key tasks is improved.

The invention discloses a cloud server physical GPU acceleration capability release method and system, and belongs to the technical field of computer GPU capability release, and the method specifically comprises the steps: collecting GPU operation parameters in real time, carrying out the preprocessing, storing the GPU operation parameters in a shared state database, constructing a lightweight virtualization abstraction layer on a physical GPU, dividing the physical GPU into a plurality of logic units, and storing the logic units in a shared state database; the method comprises the following steps: establishing a physical GPU, constructing an isolation layer, utilizing a three-dimensional resource segmentation strategy, adopting a priority queue, a jump mechanism and a delay tolerance mechanism in a scheduling process, dynamically allocating logic unit resources of the physical GPU, accelerating release of GPU resources, and realizing unification and real-time updating of physical GPU state information among nodes of a cloud server through a distributed state synchronization mechanism; through dynamic task scheduling, a jumping mechanism, a delay tolerance mechanism and distributed state synchronization, efficient GPU resource management is realized, and GPU capability release is accelerated.

Disclosed in the present application are a resource adjustment method and apparatus, an electronic device, a storage medium and a training platform, which are applied to the technical field of artificial intelligence. The method comprises: acquiring a container set of computing nodes executing a task to be trained and a container associated with same; on the basis of resource configuration information of the container set, resource occupation information of the computing nodes and user resource requirements, and in a mode that target graphics processing unit and target central processing unit cores executing said task are preferentially located in the same non-uniform memory access group, selecting target central processing unit cores needing to be bound for the container, and updating configuration information of a process group corresponding to the container, so as to complete binding of the central processing unit cores and the container. The present application can solve the problem that AI training tasks cannot be efficiently completed in the related art, and can maximally ensure the efficient completion of AI training tasks by means of resource adjustment during the execution of the AI training tasks.

A system (100) for optimizing the allocation of computing resources, comprising: a data acquisition unit (102) comprising a retrieval processor (102a) configured to retrieve historical performance data, which is then stored in a data storage module (104); a centralized processing unit (106) comprising a dedicated processor and a graphics processing unit (GPU) for performing a plurality of resource allocation functions, the centralized processing unit further comprising: a prediction module (106a) configured to implement machine learning algorithms to analyze historical performance data and generate workload forecasts; a performance monitoring module (106b) configured to track real-time system metrics, including CPU utilization, memory consumption, and network latency; a resource allocation module (106c) configured to dynamically adjust the computing resources based on the workload forecasts and real-time system metrics; a container orchestration module (106d) configured to manage the deployment and scaling of microservices across distributed containers; and an administrative user interface module (108) comprising a user display (110) and a visualization module (112), wherein the visualization module (112) generates representations of performance metrics and the user display (110) is configured to display system performance metrics and enable configuration of scaling policies.

The invention discloses a high-order precision flood simulation method based on GPU acceleration, and relates to application of a parallel computing technology utilizing a graphic processing unit and a high-order precision numerical discretization method in flood simulation. A WENO reconstruction technology, an implicit time discretization scheme, depth average model innovation, a GPU parallel computing strategy, a terrain adaptability algorithm, a dynamic source item processing mechanism and a multi-source item coupling simulation technology are combined; simulation efficiency can be remarkably improved while high calculation precision is kept, and the method is particularly suitable for flood simulation under complex terrain conditions; through strict model verification and calibration processes and user-friendly software implementation, the accuracy and reliability of the method in practical application are ensured, the limitation of an existing flood simulation technology under large-scale and complex terrain conditions is effectively solved, and the high-order precision flood simulation method based on GPU acceleration is introduced, so that the high-order precision flood simulation method has a good application prospect. The simulation precision and efficiency are remarkably improved, and the dynamic process of the flood event is effectively predicted and analyzed.

The present disclosure relates to the technical field of Graphics Processing Units (GPUs), and in particular to a GPU scheduling method and apparatus, and a storage medium. The method comprises: acquiring a migration command, the migration command being used for migrating a workload of a source Virtual Machine (VM) from a source GPU core to a target GPU core; on the basis of the migration command, establishing an association relationship between the source VM and a corresponding hardware identifier on the target GPU core; and on the basis of the association relationship after the migration, using the target GPU core to process the workload from the source VM. According to embodiments of the present application, while the VM is not shut down, the target GPU core is used to process the workload from the source VM, and thus the workload of the source VM is migrated from a high-load source GPU core to an idle target GPU core, so that the thermal migration between multi-core GPU cores can be achieved, the load between a plurality of GPU cores can be balanced, the pressure of a high-load GPU core can be reduced, the response time can be shortened, and the throughput rate can be increased.

The invention provides an allocation method and system for improving the GPU resource utilization rate, a medium and a program product, and relates to the technical field of data processing. The method comprises the following steps of: constructing a hybrid resource pool which comprises a divided resource pool divided according to set computing power and video memory granularity and a whole spare resource pool retaining a complete GPU resource unit; then, a to-be-processed task is obtained from an interface end, and the computing power scale and continuity demand data of the to-be-processed task are determined through a task analysis model obtained based on multi-factor machine learning training; finally, according to the data, computing power resources are distributed from the hybrid resource pool according to a preset method, so that diversified task requirements are met, efficient and flexible distribution and utilization of GPU resources are achieved, and the overall efficiency is improved.

The invention relates to the technical field of GPUs, in particular to a GPU virtualization video memory management method and device, a storage medium and a program product. The method comprises the steps that a virtual machine transmits a page table root address corresponding to a target application program running in the virtual machine to a host machine; the virtual machine calls the non-memory operation type API in response to the target application program, intercepts calling of the non-memory operation type API and forwards the calling to the host machine; and in the host machine, accessing a video memory area corresponding to a target application program based on the page table root address through the drive of the non-memory operation type API, executing an instruction of the non-memory operation type API, and returning a processing result of the instruction of the non-memory operation type API to the virtual machine. According to the method and the device, the rear-end driver in the host machine can directly access the video memory resources allocated by the virtual machine, so that the resource utilization rate and the task execution efficiency are improved.

Approaches presented herein provide systems and methods for allocating streaming multiprocessors (SMs) to execute one or more tasks. A utilization for a given task may be determined by one or more parameters, such as a task execution time or a period. The utilization may then be used to assign a proportionate number of SMs associated with a processing unit, such as a graphics processing unit (GPU) or other type of processing unit, executing the task. The SMs may then be identified, allocated, and reserved until execution of the task is complete.

The invention relates to an application method of a GPU (Graphics Processing Unit) in design rule inspection in the field of integrated circuits, in particular to a layout design rule inspection method, system and equipment, a medium and a product. The layout design rule checking method comprises the following steps: inputting initial layout data, analyzing a design rule checking task to be executed, and marking the priority and complexity level of the task; the method comprises the following steps: dividing layout data into a plurality of data blocks according to regions on the basis of regional characteristics of the layout data, distributing tasks and corresponding data blocks to a GPU computing core according to task priorities and complexity levels, and distributing new data blocks according to the overall load condition of the GPU computing core after the GPU computing core completes data block processing, and repeating the steps until all tasks are executed, and outputting a layout design rule check result. The system, the computer equipment, the computer readable storage medium and the computer program product have the same beneficial effects as the layout design rule checking method.

The embodiment of the invention discloses a method for performing model reasoning by adopting a GPU (Graphics Processing Unit), electronic equipment, a computer readable storage medium and a computer software product. The method comprises the following steps: pre-loading and preheating a plurality of model instances in the GPU; setting a shared memory shared by at least two threads, and setting a state machine for the shared memory; the state machine can comprise a writable state and a non-writable state; when the state machine is in a writable state, writing a video frame into the shared memory, and setting the state machine to be in a non-writable state; calling the model instance loaded and preheated in the GPU to perform reasoning based on the video frames in the shared memory; and after the reasoning is completed, setting the state machine to be in a writable state. According to the embodiment of the invention, high efficiency and stability of model reasoning are realized, and calculation overhead and system complexity are reduced.

The invention relates to the technical field of GPGPU (General Purpose Graphics Processing Unit), and relates to an SIMT stack and thread scheduling optimization method and device for GPGPU (General Purpose Graphics Processing Unit) branch instructions, a medium and a GPGPU architecture. The method comprises the steps that in a GPGPU, an SIMT stack is optimized, an SM unit is expanded to be a double-instruction assembly line, and a main thread beam scheduler and an auxiliary thread beam scheduler are arranged. And in a non-branch state, half threads of each tube of the scheduler are executed in parallel by the assembly line. When a branch instruction is encountered, instruction addresses, thread masks and branch reunion points of the two branch paths are pressed into the SIMT stack. Judging conflicts according to the thread masks; if conflicts exist, performing serial execution; and if no conflict exists, executing on double pipelines in parallel. And when the branch is completed to a reunion point, the SIMT stack pops up an entry, and the execution is continued. The method has remarkable effects in the aspects of improving the thread resource utilization rate, improving the calculation efficiency, simplifying the branch control complexity, enhancing the GPGPU flexibility and the like.

The invention provides a method for model training, a host and a storage device. The method includes instructing, by a host device, a graphics processing unit (GPU) to load first intermediate data of a current layer of the model from a dynamic random access memory (DRAM) of a storage device into a memory of the GPU, where the current layer is a layer of the model that is performing computations; and based on the space capacity of the DRAM, notifying, by the host device, the storage device to pre-fetch second intermediate data of the layer to be calculated of the model from the NAND of the storage device to the DRAM of the storage device, the first intermediate data for the current layer is used by the GPU in back propagation of the model to perform computations for the current layer.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a GPU (Graphics Processing Unit) and NPU (Network Processing Unit) resource scheduling method for high-performance computing, which comprises the following steps of: waiting for a scheduling optimization module to call acquired resource condition data of GPU and NPU hardware accelerators; the task analysis module analyzes a to-be-executed AI calculation task, obtains task feature data and transmits the task feature data to the scheduling optimization module; the scheduling optimization module calculates an optimal task allocation scheme and transmits the optimal task allocation scheme to the dynamic scheduling module; the dynamic scheduling module monitors task execution conditions and resource changes in real time, dynamically adjusts a task allocation scheme according to feedback information, and feeds back the task allocation scheme to the scheduling optimization module; meanwhile, the dynamic scheduling module feeds back the change information of the resource condition data and the task execution condition to the resource monitoring module and the task analysis module respectively; according to the method, dynamic scheduling is carried out on heterogeneous hardware accelerators such as the GPU and the NPU, respective advantages are fully exerted, and the overall calculation performance is improved.

The invention discloses a GPU (Graphics Processing Unit) hot plug method based on a Kubernetes architecture and a readable storage medium. The method comprises the following steps: a user selects to start a GPU hot plug option when a service is created, NVIDIA GPU resources are changed into self-defined GPU resources adaptive to GPU hot plug, and a condition that the service capacity is reduced to 0 is configured; when the GPU HotPlug Controller detects the newly built Pod using the hot plug, the GPU equipment is dynamically inserted into the Pod; when it is detected that the service Pod has no request and the duration time reaches a GPU release condition configured by a user, executing GPU dynamic release operation; when it is detected that the service Pod has a request, the operation of dynamic allocation of the GPU is executed immediately, and the Knative initiates a GPU hot plug request to the GPU HotPlug Controller; and after the GPU hot plug succeeds, the Knative sends the originally accumulated request to the service Pod, and at the same time, the route is updated: the subsequent flow is directly transmitted to the Pod without being forwarded by the Knative. According to the invention, the problem of resource waste caused by incapability of hot plugging of the GPU in the prior art can be solved.

The invention discloses a key thread bundle scheduling method and device, and relates to the technical field of GPGPU (General Purpose Graphics Processing Unit), a thread bundle which is executed in sequence and a thread bundle which is branched are scheduled at different frequencies, different scheduling schemes are appointed for the two types of thread bundles in the mode, the parallel performance of the sequence thread bundle is ensured not to be influenced to the greatest extent, and the scheduling efficiency is improved. And meanwhile, the operation of the branch thread bundles is accelerated, the problem that the operation time of the thread bundles is prolonged due to excessive thread branches is avoided, and the combining performance of the universal graphics processor is improved to a certain extent.