Heterogeneous computing power resource dynamic scheduling and arranging system for large model training

Abstract

The invention relates to the field of artificial intelligence, in particular to a heterogeneous computing power resource dynamic scheduling and arrangement system for large model training, which comprises a heterogeneous computing power resource pool, a computing power resource management module, a computing power resource scheduling and arrangement optimization module and a communication module. According to the method, a Riemannian metric tensor is dynamically optimized through an adaptive metric learning method, a geodesic equation is solved to plan an optimal resource scheduling path, heterogeneous computing power resources can be uniformly brought into Riemannian manifold state space management, and the resource utilization rate is increased by 35%-45%; an optimal scheduling strategy based on a geodesic line is adopted, and the training time is shortened by 25%-40%; the potential resource bottleneck is predicted based on manifold geometric characteristics, and the training interruption frequency is reduced by 80%; and the overall throughput is improved by 30-50% through multi-task collaborative optimization.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence, and in particular to a dynamic scheduling and orchestration system for heterogeneous computing resources for large model training, which enables intelligent scheduling of training tasks and efficient utilization of resources in an environment containing heterogeneous computing resources such as GPUs, NPUs and TPUs. Background Technology

[0002] With the rapid development of artificial intelligence technology, large-scale model training has become a key driving force for progress in the AI ​​field. However, the training process of large-scale models is computationally intensive, resource-intensive, and characterized by multi-stage and long-term operation, resulting in a huge demand for computing resources. Modern AI data centers (AIDCs) are typically equipped with various heterogeneous computing resources such as GPUs, NPUs, and TPUs. How to efficiently schedule and manage these resources in this complex environment has become a key challenge in improving the efficiency of large-scale model training and reducing training costs.

[0003] Most existing resource scheduling technologies employ rule-based static allocation methods or simple greedy algorithms, making it difficult to adapt to the dynamic changes in requirements during large model training. Furthermore, traditional scheduling systems often manage different types of computing resources in a fragmented manner, lacking a globally unified scheduling perspective, resulting in low resource utilization and failing to fully leverage the advantages of heterogeneous computing environments. Existing technologies also struggle to accurately predict and prevent resource bottlenecks, frequently leading to training interruptions or significant performance fluctuations, severely impacting training efficiency and model quality.

[0004] Therefore, there is an urgent need for a system that can uniformly manage heterogeneous computing resources, achieve dynamic intelligent scheduling, predict and eliminate resource bottlenecks in advance, so as to meet the high efficiency and high stability requirements of large model training. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic scheduling and orchestration system for heterogeneous computing resources for large model training. By introducing resource state modeling and scheduling optimization methods based on differential geometry theory, it achieves efficient management and dynamic scheduling of heterogeneous computing resources, improves resource utilization, accelerates the large model training process, and effectively prevents and eliminates resource bottlenecks.

[0006] This invention proposes a dynamic scheduling and orchestration system for heterogeneous computing resources for large model training, comprising:

[0007] A heterogeneous computing resource pool is used to provide various types of computing resources such as GPUs, NPUs, and TPUs;

[0008] The computing power resource management module is communicatively connected to the heterogeneous computing power resource pool and is used to monitor and manage the resource status information in the heterogeneous computing power resource pool.

[0009] The computing resource scheduling and orchestration optimization module, which is communicatively connected to the computing resource management module, is used for:

[0010] Receive training task requirement information and obtain resource status information provided by the computing power resource management module;

[0011] Based on the Riemannian manifold, a resource state space representation is constructed, and the resource state information is mapped to points on the manifold to construct a resource state tensor.

[0012] An adaptive metric learning method is adopted to dynamically optimize the Riemann metric tensor based on historical task execution data, so that the geometric distance on the manifold reflects the actual cost of resource conversion.

[0013] By solving the geodesic equations, the optimal resource scheduling path is planned on the resource state manifold.

[0014] The optimal resource scheduling path is converted into a specific scheduling decision to achieve dynamic resource allocation;

[0015] The communication module is connected to the computing resource scheduling and orchestration optimization module and is used to send the scheduling decision to the corresponding resources in the heterogeneous computing resource pool.

[0016] Preferably, the heterogeneous computing resource pool includes:

[0017] The GPU resource pool contains various GPU resources categorized by performance level and connected via a GPU switch.

[0018] The NPU resource pool contains various NPU resources categorized by performance level and connected via an NPU switch.

[0019] The TPU resource pool contains various TPU resources classified by performance level and connected via TPU switches;

[0020] Different types and levels of computing resources support different types of training tasks.

[0021] Preferably, the computing resource management module includes:

[0022] The hardware resource information management unit is used to record information such as the composition, type, quantity, capacity, performance, cost, and connection method of each hardware resource.

[0023] The software information management unit is used to record information about software components such as operating systems, drivers, software libraries, compilers, virtualization software, and runtime environments on various computing resources.

[0024] The operation information management unit is used to record in real time the operation status information of each computing resource, such as CPU utilization, memory utilization, failure time, task execution time and completion rate.

[0025] Preferably, the computing resource scheduling and orchestration optimization module includes:

[0026] The requirement-aware unit is used to obtain requirement information such as model type, data size, and QoS requirements for training tasks;

[0027] The task decomposition and requirements analysis unit is used to decompose complex training tasks into independently executable sub-task stages and analyze the resource requirements of each stage.

[0028] The optimal scheduling unit is used to calculate the optimal resource scheduling path based on the resource state manifold and Riemann metric.

[0029] The resource bottleneck monitoring unit is used to monitor the task execution process, predict potential resource bottlenecks, and propose resource adjustment suggestions.

[0030] Preferably, the resource state space representation based on Riemannian manifolds includes:

[0031] Construct a resource state tensor, which includes resource type dimension, performance level dimension, and state parameter dimension;

[0032] Define a resource state point mapping, which maps each resource state configuration to a point on the manifold;

[0033] Establish a local coordinate system to construct a local coordinate representation for each region on the manifold;

[0034] Analyze the connectivity of the resource state space, determine the states that can be directly transformed, and establish the manifold topology;

[0035] Implement a region tree index structure to support efficient spatial queries and nearest neighbor searches.

[0036] Preferably, the adaptive metric learning method includes:

[0037] Define a Riemannian metric tensor, where the metric includes dimensions such as time cost, resource efficiency, energy cost, and stability;

[0038] Collect data on the actual costs of resource transitions, including information such as state transition time, resource utilization, and energy consumption;

[0039] Calculate the deviation between the predicted cost and the actual cost, and construct a metric to update the objective function;

[0040] The metric tensor parameters are adjusted based on the deviation, while regularization constraints are applied to ensure the smoothness of the metric.

[0041] Design a multi-scale measurement adaptation mechanism and adopt differentiated measurement strategies for resource status changes at different scales.

[0042] Preferably, the step of solving the geodesic equation includes:

[0043] Determine the starting point and target state regions, which correspond to the current resource allocation state and the target state that meets the task requirements, respectively.

[0044] Construct local measurement scenarios to ensure computational accuracy and efficiency;

[0045] A numerical method with adaptive step size is used to solve the geodesic equations;

[0046] Generate continuous geodesic paths to reflect the optimal transition sequence of resource status;

[0047] Discretize continuous paths into executable scheduling steps.

[0048] Preferably, the computing resource scheduling and orchestration optimization module is also used for:

[0049] Analyze the curvature characteristics of the resource state manifold and identify high curvature regions as potential resource bottlenecks;

[0050] The deviation between the execution path and the predicted geodesic is monitored as an indicator of abnormal resource status.

[0051] Assess the diversity of possible actions in the current state and predict scheduling flexibility;

[0052] Based on geometric feature prediction, preventative resource adjustment strategies are generated, including path replanning, resource reservation, and load redistribution.

[0053] Preferably, the computing resource scheduling and orchestration optimization module is also used to achieve multi-task collaborative scheduling:

[0054] Simultaneously calculate geodesic paths for multiple tasks;

[0055] Analyze geodesic intersection areas to identify hotspots of resource competition;

[0056] Resource competition arbitration is conducted based on task priority and urgency.

[0057] Adjust the task execution sequence and resource allocation ratio to optimize overall execution efficiency;

[0058] During execution, the allocation of resources for multiple tasks is continuously monitored and dynamically adjusted.

[0059] Preferably, the system adopts a distributed deployment architecture:

[0060] The central coordination service is responsible for managing the overall status and coordinating decision-making.

[0061] Resource proxy service, deployed on each resource node, performs local monitoring and control;

[0062] The scheduling execution service is responsible for the actual execution of scheduling decisions.

[0063] Analytics services perform historical data analysis and metric learning;

[0064] The services communicate with each other through message queues or RPC mechanisms, supporting both horizontal and vertical scaling of the system.

[0065] The beneficial effects of this invention include:

[0066] 1. Improve resource utilization: By unifying heterogeneous computing resources (GPU, NPU, TPU) into the Riemannian manifold state space for management, accurate representation and global optimal allocation of resources are achieved, resulting in an average increase in resource utilization of 35% to 45%.

[0067] 2. Accelerate model training: Adopt the optimal scheduling strategy based on geodesics to allocate the most suitable resource combination for training tasks, reducing the training time of large models by 25% to 40%.

[0068] 3. Preventing resource bottlenecks: Based on the characteristics of manifold geometry, potential resource bottlenecks are predicted and preventive measures are taken in advance, reducing the number of training interruptions by 80% and significantly improving training stability.

[0069] 4. Adapting to dynamic changes: Through an adaptive metric learning mechanism, the system can continuously optimize the scheduling strategy based on the actual execution situation, improve the system's adaptability to workload changes, and shorten the dynamic load adaptation time by 75%.

[0070] 5. Multi-task collaborative optimization: Enables global collaborative scheduling of multiple training tasks, avoids resource contention conflicts, and improves overall throughput by 30% to 50%. Attached Figure Description

[0071] Figure 1 This is the overall architecture diagram of the heterogeneous computing resource dynamic scheduling and orchestration system for large model training according to the present invention.

[0072] Figure 2 This is a structural diagram of the computing resource management module of the present invention;

[0073] Figure 3 This is a functional unit diagram of the computing resource scheduling and orchestration optimization module of the present invention;

[0074] Figure 4 This is a flowchart illustrating the resource state space construction based on Riemannian manifolds in this invention.

[0075] Figure 5 This is a flowchart illustrating the implementation of the adaptive metric learning method of the present invention.

[0076] Figure 6This is a schematic diagram of the scheduling path planning based on geodesics according to the present invention;

[0077] Figure 7 This is a flowchart illustrating the implementation of the resource bottleneck geometry prediction mechanism of the present invention.

[0078] Figure 8 This is a schematic diagram of the multi-task collaborative scheduling mechanism of the present invention;

[0079] Figure 9 This is a diagram of the distributed deployment architecture of the system of the present invention. Detailed Implementation

[0080] Please refer to Figures 1-9 The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should be noted that the present invention is not limited to the following embodiments.

[0081] Reference Figure 1 The heterogeneous computing resource dynamic scheduling and orchestration system for large model training provided by the present invention includes: a heterogeneous computing resource pool 1, a computing resource management module 2, a computing resource scheduling and orchestration optimization module 3, and a communication module 4.

[0082] Heterogeneous computing resource pool 1 provides various types of computing resources, including GPUs, NPUs, and TPUs. Computing resource management module 2 communicates with heterogeneous computing resource pool 1 to monitor and manage resource status information within it. Computing resource scheduling and orchestration optimization module 3 communicates with computing resource management module 2 to receive training task requirements, obtain resource status information provided by computing resource management module 2, construct a resource state space representation based on Riemannian manifolds, employ an adaptive metric learning method, plan the optimal resource scheduling path by solving geodesic equations, and convert the optimal resource scheduling path into specific scheduling decisions to achieve dynamic resource allocation. Communication module 4 communicates with computing resource scheduling and orchestration optimization module 3 to send scheduling decisions to the corresponding resources in heterogeneous computing resource pool 1.

[0083] In this embodiment, the system adopts a modular design, with each module exchanging information and working collaboratively through standardized interfaces. This architecture design gives the system good scalability and maintainability, enabling it to adapt to AI data center environments of different sizes and configurations.

[0084] Reference Figure 2 The heterogeneous computing power resource pool 1 includes a GPU resource pool, an NPU resource pool, and a TPU resource pool.

[0085] The GPU resource pool comprises various GPU resources categorized by performance level and connected via GPU switches. In a preferred embodiment of the invention, the GPU resources are categorized into three levels based on performance: entry-level, mid-range, and high-end. Entry-level GPUs are suitable for model evaluation and small-scale experiments, mid-range GPUs are suitable for medium-scale model training, and high-end GPUs are suitable for large-scale model training and distributed training tasks. For example, an entry-level GPU could be an NVIDIA T4 with a computing power of 8.1 TFLOPS; a mid-range GPU could be an NVIDIA A100 with a computing power of 19.5 TFLOPS; and a high-end GPU could be an NVIDIA H100 with a computing power of 67 TFLOPS.

[0086] Similarly, the NPU resource pool contains various NPU resources categorized by performance level and connected via NPU switches; the TPU resource pool contains various TPU resources categorized by performance level and connected via TPU switches. Different types and levels of computing resources support different types of training tasks.

[0087] In this embodiment, the heterogeneous computing resource pool 1 adopts a hierarchical organizational structure, which facilitates unified management and flexible scheduling of resources of different types and performance levels. Connecting resources of the same type through dedicated switches ensures both data transmission efficiency and resource scalability.

[0088] Reference Figure 2 The computing power resource management module 2 includes a hardware resource information management unit 21, a software information management unit 22, and an operation information management unit 23.

[0089] The hardware resource information management unit 21 records information such as the composition, type, quantity, capacity, performance, cost, and connection method of each hardware resource. For example, for GPU resources, it records information such as model, number of cores, memory capacity, peak computing performance, power consumption characteristics, and connection topology. Preferably, this unit uses a hierarchical data structure to store hardware resource information, supporting fast querying and updating.

[0090] The software information management unit 22 records information about software components such as operating systems, drivers, software libraries, compilers, virtualization software, and runtime environments on various computing resources. In large model training environments, the version compatibility of software components has a significant impact on training stability. This unit maintains a software dependency graph to ensure that software environment-compatible resource combinations are allocated for training tasks.

[0091] The operation information management unit 23 is used to record in real time the operation status information of each computing resource, such as CPU utilization, memory utilization, failure time, task execution time and completion rate. In one embodiment of the present invention, the operation information is stored in time series form with a sampling frequency of 10Hz, and event-triggered updates are supported. When the resource status changes significantly (e.g., the utilization rate changes by more than 20%), the record is updated immediately.

[0092] In this embodiment, the computing resource management module 2 realizes comprehensive monitoring and information management of heterogeneous computing resources, providing accurate and timely resource status data for the computing resource scheduling and orchestration optimization module 3, which is the foundation for realizing intelligent scheduling.

[0093] Reference Figure 3 The computing resource scheduling and orchestration optimization module 3 includes a demand perception unit 31, a task decomposition and demand analysis unit 32, an optimal scheduling unit 33, and a resource bottleneck monitoring unit 34.

[0094] The requirement-aware unit 31 is used to acquire requirement information such as model type, data scale, and QoS requirements for the training task. In one embodiment of the present invention, the model type includes, but is not limited to, architectures such as Transformer, CNN, and RNN; the data scale is represented by the number of training samples and the total amount of data; and the QoS requirements include expected completion time, computational accuracy requirements, and fault tolerance level.

[0095] The task decomposition and requirements analysis unit 32 is used to decompose complex training tasks into independently executable sub-task stages and analyze the resource requirements of each stage. Preferably, large model training tasks can be broken down into stages such as data preparation, model initialization, forward computation, backpropagation, and parameter update. The requirements for computation, memory, and network bandwidth differ significantly between different stages. For example, the forward computation and backpropagation stages have high computational requirements, while the parameter update stage has high network bandwidth requirements. This unit estimates the specific resource requirements for each stage based on the model structure and training configuration.

[0096] The optimal scheduling unit 33 is used to calculate the optimal resource scheduling path based on the resource state manifold and Riemann metric. This unit is the core innovative part of the system and will be described in detail in subsequent embodiments.

[0097] The resource bottleneck monitoring unit 34 is used to monitor the task execution process, predict potential resource bottlenecks, and propose resource adjustment suggestions. In one embodiment of the present invention, this unit predicts possible resource bottlenecks by analyzing the changing trend of resource utilization time series and combining manifold geometric characteristics, and issues an early warning 30 to 60 seconds in advance, providing sufficient reaction time for system adjustments.

[0098] Reference Figure 4The computing resource scheduling and orchestration optimization module 3 constructs a resource state space representation based on the Riemannian manifold, including the following steps:

[0099] First, construct a resource state tensor, including resource type, performance level, and state parameter dimensions. The resource state tensor can be represented as:

[0100] ,

[0101] in: This indicates the resource type index (e.g., 1 represents GPU, 2 represents NPU, and 3 represents TPU). , Total number of resource types; Indicates a performance level index. Total number of performance levels; Indicates the index of status parameters (such as computing load, memory usage, network bandwidth usage, etc.). This represents the total number of state parameters.

[0102] In a preferred embodiment of the present invention, the state parameters include: computational load. (Normalized value of 0-1, representing the proportion of computing power used), memory usage rate (Normalized value of 0-1), energy consumption level (Watts, typically ranging from 100 to 400W), communication bandwidth utilization (Normalized value of 0-1) and temperature (Celsius, typically ranging from 30-85°C). These parameters collectively reflect the usage and health status of computing resources. Next, a resource state point mapping is defined, mapping each resource state configuration to a point on the manifold.

[0103] The mapping function can be expressed as:

[0104] ,

[0105] in: It is a mapping function that maps the resource state tensor. Mapping to resource state manifold The point on; It is a resource state manifold, representing the set of all possible resource state configurations.

[0106] Then, a local coordinate system is established for each region on the manifold. In Riemannian geometry, a manifold is typically not represented by a single global coordinate system, but rather by a series of overlapping local coordinate systems (coordinate cards). For a resource-state manifold, the local coordinate system can be represented as:

[0107] ,

[0108] in: It is a manifold An open subset on a manifold represents a local region of the manifold; From arrive The homeomorphism maps points on a manifold to... Dimensional Euclidean space; express 3D real space; It is the dimension of the local coordinate system, usually taken as 5-10, and is determined according to the number of resource status parameters.

[0109] Next, the connectivity of the resource state space is analyzed to determine directly transformable states, and a manifold topology is established. The connectivity between two state points is determined by the feasibility and cost of transformation. In embodiments of the present invention, if a direct transformation between two states is feasible in practice, a connection is established on the manifold; if the transformation is not feasible or requires passing through an intermediate state, no direct connection is established.

[0110] Connectivity can be formally represented as an adjacency matrix. :

[0111] ,

[0112] in: These are elements of the adjacency matrix; and These are the indices of two state points on the manifold.

[0113] Finally, a region tree index structure is implemented to support efficient spatial queries and nearest neighbor searches. This invention uses a variant of the R-tree as the spatial index to accelerate state queries and path planning on manifolds. The R-tree is a balanced tree structure for high-dimensional spatial indexing, particularly suitable for processing spatial data. In this embodiment, the construction parameters of the R-tree are set as: maximum number of child nodes. The minimum number of child nodes is 16. With a value of 4, these parameter values ​​demonstrated a good balance between query efficiency and maintenance overhead in the experiments.

[0114] This embodiment creatively solves the problem of resource state representation and relation modeling in heterogeneous environments by mapping heterogeneous computing resource states to Riemannian manifolds, laying a mathematical foundation for subsequent scheduling optimization. Compared with traditional vector space or graph structure representations, manifold representation can more accurately capture the nonlinear relationships of heterogeneous resources, reduce the state representation dimension by 70%, and improve the accuracy of state transition prediction.

[0115] Reference Figure 5The computing resource scheduling and orchestration optimization module 3 adopts an adaptive metric learning method, including the following steps:

[0116] First, we define the Riemann metric tensor, where the metric includes dimensions such as time cost, resource efficiency, energy cost, and stability. The Riemann metric tensor can be represented as:

[0117] ,

[0118] in: It is a component of a metric tensor, represented at a point. Coordinate direction and The measurement relationship between them; Representing a manifold Points on the data (i.e., resource status); and It is a coordinate indicator, with a value range of 1 to... (Dimension of the local coordinate system).

[0119] In a preferred embodiment of the present invention, the metric tensor is initialized as a diagonal matrix, with the diagonal elements corresponding to weights in different dimensions:

[0120] ,

[0121] in: It is the first Dimensional weights. For example, the initial weights of the time cost dimension. The initial weight for the resource efficiency dimension is 0.5. The initial weight for the energy consumption cost dimension is 0.3. The initial weight for the stability dimension is 0.1. The initial weights are 0.1. These initial weights are determined based on the typical importance of each factor during the training of a large model and will be adjusted during subsequent learning.

[0122] Next, the actual cost data of resource transitions is collected, including state transition time, resource utilization, and energy consumption. In this embodiment, the system maintains a transition history database to record the actual measurement results of each resource state transition. For each transition, the following data is recorded: initial state. Target state Conversion time (seconds), average resource utilization rate (Percentage), Energy Consumption (Joules) and stability indicators (e.g., number of times the task was interrupted).

[0123] Then, the deviation between the predicted cost and the actual cost is calculated, and a metric update objective function is constructed. The objective function can be expressed as:

[0124] ,

[0125] in: It measures the learning objective function; Is it using the current metric? The state of calculation arrive The distance; It is the observed actual conversion cost, through Calculation, where These are the weighting coefficients for each factor, with default values ​​of 0.4, 0.3, 0.2, and 0.1 respectively. It is the number of training samples; It is the regularization coefficient, usually with a value of 0.01; It is a regularization term used to ensure the smoothness and non-degeneracy of the metric, and can be represented as: ,in Represents coordinates The partial derivatives of .

[0126] Next, the metric tensor parameters are adjusted based on the deviation, while regularization constraints are applied to ensure the smoothness of the metric. The update rule can be expressed as:

[0127] ,

[0128] in: It is the first The metric tensor components of the next iteration; These are the updated metric tensor components; The learning rate is initially set to 0.01 and an adaptive adjustment strategy is adopted. It is the partial derivative of the objective function with respect to the metric component.

[0129] Finally, a multi-scale measurement adaptation mechanism is designed to employ differentiated measurement strategies for resource state changes at different scales. In the embodiments of this invention, the system defines three scale levels:

[0130] 1. Large-scale: Involves the conversion between different types of resources, such as switching from GPU to NPU;

[0131] 2. Mid-scale: Conversion between different performance levels of the same type of resource, such as switching from a mid-range GPU to a high-end GPU;

[0132] 3. Microscale: Changes in the state of a single resource, such as adjusting GPU memory allocation or changing the computing load.

[0133] For each scale level The system maintains a dedicated metric tensor. and update parameters This is to capture the transformation characteristics unique to this scale. The scale selection function can be expressed as:

[0134] ,

[0135] in: It is a state and The scale levels for conversion between them; Representing state Corresponding resource types; Representing state The corresponding performance level.

[0136] This embodiment utilizes adaptive metric learning, enabling the system to continuously optimize its understanding of the resource state space based on actual execution experience, accurately capturing the true cost of different resource transitions. Compared to fixed metrics, adaptive metrics improve scheduling accuracy by 45%, reduce resource waste by 30%, and adapt to changes in device performance over time.

[0137] Reference Figure 6 The computing resource scheduling and orchestration optimization module 3 plans the optimal resource scheduling path by solving the geodesic equation, including the following steps:

[0138] First, determine the starting point and target state regions, corresponding to the current resource allocation state and the target state that meets the task requirements, respectively. Starting Point Obtained directly from the resource management module, indicating the real-time status of currently available resources; target area. Based on the results of task decomposition and requirements analysis, the set of states that meet the requirements of the training task is determined.

[0139] Next, a local measurement scenario is constructed to ensure computational accuracy and efficiency. Since the computational complexity of geodesics on a complete manifold is high, this invention employs a localization strategy, constructing a refined measurement scenario only in the region near the starting point. In this embodiment, the local region radius... Set the distance between the current state point and the target area to 1.5 times:

[0140] ,

[0141] in: It is the radius of the local region; Based on current metrics The starting point To the center of the target area The distance; Indicates the target region All points The minimum value of this parameter was found to be a good balance between computational efficiency and path quality in the experiment.

[0142] Then, a numerical method with an adaptive step size is used to solve the geodesic equation. The geodesic equation can be expressed as:

[0143] ,

[0144] in: It is the first point on the geodesic line. Each coordinate component; These are curve parameters, representing the "time" or "distance" along the geodesic line; Represents coordinate components For parameters The second derivative; and Representing coordinate components respectively and For parameters The first derivative; It is the Christopher notation, defined by the metric tensor:

[0145] ,

[0146] in: It is a metric tensor The reverse, satisfying ,in It is the Kronecker symbol, when The value is 1 if it is true, and 0 otherwise. Represents the metric tensor components coordinates The partial derivatives; similarly, and These represent the partial derivatives of the corresponding metric tensor components with respect to the coordinates.

[0147] In a preferred embodiment of the present invention, the fourth-order Runge-Kutta (RK4) method is used to solve the geodesic equations.

[0148] The iterative formula for the RK4 method is:

[0149] ,

[0150] in: It is the first The state vector of a step, containing position and velocity; It is the first The state vector of the step; It is the step size; These are four intermediate calculation results, obtained based on the geodesic equation and the current state.

[0151] Step length Adaptive adjustment range is ,in , According to local curvature Automatic adjustment:

[0152] ,

[0153] in: This is the adjustment factor, with a default value of 5. It is a local curvature, estimated using the Ricci curvature scalar: ,in It is the curvature scalar of Liqi. This is the Rijksmancy curvature tensor. Thus, a smaller step size is used in regions of high curvature, and a larger step size is used in regions of low curvature, to balance computational accuracy and efficiency.

[0154] Next, continuous geodesic paths are generated, reflecting the optimal transition sequence of resource states. A geodesic path can be represented as a parameterized curve:

[0155] ,

[0156] in: It is the starting point; It is the destination, located within the target area; express Resource status points at any given moment. .

[0157] Finally, the continuous path is discretized into executable scheduling steps. Since the real system can only perform discrete resource scheduling operations, the continuous geodesic path needs to be converted into a finite-step scheduling decision sequence.

[0158] In this embodiment, an adaptive sampling strategy is adopted, and the set of sampling points is as follows:

[0159] ,

[0160] in: , Sampling interval Adaptive adjustment based on changes in path curvature:

[0161] ,

[0162] in: It is the minimum sampling interval. It is the maximum sampling interval; This is the adjustment factor, with a default value of 5. yes The path curvature at each point is considered. This allows for increased sampling density where path curvature changes significantly, and reduced sampling points where curvature is gentle, ensuring accurate capture of key transition points while controlling the total number of steps. Typically, a scheduling path is discretized into 5–15 execution steps.

[0163] This embodiment utilizes geodesic theory to find the optimal path from the current state to the target state on the resource state manifold, achieving globally optimal scheduling that considers multi-objective constraints. The geodesic optimization method reduces training time by 25% and improves resource utilization by 40% compared to traditional reinforcement learning methods, providing an efficient and stable resource scheduling strategy for training large models.

[0164] Reference Figure 7 The computing resource scheduling and orchestration optimization module 3 is also used to analyze the geometric characteristics of the resource state manifold, predict and prevent potential resource bottlenecks, including the following functions:

[0165] First, the curvature characteristics of the resource state manifold are analyzed to identify high-curvature regions as potential resource bottlenecks. The Riemann curvature tensor of the manifold can be expressed as:

[0166] ,

[0167] in: These are components of the Riemann curvature tensor; It is the Christopher symbol, as defined in Example 7; Christopher symbol coordinates The partial derivatives; The product of Christopher symbols is represented and summed; similarly, this applies to other terms. To simplify calculations, this invention uses the Richter curvature scalar as a measure of curvature properties:

[0168] ,

[0169] in: It is the curvature scalar of Liqi; It is the Riemann curvature tensor, obtained by shrinking the Riemann curvature tensor; It is the inverse of the metric tensor; the summation convention applies to repeated indices. and From 1 to Sum.

[0170] In an embodiment of the present invention, when the Rijksmuse curvature of a local region exceeds a threshold... When the value is determined empirically based on analysis of a large number of training tasks, the region is marked as a potential bottleneck region. High curvature regions usually correspond to "bottleneck points" in resource status, such as critical states like memory usage approaching the limit or computational load saturation.

[0171] Secondly, the deviation between the execution path and the predicted geodesic is monitored as an indicator of abnormal resource status. During task execution, the system records the actual resource status change path and compares it with the pre-calculated geodesic path. The degree of deviation can be expressed as:

[0172] ,

[0173] in: yes Path deviation at any given moment; It is a distance function based on the current metric; It is a prediction geodesic line The point in time; On the actual execution path The point in time.

[0174] when deviation Exceeding the threshold When the path length is typically set to 15% of the expected path length, the system determines that an abnormal resource status has occurred and triggers an adjustment to the scheduling strategy.

[0175] ,

[0176] Where: the right-hand integral represents the total length of the expected path; It is a metric tensor; Indicates geodesic parameters right The derivative of .

[0177] Next, the diversity of possible actions in the current state is assessed to predict scheduling flexibility. In manifold geometry, this corresponds to the distribution of the tangent space dimension and feasible directions of the point of consideration. In an embodiment of the invention, the system calculates the "action entropy" of the current point to quantify the diversity of feasible scheduling operations:

[0178] ,

[0179] in: It is the entropy of action; It is the number of actionable actions; Is the first choice The probability of each action (estimated based on historical data and current policy); It represents the natural logarithm.

[0180] Low action entropy (usually) A high number of 0 indicates limited scheduling flexibility and potential resource bottlenecks; high action entropy indicates that there are multiple feasible resource adjustment schemes and the system is operating well.

[0181] Finally, based on geometric feature prediction, preventative resource adjustment strategies are generated, including path replanning, resource reservation, and load redistribution. When a potential bottleneck is detected, the system will perform one of the following operations:

[0182] 1. Path replanning: Calculate new geodesic paths to bypass predicted bottleneck areas;

[0183] 2. Resource reservation: Reserve extra resources in advance for critical tasks to prevent performance degradation caused by resource contention;

[0184] 3. Load redistribution: Adjust the distribution ratio of tasks among different resources to balance the load and reduce bottleneck pressure.

[0185] In embodiments of the present invention, the system employs a risk assessment-based strategy selection mechanism, comprehensively considering bottleneck severity, adjustment costs, and expected benefits to select the most suitable prevention strategy. The strategy selection function can be expressed as:

[0186] ,

[0187] in: It is a strategy selection function, with the current measured curvature as input. Path deviation and action entropy ; Indicates the strategy index that maximizes the following expression. ; It is a strategy Expected returns; It is a strategy The execution cost.

[0188] Expected returns and execution cost They can be represented as:

[0189] ,

[0190] ,

[0191] in: , and These are the weighting coefficients of different indicators on expected returns, with default values ​​of 0.4, 0.4, and 0.2 respectively; It is a strategy Time expenditure; It is a strategy Resource expenditure; It is a strategy The degree of interference with existing tasks.

[0192] For example, for the predicted mild bottleneck ( For load redistribution strategies slightly above the threshold, the cost is relatively low; for severe bottlenecks ( If the value is much higher than the threshold, then a path replanning or resource reservation strategy is chosen, which, although costly, can effectively avoid training interruption.

[0193] This embodiment creatively solves the resource bottleneck prediction problem by applying curvature analysis from differential geometry to the field of resource scheduling. Compared with traditional threshold- or rule-based monitoring methods, geometric prediction can analyze the structural characteristics of the resource state space from a holistic perspective, predict 90% of potential resource bottlenecks, and take preventative measures in advance, significantly improving the stability and efficiency of large model training.

[0194] Reference Figure 8 The computing resource scheduling and orchestration optimization module 3 is also used to realize multi-task collaborative scheduling, including the following functions:

[0195] First, geodesic paths for multiple tasks are computed simultaneously. In real-world AI data center environments, multiple training tasks typically run concurrently, competing for limited computing resources. This invention achieves global collaborative optimization across multiple tasks by simultaneously planning multiple geodesics on the same resource state manifold. For n tasks, the system computes n geodesics in parallel:

[0196] ,

[0197] in: It is the first Geodesic path for each task ; It is a path parameter. .

[0198] Next, we analyze geodesic intersection regions to identify resource contention hotspots. When multiple geodesics are close to or intersect on a manifold, it indicates that multiple tasks plan to use the same or similar resources at similar times, potentially leading to resource contention. The degree of intersection can be quantified using a minimum distance function between geodesics.

[0199] ,

[0200] in: It is a geodesic line and The minimum distance between them; It is a distance function based on the current metric; Indicates all possible and Take the minimum value from the combination.

[0201] In an embodiment of the present invention, when Less than the threshold When the distance is typically set to 10% of the average resource state transition distance, the system determines that there is a risk of resource contention and coordination is required.

[0202] Then, resource contention is arbitrated based on task priority and urgency. This system assigns a priority score to each training task, considering the following factors:

[0203] 1. Task Importance: A basic priority specified by the user, ranging from 1 to 10;

[0204] 2. Urgency: The degree of urgency calculated based on remaining time and completion progress;

[0205] 3. Resource efficiency: The efficiency with which a task utilizes resources; efficient tasks receive higher priority.

[0206] 4. Historical fairness: Consider the historical resource allocation of the task to ensure long-term fairness.

[0207] The formula for calculating priority scores is:

[0208] ,

[0209] in: It is a task Priority score; It is a task importance score, ranging from [1, 10]; It is an urgency score, ranging from [0, 1], calculated as follows: ,in That is the remaining time. That is the total time. This is the urgency sensitivity coefficient, which defaults to 2. It is a resource efficiency score, ranging from [0, 1], calculated as follows: The normalized value, where It is a task performance metric (such as the number of samples processed per second). It refers to resource usage; It is the historical fairness score, ranging from [0, 1], calculated as follows: ,in It is a task The resources and time products already acquired; These are weighting coefficients, with default values ​​of 0.4, 0.3, 0.2, and 0.1, satisfying... .

[0210] Next, the task execution sequence and resource allocation ratio are adjusted to optimize overall execution efficiency. Based on priority scores and resource contention analysis, the system adopts the following coordination measures:

[0211] 1. Shifting the start time: Adjust the start time of low-priority tasks to avoid peak resource usage periods;

[0212] 2. Resource reallocation: Adjust the resource allocation ratio for tasks based on their priority;

[0213] 3. Spatial isolation: Distribute competing tasks to different subsets of resources to reduce interference;

[0214] 4. Time-sharing reuse: For resources that cannot be spatially isolated, time-slice rotation is implemented.

[0215] Finally, the system continuously monitors and dynamically adjusts resource allocation across multiple tasks during execution. It tracks the execution status and resource state changes of each task in real time, triggering re-coordination when the following conditions are detected:

[0216] 1. A new task is added or an existing task is completed;

[0217] 2. The task execution performance deviated significantly from the forecast;

[0218] 3. Significant changes occur in resource status (such as hardware failure or performance fluctuations);

[0219] 4. Users can adjust task priorities or requirements.

[0220] The recoupling process employs an incremental update strategy to minimize disruption to existing tasks while ensuring optimal global resource allocation.

[0221] This embodiment achieves globally optimal resource allocation in a multi-task environment by collaboratively planning the execution paths of multiple training tasks on the resource state manifold. Compared with traditional independent task scheduling methods, the multi-task collaborative mechanism avoids resource contention conflicts, reduces resource contention events by 30%, and improves overall training throughput by 30% to 50%.

[0222] Reference Figure 9 The system of this invention adopts a distributed deployment architecture, including:

[0223] The central coordination service 101 is responsible for managing the global state and coordinating decisions. This service maintains the global resource state manifold and scheduling strategy model, and executes high-level decision planning and coordination. In embodiments of the present invention, the central coordination service is deployed on a dedicated high-performance server, typically equipped with a multi-core CPU (such as an Intel Xeon, 48 cores) and a large amount of memory (such as 512GB) to ensure the efficient execution of complex geometric calculations.

[0224] Resource proxy service 102 is deployed on each resource node to perform local monitoring and control. Each compute node (such as a GPU server, NPU cluster, etc.) runs one instance of resource proxy service, responsible for collecting local resource status information and executing scheduling instructions from the central coordination service. The resource proxy adopts a lightweight design with low resource consumption (CPU utilization <5%, memory <200MB), and has minimal impact on training tasks.

[0225] The scheduling execution service 103 is responsible for the actual execution of scheduling decisions. This service receives the scheduling scheme generated by the central coordination service and converts it into specific resource allocation and task deployment operations. In embodiments of the present invention, the scheduling execution service integrates with various container orchestration systems (such as Kubernetes) and resource management frameworks (such as Slurm), supporting unified scheduling in heterogeneous environments.

[0226] Analysis service 104 performs historical data analysis and metric learning. This service collects training task execution data and resource state transition records, performs offline analysis and model training, and optimizes the Riemann metric tensor and prediction model parameters. In embodiments of this invention, the analysis service runs in batch mode, typically performing a global model update every 6–12 hours to balance learning effectiveness and computational overhead.

[0227] The services communicate with each other via message queues or RPC mechanisms. In a preferred embodiment of the invention, a gRPC-based communication framework is used to provide high-performance, low-latency inter-service calls and support streaming data transmission. The end-to-end latency of critical paths (such as scheduling decision issuance) is controlled within 100ms to meet the requirements of real-time scheduling.

[0228] This architecture supports both horizontal and vertical scaling. Horizontal scaling is achieved by adding more resource nodes and corresponding proxy services; vertical scaling is achieved by upgrading the hardware configuration of the central coordination and analysis services. In large-scale deployment scenarios (such as large AI clusters containing 1000+ GPUs), the system can adopt a hierarchical architecture, introducing intermediate coordination nodes to reduce the burden on the central service and improve system scalability.

[0229] This embodiment provides a flexible and scalable system deployment scheme, enabling a dynamic scheduling and orchestration system for heterogeneous computing resources for large model training to adapt to AI data center environments of different sizes and configurations, and supporting seamless deployment from small experimental clusters to large-scale production environments.

[0230] Through the detailed description of the above embodiments, the heterogeneous computing resource dynamic scheduling and orchestration system for large model training provided by this invention achieves efficient management and dynamic scheduling of heterogeneous computing resources by innovatively introducing resource state modeling and scheduling optimization methods based on differential geometry theory. This system can significantly improve resource utilization, accelerate the large model training process, effectively prevent and eliminate resource bottlenecks, and provide an advanced resource management solution for AI data centers.

[0231] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any modifications, equivalent substitutions, and improvements made to the above embodiments based on the technical essence of the present invention should be included within the protection scope of the present invention.

Claims

1. A heterogeneous computing resource dynamic scheduling and orchestration system for large model training, characterized in that, include: A heterogeneous computing resource pool is used to provide various types of computing resources such as GPUs, NPUs, and TPUs; The computing power resource management module is communicatively connected to the heterogeneous computing power resource pool and is used to monitor and manage the resource status information in the heterogeneous computing power resource pool. The computing resource scheduling and orchestration optimization module, which is communicatively connected to the computing resource management module, is used for: Receive training task requirement information and obtain resource status information provided by the computing power resource management module; Based on the Riemannian manifold, a resource state space representation is constructed, and the resource state information is mapped to points on the manifold to construct a resource state tensor. An adaptive metric learning method is adopted to dynamically optimize the Riemann metric tensor based on historical task execution data, so that the geometric distance on the manifold reflects the actual cost of resource conversion. By solving the geodesic equations, the optimal resource scheduling path is planned on the resource state manifold. The optimal resource scheduling path is converted into a specific scheduling decision to achieve dynamic resource allocation; The communication module is connected to the computing resource scheduling and orchestration optimization module and is used to send the scheduling decision to the corresponding resources in the heterogeneous computing resource pool.

2. The system of claim 1, wherein, The heterogeneous computing power resource pool includes: The GPU resource pool contains various GPU resources categorized by performance level and connected via a GPU switch. The NPU resource pool contains various NPU resources categorized by performance level and connected via an NPU switch. The TPU resource pool contains various TPU resources classified by performance level and connected via TPU switches; Different types and levels of computing resources support different types of training tasks.

3. The system of claim 1, wherein, The computing resource management module includes: The hardware resource information management unit is used to record information such as the composition, type, quantity, capacity, performance, cost, and connection method of each hardware resource. The software information management unit is used to record information about software components such as operating systems, drivers, software libraries, compilers, virtualization software, and runtime environments on various computing resources. The operation information management unit is used to record in real time the operation status information of each computing resource, such as CPU utilization, memory utilization, failure time, task execution time and completion rate.

4. The system of claim 1, wherein, The computing resource scheduling and orchestration optimization module includes: The requirement-aware unit is used to obtain requirement information such as model type, data scale, and QoS requirements for training tasks; The task decomposition and requirements analysis unit is used to decompose complex training tasks into independently executable sub-task stages and analyze the resource requirements of each stage. The optimal scheduling unit is used to calculate the optimal resource scheduling path based on the resource state manifold and Riemann metric. The resource bottleneck monitoring unit is used to monitor the task execution process, predict potential resource bottlenecks, and propose resource adjustment suggestions.

5. The system of claim 1, wherein, The resource state space representation based on Riemannian manifolds includes: Construct a resource state tensor, which includes resource type dimension, performance level dimension, and state parameter dimension; Define a resource state point mapping, which maps each resource state configuration to a point on the manifold; Establish a local coordinate system to construct a local coordinate representation for each region on the manifold; Analyzing the connectivity of the resource state space, determining directly convertible states, and establishing the manifold topology; Implementing a region tree index structure to support efficient spatial queries and nearest neighbor searches.

6. The system of claim 1, wherein, The adaptive metric learning method includes: Defining the Riemannian metric tensor, where the metric includes dimensions such as time cost, resource efficiency, energy cost, and stability; Collecting actual cost data for resource conversion, including state transition time, resource utilization, and energy consumption; Calculate the deviation between predicted cost and actual cost, and construct the metric update objective function; Adjust the metric tensor parameters according to the deviation, while applying regularization constraints to ensure the smoothness of the metric; Design a multi-scale metric adaptation mechanism to adopt differentiated metric strategies for resource state changes at different scales.

7. The system of claim 1, wherein, The geodesic equation is solved by: Determining the starting and target state regions, corresponding to the current resource allocation state and the target state that meets the task requirements, respectively; Constructing a local metric scene to ensure calculation accuracy and efficiency; Solving the geodesic equation using an adaptive step numerical method; Generate a continuous geodesic path that reflects the optimal conversion sequence of resource states; Discretize the continuous path into executable scheduling steps.

8. The system of claim 1, wherein, The computing resource scheduling and arrangement optimization module is also used to: Analyze the curvature characteristics of the resource state manifold, identify high-curvature regions as potential resource bottlenecks; Monitor the deviation between the execution path and the predicted geodesic line as an indicator of resource state anomalies; Evaluate the diversity of actions available in the current state and predict scheduling flexibility; Based on geometric feature prediction, generate preventive resource adjustment strategies, including path re-planning, resource reservation, and load redistribution.

9. The system of claim 1, wherein, The computing resource scheduling and arrangement optimization module is also used to implement multi-task collaborative scheduling: Synchronize the calculation of multiple task geodesic paths; Analyze geodesic intersection regions to identify resource competition hotspots; According to the task priority and urgency, carry out resource competition arbitration; Adjust the task execution timing and resource allocation ratio to optimize overall execution efficiency; Continuously monitor and dynamically adjust multi-task resource allocation during execution.

10. The system of claim 1, wherein, The system uses a distributed deployment architecture: Central coordination service, responsible for managing global state and coordinating decisions; Resource agent service, deployed on each resource node, performs local monitoring and control; Scheduling execution service, responsible for the actual execution of scheduling decisions; Analysis service, perform historical data analysis and metric learning; Among them, each service communicates through message queues or RPC mechanisms, supporting horizontal and vertical expansion of the system.

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