An mlbm-dem pipeline scheduling optimization method for heterogeneous multi-domain processors

Abstract

The application provides an MLBM-DEM pipeline scheduling optimization method for a heterogeneous multi-domain processor. The method comprises the following steps: S1, performing data division based on application perception and the heterogeneous multi-domain processor, respectively calculating the calculation amount of the MLBM and the DEM, and the calculation capability proportion of the MLBM and the DEM; S2, allocating the calculation resources of the CPU end and the acceleration domain cluster according to the calculation capability proportion of the MLBM and the DEM, and constructing a pipeline design module for the heterogeneous multi-domain processor; S3, obtaining the remaining calculation resources except the CPU end and the acceleration domain cluster, and performing a Manager-Worker real-time scheduling mechanism. The application realizes MLBM-DEM pipeline scheduling optimization calculation under the condition of ensuring calculation accuracy, and further improves the overall calculation performance.

Description

Technical field

[0001] The present invention relates to the fields of fluid mechanics and high-performance computing, and in particular to an MLBM-DEM pipeline scheduling optimization method for heterogeneous multi-domain processors. [Background Technology]

[0002] In today's era of rapid technological development, high-performance computing plays an important role in many fields. Fluid mechanics simulation, as the basis of scientific research and engineering design, requires large-scale computing resources to achieve accurate simulation. The Discrete Element Multi-Level Lattice Boltzmann Method (MLBM-DEM) is a numerical simulation technology that combines the Discrete Element Method (DEM) and the Multi-Level Lattice Boltzmann Method (MLBM). It aims to simulate and analyze complex systems composed of fluids and granular materials. This method combines the respective advantages of DEM and MLBM, and is used to study the dynamic behavior of particle-fluid interactions, with broad application prospects. Due to its complex scenarios, MLBM-DEM has a larger amount of computation. Therefore, using the heterogeneous multi-domain processors of domestic E-class supercomputer systems to accelerate it can effectively reduce the amount of computation and improve computing performance and simulation accuracy. However, since the core calculation of MLBM-DEM lies in the collision migration calculation in MLBM and the interaction force between particles in DEM, the calculation of these two parts is carried out in parallel on a heterogeneous multi-domain processor, and the remaining boundary processing and other tasks are placed on the CPU side. This will produce a pipeline with strong data dependencies, resulting in challenges in data partitioning and load balancing, especially when applied to domestic E-class supercomputer systems.

[0003] Today, computational fluid dynamics research continues to grow in scale and complexity, and single computing nodes are no longer sufficient to meet computing demands, necessitating the reliance on high-performance computers. my country's supercomputers are evolving from petabytes to exabytes, with heterogeneous multiprocessors and accelerators becoming a growing trend. With the bottleneck of single-core performance improvements after Dennard expansion, the construction of multi-core high-performance processors has become essential.

[0004] How to implement the pipeline scheduling optimization method of MLBM-DEM on heterogeneous multi-domain processors to fully utilize the performance of heterogeneous multi-domain processors and improve the computational efficiency of MLBM-DEM, as well as how to design and optimize the pipeline scheduling algorithm with strong data dependence, have become urgent problems to be solved. [Summary of the invention]

[0005] In response to the shortcomings of the existing technology, this application provides an MLBM-DEM pipeline scheduling optimization method for heterogeneous multi-domain processors, realizes data partitioning based on application perception and heterogeneous multi-domain processors, constructs a pipeline design module for heterogeneous multi-domain processors, and designs a Manager-Worker real-time scheduling mechanism to optimize pipeline scheduling, further improving the overall computing performance.

[0006] The present application provides an MLBM-DEM pipeline scheduling optimization method for heterogeneous multi-domain processors, comprising the following steps:

[0007] S1. Perform data partitioning based on application awareness and heterogeneous multi-domain processors, and calculate the computational load of MLBM and DEM, as well as the computational capacity ratio of MLBM and DEM respectively.

[0008] S2. Allocate computing resources of the CPU and the acceleration domain cluster according to the computing power ratio of the MLBM and the DEM, and build a pipeline design module for heterogeneous multi-domain processors;

[0009] S3: Obtain the remaining computing resources except for the CPU and acceleration domain clusters, and execute the Manager-Worker real-time scheduling mechanism.

[0010] Preferably, the heterogeneous multi-domain processor includes a general area and an acceleration area, and includes the following features:

[0011] The heterogeneous multi-domain processor includes 16 CPUs, 96 control cores (Ctrl), and 1536 acceleration cores (Acc). It achieves 11.6 TFLOPS of double-precision performance and 45.4 GFLOPS / W of power efficiency when running at 1.2 GHz. The heterogeneous multi-domain processor has the following features:

[0012] (1) Heterogeneous multi-domain microarchitecture, where 16 CPUs form a general-purpose region, 96 control cores (Ctrl) and 1536 acceleration cores (Acc) form an acceleration region, and the acceleration region is evenly divided into four acceleration domain clusters;

[0013] (2) Combining Very Long Instruction Word (VLIW) and Accelerator Array microarchitecture, where each acceleration core operates in VLIW mode, and every 16 acceleration cores (Acc) and 1 control core (Ctrl) are organized into an acceleration array;

[0014] (3) Ultra-high bandwidth and high reliability array memory, which is a vector memory that supports up to two vector loads / stores. The data types loaded / stored by the array memory include halfword (32 bits), word (64 bits) and doubleword (128 bits). Therefore, the array memory can provide up to 512 bytes (16 × 2 × 128 bits) to 16 accelerator cores at the same time.

[0015] Each acceleration domain cluster has 24 control cores (Ctrl), 384 acceleration cores (Acc), as well as on-chip shared memory (GSM), high-bandwidth shared memory (HBSM), and off-chip DDR memory. Each acceleration domain cluster executes independently. The CPU in the general area can access all HBSM and DDR spaces across different acceleration domain clusters, while the control cores and accelerator cores can only access the GSM, HBSM, and DDR within their respective acceleration domain clusters.

[0016] Data sharing between different acceleration domain clusters is implemented on the CPU side. The 16 CPUs in the general area have their own L1 and L2 caches and are connected via a cache-coherent mesh NoC. The general area is capable of overall task control, operating system booting, and general processing, while the acceleration area is designed for compute-intensive tasks. In each acceleration domain cluster, one control core (Ctrl) and 16 acceleration cores (Acc) form an acceleration array, while 24 control cores (Ctrl) and 384 acceleration cores (Acc) form 24 acceleration arrays.

[0017] Preferably, the data partitioning based on application awareness and heterogeneous multi-domain processors includes the following steps:

[0018] In heterogeneous computing, the relationship between task size and computing power is crucial. Appropriate task partitioning can significantly improve computing efficiency and achieve load balancing. Based on this, we propose an application-aware dynamic data partitioning method. This method dynamically partitions and allocates computing grids based on the characteristics of the computing tasks and the amount of data. This method dynamically adapts and optimizes computing resource usage based on the needs of different computing tasks, thereby improving computing efficiency and reducing resource waste. The method includes the following steps:

[0019] S11. Calculate the adjusted parallel processing capability

[0020]

[0021] Among them, P adj is the parallel processing capability after adjustment, C i is the computing power of the i-th core, f i is the proportion of this type of core in the total calculation, and n is the number of core types; for heterogeneous multi-domain processors, it includes CPU and acceleration domain clusters;

[0022] S12. Obtain the required ratio of computing power to data volume

[0023]

[0024] Where R is the required ratio of computational load to data volume, C and D are the computational load and data volume required to complete a grid block, respectively;

[0025] S13. Calculating the ideal size of pipeline grid blocks on heterogeneous multi-domain processors

[0026]

[0027] Where B is the memory bandwidth and L is the data transmission delay;

[0028] S14. Calculate the size of pipeline grid blocks on heterogeneous multi-domain processors

[0029]

[0030] Among them, α is the adaptive adjustment factor;

[0031] S15. Calculation of MLBM and DEM and the ratio of computing power between them

[0032] Assume that the computational cost of MLBM is C MLBM , represents the computational effort required to perform the MLBM operation, including the collision and migration steps; the computational effort for DEM is C DEM , represents the amount of calculation required to perform DEM operations, including the calculation of interactions between particles; the initial data volume of MLBM is D MLBM , represents the initial data volume of MLBM in the user input data scale; the initial data volume of DEM is D DEM , represents the initial data volume of DEM in the user input data scale; assuming that the computing power ratio between MLBM and DEM is R comp ,but:

[0033] The computational cost of MLBM C MLBM It is related to the number of grid points and the number of operations performed on each grid point. Assuming that an average of O MLBM operations, then:

[0034]

[0035] The calculation amount of DEM C DEM Depends on the number of particles and the computational complexity of the interaction between each pair of particles; assuming that each pair of particles performs an average of O DEM operations. Since the interaction between particles usually involves all particle pairs, the computational cost is approximately:

[0036]

[0037] Therefore, the computing power ratio R between MLBM and DEM is comp , expressed as:

[0038]

[0039] Preferably, the pipeline design module for heterogeneous multi-domain processors includes:

[0040] MLBM information update and boundary processing module: Based on the relevant information fed back by the DEM information update and feedback module, the CPU side updates the MLBM information and processes the boundary conditions;

[0041] MLBM collision migration calculation module: Based on MLBM information update and boundary processing, it is used to perform MLBM collision migration calculation in the acceleration domain cluster;

[0042] Meshing model: Based on MLBM collision migration calculation, CPU-side adaptive meshing;

[0043] Information transmission module: The CPU transmits the relevant information of MLBM to DEM;

[0044] DEM core calculation module: used to perform DEM core calculation in the acceleration domain cluster;

[0045] DEM information update and feedback module: used by the CPU to update DEM related information and feed the information back to the MLBM information update and boundary processing module.

[0046] In order to avoid pipeline blocking, the following strategies are designed:

[0047] (1) Introducing a buffer: A buffer is set up between each pipeline module to store temporary calculation results and ensure smooth data transfer between processors. For example, when the MLBM collision migration calculation module completes the MLBM collision migration calculation, the result will be stored in the buffer and wait for the CPU to perform subsequent processing.

[0048] (2) Efficient inter-process communication: Use MPI for efficient inter-process communication to deliver Halo layer data in a timely manner, ensuring that the next stage of calculation is not affected by data transmission delays.

[0049] (3) Asynchronous computing and communication overlap: Asynchronous computing and communication overlap technology is used to enable the processor to receive and send data simultaneously while performing calculations, further improving the overall efficiency of the pipeline.

[0050] Preferably, the Manager-Worker real-time scheduling mechanism includes the following steps:

[0051] S31. Divide the remaining computing resources into a Manager node and multiple Worker nodes;

[0052] S32. Based on the real-time polling and dynamic scheduling mechanism, the multiple Worker nodes execute task scheduling and regularly report task progress to the Manager;

[0053] S33. The Manager receives the task progress of the Worker node, dynamically schedules the Worker node based on real-time data, and sends a termination message to the Worker node after all tasks are completed;

[0054] S34: The Worker node receives the termination message, performs cleanup, and exits. Experiments have shown that when using this method, an MLBM-DEM pipeline scheduling optimization method for heterogeneous multi-domain processors, 16 CPU cores and 4 acceleration domain clusters have significantly improved computing performance compared to 16 CPU cores. This is particularly significant for large-scale data sets, even reaching more than three times the original computing performance.

[0055] While ensuring computing accuracy, the present invention implements data partitioning based on application awareness and heterogeneous multi-domain processors, designs a Manager-Worker real-time scheduling mechanism to optimize pipeline scheduling, and further improves overall computing performance.

Brief Description of the Drawings

[0056] Figure 1 This is a flow chart of an MLBM-DEM pipeline scheduling optimization method for heterogeneous multi-domain processors of the present invention;

[0057] Figure 2 This is a diagram of the acceleration domain framework of a heterogeneous multi-domain processor according to an MLBM-DEM pipeline scheduling optimization method for heterogeneous multi-domain processors of the present invention;

[0058] Figure 3 This is a diagram of the Manager-Worker real-time scheduling mechanism of the MLBM-DEM pipeline scheduling optimization method for heterogeneous multi-domain processors of the present invention. [Specific implementation method]

[0059] This paper discloses an MLBM-DEM pipeline scheduling optimization method for heterogeneous multi-domain processors. This application features high computational performance while ensuring accuracy and is widely used in multiple fields, including geotechnical engineering (such as soil liquefaction research and slope stability analysis), chemical engineering (such as multiphase flow reactor design and particle flow), biofluid mechanics (such as drug delivery and cell motility), environmental engineering (such as pollutant diffusion and soil erosion), and energy engineering (such as particle combustion and gas-solid two-phase flow).

[0060] This application can significantly improve computing performance while ensuring accuracy. The specific implementation methods of this application will be described in detail below with reference to the accompanying drawings. It is worth noting that what is described in this implementation method is only a relatively common use method of this application. The technology used in this application should be protected until practitioners in related fields can develop better solutions.

[0061] In one embodiment, Figure 1 The figure shows the overall flow chart of an MLBM-DEM pipeline scheduling optimization method for heterogeneous multi-domain processors. It can be seen that

[0062] Step 101 , performing data partitioning based on application awareness and heterogeneous multi-domain processors, and calculating the computational load of MLBM and DEM and the computational capacity ratio of MLBM and DEM respectively;

[0063] Step 102: Allocate computing resources of the CPU and the acceleration domain cluster according to the computing power ratio of the MLBM and the DEM, and construct a pipeline design module for heterogeneous multi-domain processors;

[0064] Step 103: Obtain the remaining computing resources except the CPU and the acceleration domain cluster, and execute the Manager-Worker real-time scheduling mechanism.

[0065] In step 101, data partitioning based on application awareness and heterogeneous multi-domain processors includes the following steps:

[0066] (1) Calculate the adjusted parallel processing capability

[0067]

[0068] Among them, P adj is the parallel processing capability after adjustment, C i is the computing power of the i-th core, f i is the proportion of this type of core in the total calculation, and n is the number of core types; for heterogeneous multi-domain processors, it includes CPU and acceleration domain clusters;

[0069] (2) Obtaining the required ratio of computing power to data volume

[0070]

[0071] Where R is the required ratio of computational load to data volume, C and D are the computational load and data volume required to complete a grid block, respectively;

[0072] (3) Calculating the ideal size of pipeline grid blocks on heterogeneous multi-domain processors

[0073]

[0074] Where B is the memory bandwidth and L is the data transmission delay;

[0075] (4) Calculating the size of pipeline grid blocks on heterogeneous multi-domain processors

[0076]

[0077] Among them, α is the adaptive adjustment factor;

[0078] (5) Calculation of the computational effort for MLBM and DEM and the computational power ratio between them

[0079] Assume that the computational cost of MLBM is C MLBM , represents the computational effort required to perform the MLBM operation, including the collision and migration steps; the computational effort for DEM is C DEM , represents the amount of calculation required to perform DEM operations, including the calculation of interactions between particles; the initial data volume of MLBM is D MLBM , represents the initial data volume of MLBM in the user input data scale; the initial data volume of DEM is D DEM , represents the initial data volume of DEM in the user input data scale; assuming that the computing power ratio between MLBM and DEM is R comp ,but:

[0080] The computational cost of MLBM C MLBM It is related to the number of grid points and the number of operations performed on each grid point. Assuming that an average of O MLBM operations, then:

[0081]

[0082] The calculation amount of DEM C DEM Depends on the number of particles and the computational complexity of the interaction between each pair of particles; assuming that each pair of particles performs an average of O DEM operations. Since the interaction between particles usually involves all particle pairs, the computational cost is approximately:

[0083]

[0084] Therefore, the computing power ratio R between MLBM and DEM is comp , expressed as:

[0085]

[0086] In step 102, the pipeline design module for the heterogeneous multi-domain processor includes the following steps:

[0087] MLBM information update and boundary processing module: Based on the relevant information fed back by the DEM information update and feedback module, the CPU side updates the MLBM information and processes the boundary conditions;

[0088] MLBM collision migration calculation module: Based on MLBM information update and boundary processing, it is used to perform MLBM collision migration calculation in the acceleration domain cluster;

[0089] Meshing model: Based on MLBM collision migration calculation, CPU-side adaptive meshing;

[0090] Information transmission module: The CPU transmits the relevant information of MLBM to DEM;

[0091] DEM core calculation module: used to perform DEM core calculation in the acceleration domain cluster;

[0092] DEM information update and feedback module: used by the CPU to update DEM related information and feed the information back to the MLBM information update and boundary processing module.

[0093] In order to avoid pipeline blocking, the following strategies are designed:

[0094] (1) Introducing a buffer: A buffer is set up between each pipeline module to store temporary calculation results and ensure smooth data transfer between processors. For example, when the acceleration domain cluster completes the collision migration calculation of MLBM, the result will be stored in the buffer and wait for subsequent processing by the CPU.

[0095] (2) Efficient inter-process communication: Use MPI for efficient inter-process communication to deliver Halo layer data in a timely manner, ensuring that the next stage of calculation is not affected by data transmission delays.

[0096] (3) Asynchronous computing and communication overlap: Asynchronous computing and communication overlap technology is used to enable the processor to receive and send data simultaneously while performing calculations, further improving the overall efficiency of the pipeline.

[0097] In step 103, the Manager-Worker real-time scheduling mechanism includes the following steps:

[0098] S31. Divide the remaining computing resources into a Manager node and multiple Worker nodes;

[0099] S32. Based on the real-time polling and dynamic scheduling mechanism, the multiple Worker nodes execute task scheduling and regularly report task progress to the Manager;

[0100] S33. The Manager receives the task progress of the Worker node, dynamically schedules the Worker node based on real-time data, and sends a termination message to the Worker node after all tasks are completed;

[0101] S34. The Worker node receives the termination message, performs cleanup work and exits.

[0102] In one embodiment, Figure 2 The figure shows the overall framework of a heterogeneous multi-domain processor using the MLBM-DEM pipeline scheduling optimization method. It can be seen that

[0103] The heterogeneous multi-domain processor includes 16 CPUs, 96 control cores (Ctrl), and 1536 acceleration cores (Acc). It achieves 11.6 TFLOPS of double-precision performance and 45.4 GFLOPS / W of power efficiency when running at 1.2 GHz. The heterogeneous multi-domain processor has the following features:

[0104] (1) Heterogeneous multi-domain microarchitecture, where 16 CPU cores form a general-purpose region, 96 control cores (Ctrl) and 1536 acceleration cores (Acc) form an acceleration region, and the acceleration region is evenly divided into four acceleration domain clusters;

[0105] (2) Combining Very Long Instruction Word (VLIW) and Accelerator Array microarchitecture, where each acceleration core operates in VLIW mode, and every 16 acceleration cores and one control core are organized into an acceleration array;

[0106] (3) Ultra-high bandwidth and high reliability array memory, which is a vector memory that supports up to two vector loads / stores. The data types loaded / stored by the array memory include halfword (32 bits), word (64 bits) and doubleword (128 bits). Therefore, the array memory can provide up to 512 bytes (16 × 2 × 128 bits) to 16 accelerator cores at the same time.

[0107] Each acceleration domain cluster consists of 24 control cores (Ctrl), 384 acceleration cores (Acc), as well as on-chip shared memory (GSM), high-bandwidth shared memory (HBSM), and off-chip DDR memory. Each acceleration domain cluster executes independently. The CPU in the general area can access all HBSM and DDR memory spaces across all acceleration domain clusters, while the control cores and accelerator cores can only access the GSM, HBSM, and DDR within their respective acceleration domain clusters. Data sharing between different acceleration domain clusters is achieved through the CPUs. The 16 CPUs in the general area have their own L1 and L2 caches and are connected via a cache-coherent mesh NoC. The general area is capable of overall task control, operating system booting, and general processing. It is designed for compute-intensive tasks. In each acceleration domain cluster, one control core (Ctrl) and 16 acceleration cores (Acc) form an acceleration array, and the 24 control cores (Ctrl) and 384 acceleration cores (Acc) form 24 acceleration arrays.

[0108] In one embodiment, Figure 3 The figure shows an MLBM-DEM pipeline scheduling optimization method for heterogeneous multi-domain processors. It can be seen that

[0109] In step 301, the system initially allocates tasks to the CPU and acceleration domain clusters based on their computing requirements and the computing capabilities of their respective platforms. The entire computing cluster is divided into a manager node and multiple worker nodes. Worker nodes include both CPU cores and compute nodes in the acceleration domain cluster. These nodes are not responsible for initial fixed task allocation and are instead dynamically managed by the manager node.

[0110] In step 302, the Manager monitors the execution of all tasks in real time, paying particular attention to the data dependencies between tasks and the remaining computing power of each task. Based on real-time data such as idle and busy status, the Manager can flexibly schedule Worker nodes to process computing tasks that are about to be completed and have important data dependencies on subsequent tasks, so as to avoid any task delays or interruptions. In addition, when the Worker node is close to completing the current task, the Manager continues to monitor the computing load of the entire system to ensure that the Worker node can be immediately rescheduled to process other tasks that need to be accelerated after completing a task. This real-time polling and dynamic task scheduling mechanism is designed to maintain the efficient operation of all computing nodes, avoid the idle state of any node, and ensure that tasks that are closely linked due to data dependencies can be executed smoothly and continuously.

[0111] This real-time monitoring and dynamic scheduling approach not only ensures smooth and efficient task execution, but also maximizes the utilization of domestically produced E-class supercomputer system resources, achieving efficient and balanced distribution of computing tasks. This approach is particularly well-suited for complex computing tasks with strong data dependencies, effectively improving overall computing performance and resource utilization.

[0112] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] Finally, it should be noted that while this invention primarily presents specific examples and designs, and the software involved is in the form of a system, all components of this system, including but not limited to data partitioning, the Manager-Worker real-time scheduling mechanism, and its code, should still be protected. Therefore, any modifications, improvements, or uses of this invention should be covered by the claims and specification.

Claims

1. A MLBM-DEM pipeline scheduling optimization method for heterogeneous multi-domain processors, characterized by: The following steps are involved: S1. Perform data partitioning based on application awareness and heterogeneous multi-domain processors to obtain the computing power ratio of MLBM and DEM; S2. Allocate computing resources of the CPU and acceleration domain cluster according to the computing power ratio of the MLBM and DEM, and build a pipeline design module for heterogeneous multi-domain processors; S3: Obtain the remaining computing resources except for the CPU and acceleration domain clusters and execute the Manager-Worker real-time scheduling mechanism. The data partitioning based on application awareness and heterogeneous multi-domain processors to obtain the computing power ratio of MLBM and DEM includes the following steps: S11. Calculate the adjusted parallel processing capability in, is the parallel processing capability after adjustment, For the The computing power of the core, is the proportion of this type of core in the total calculation, is the number of core types; for heterogeneous multi-domain processors, it includes CPU and acceleration domain clusters; S12. Obtain the required ratio of computing power to data volume in, is the required ratio of computing power to data volume, and are the amount of computation and data required to complete a grid block, respectively; S13. Calculating the ideal size of pipeline grid blocks on heterogeneous multi-domain processors in, is the memory bandwidth, Delay in data transmission; S14. Calculate the size of pipeline grid blocks on heterogeneous multi-domain processors in, is the adaptive adjustment factor; S15. Calculation of MLBM and DEM and the ratio of computing power between them Assume that the computational cost of MLBM is , represents the computational effort required to perform the MLBM operation, including the collision and migration steps; the computational effort for DEM is , represents the amount of calculation required to perform DEM operations, including the calculation of interactions between particles; the initial data volume of MLBM is , represents the initial data volume of MLBM in the user input data scale; the initial data volume of DEM is , represents the initial data volume of DEM in the user input data scale; assuming that the computing power ratio between MLBM and DEM is ,but: The computational cost of MLBM It is related to the number of grid points and the number of operations performed at each grid point, assuming that the average number of operations performed at each point is operations, then: The amount of calculation of DEM Depends on the number of particles and the computational complexity of the interaction between each pair of particles; assuming that each pair of particles performs an average of operations. Since the interaction between particles usually involves all particle pairs, the computational cost is approximately: Therefore, the computing power ratio between MLBM and DEM , expressed as: 。 2. The method according to claim 1, characterized in that The heterogeneous multi-domain processor includes a general area and an acceleration area. The general area includes 16 CPU cores; the acceleration area is evenly divided into four acceleration domain clusters, and the acceleration domain clusters include 24 control cores Ctrl, 384 acceleration cores Acc, on-chip shared memory GSM, high-bandwidth shared memory HBSM and off-chip memory DDR.

3. The method according to claim 2, characterized in that The pipeline design module for heterogeneous multi-domain processors includes: MLBM information update and boundary processing module: Based on the relevant information fed back by the DEM information update and feedback module, the CPU side updates the MLBM information and processes the boundary conditions; MLBM collision migration calculation module: Based on MLBM information update and boundary processing, it is used to perform MLBM collision migration calculation in the acceleration domain cluster; Meshing model: Based on MLBM collision migration calculation, CPU-side adaptive meshing; Information transmission module: The CPU transmits the relevant information of MLBM to DEM; DEM core calculation module: used to perform DEM core calculation in the acceleration domain cluster; DEM information update and feedback module: used by the CPU to update DEM related information and feed the information back to the MLBM information update and boundary processing module.

4. The method according to claim 3, characterized in that A buffer zone is set between each pipeline design module to store temporary calculation results.

5. The method according to claim 1, wherein The Manager-Worker real-time scheduling mechanism includes the following steps: S31. Divide the remaining computing resources into a Manager node and multiple Worker nodes; S32. Based on the real-time polling and dynamic scheduling mechanism, the multiple Worker nodes execute task scheduling and regularly report task progress to the Manager; S33. The Manager receives the task progress of the Worker node, dynamically schedules the Worker node based on real-time data, and sends a termination message to the Worker node after all tasks are completed; S34. The Worker node receives the termination message, performs cleanup work and exits.

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