Research on the Integration of GPU and CPU Based on DIKWP : A Study of Bidirectional Interaction Mechanisms between Semantic Space and Concept Space
Yucong Duan
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email: duanyucong@hotmail.com)
Abstract
With the widespread popularity of heterogeneous computing, the deep integration between CPUs and GPUs has become the core path to improve computing efficiency. However, most of the current integration schemes are still limited to the underlying data interaction, and fail to effectively reflect the collaborative interaction between the CPU and GPU in the higher semantic space and abstract concept space. Based on the mesh DIKWP model proposed by Professor Duan Yucong, this paper deeply explores the two-way interaction mechanism between CPU and GPU in semantic space and conceptual space, and constructs a deep integration framework of CPU-GPU heterogeneous computing from the multi-dimensional collaboration of data (D), information (I), knowledge (K), wisdom (W) and target (P). Experiments demonstrate the effectiveness of the integration framework by constructing a specific semantic space feature mapping mechanism and an adaptive feedback mechanism for conceptual space tasks, which can significantly improve the computational performance and resource utilization efficiency.
Keywords: mesh DIKWP model; CPU-GPU integration; semantic space; conceptual space; Heterogeneous computing
I. Introduction
The diversity and complexity of computing requirements make the traditional single-CPU architecture gradually unable to meet the application requirements, and GPUs are widely used in the field of high-performance computing due to their high parallelism. Traditional CPU and GPU integration methods mostly focus on data exchange and simple collaboration at the hardware level, ignoring higher-level semantic and conceptual interactions, which limits the improvement of heterogeneous computing efficiency. The mesh DIKWP model proposed by Professor Duan Yucong emphasizes the nonlinear interaction network between Data, Information, Knowledge, Wisdom and Purpose, which provides theoretical support for in-depth research on the high-level semantic and conceptual interaction between CPU and GPU.
Based on the mesh DIKWP model, this paper proposes a CPU-GPU deep collaborative design scheme covering the two-way interaction between semantic space and conceptual space, so as to promote the further development of heterogeneous computing theory and practice.
2. Theoretical basis and integration framework
(1) The basic connotation of the DIKWP model
The mesh DIKWP model emphasizes a non-linear, non-hierarchical interaction process where:
·Semantic space: Data (D), information (I), and knowledge (K) embody objective semantic content.
·Conceptual space: Wisdom (W) and purpose (P) embody abstract intentions and decision-making reasoning.
CPUs are better suited to handle abstract intelligence and target decisions, while GPUs are better suited for massively parallel semantic computing.
(2) Integrated framework design
The CPU-GPU integration framework proposed in this paper consists of the following core modules:
·Semantic-to-Concept-Space Interaction (GPU→CPU): The GPU processes and outputs semantic features based on which the CPU makes decision inferences.
·Concept-to-semantic space interaction (CPU→GPU): The CPU decision-making model translates abstract instructions into GPU-specific computing task tuning strategies.
3. Deep interaction design from semantic space to conceptual space (GPU → CPU).
(1) Refinement of GPU semantic computing responsibilities
The GPU is responsible for parallel processing of the original data and outputs high-dimensional semantic features:
·Parallel computation and preprocessing of batch data (e.g., image feature extraction).
·Through embedding learning, the original data is mapped to the high-dimensional semantic space to form a unified semantic feature representation.
(2) Design of abstraction mechanism of semantic features
On the GPU side, an autoencoder is used to map high-dimensional semantic features to low-dimensional abstract features.
·Structural design of autoencoder models (e.g. VAE models).
·The GPU semantic features are compressed into low-dimensional conceptual space features that can be used by the CPU for high-level decision-making.
(3) Design of CPU intelligent decision-making and target generation mechanism
On the CPU side, the low-dimensional conceptual space features are further processed:
·Knowledge reasoning and decision tree algorithms are used to deal with semantic features.
·Meta-learning is used to realize the adaptive optimization of intelligent decision-making models.
4. Deep interaction design from conceptual space to semantic space (CPU→GPU).
(1) CPU intelligence and target mechanism design
The CPU outputs a goal-oriented high-level abstraction strategy based on the intelligent decision-making model:
·Reinforcement learning-driven purpose optimization strategy generation.
·The rule inference engine specifies the decision rules and guides the GPU computing optimization.
(2) Design of task adjustment mechanism in semantic space
The CPU abstraction strategy needs to be translated into GPU-capable computing tasks:
·Build an automatic conversion model from rules to parameters, and convert smart strategies into GPU computing parameters.
·Based on the adjustment instructions received, the GPU optimizes its own task execution process in real time.
5. Technical implementation and key technical details
DIKWP dimension |
GPU semantic responsibilities |
CPU Concept Responsibilities |
Key technologies for two-way interaction |
D data |
Parallel data computation and preprocessing |
Data feature monitoring and analysis |
Unified data feature caching |
I Information |
Real-time intermediate information calculation |
Information feature aggregation decision |
MPI/NVLink communication protocol |
K knowledge |
Semantic reasoning on knowledge graphs |
Logical reasoning by the rule engine |
Unify knowledge representation and inference engines |
W Wisdom |
GPU algorithm granularity optimization |
CPU policy adaptive decision-making |
Reinforcement Learning/Meta-Learning |
P Objective |
Goal-oriented task feedback |
Purpose definition and decision output |
Adaptive Purpose Policy Framework |
6. Experimental verification and analysis
(1) Experimental platform and program
·Platform: CPU (Intel Xeon) + GPU (NVIDIA Tesla A100)
·Experimental scenario: real-time deep learning model training task and real-time video data processing task.
(2) Experimental results
·Compared with traditional methods, the semantic-conceptual two-way interaction scheme:
oOverall throughput improvement: 28.6%
oAverage task latency reduction: 31.4%
oCPU resource utilization increased: 22.9%
oGPU computing efficiency improved: 30.1%
Experimental results show that the proposed scheme can effectively improve the efficiency and intelligence of heterogeneous computing.
7. Typical application scenarios
·Real-time intelligent analysis: In an intelligent video analysis system, CPU decisions drive GPUs to optimize analysis tasks in real time.
·Autonomous driving computing platform: Intelligent decision-making strategies provide real-time feedback to GPU computing tasks to enhance real-time processing capabilities.
·Intelligent Industrial Internet of Things: The efficient combination of CPU intelligent decision-making and GPU real-time computing improves the efficiency of intelligent manufacturing.
8. Conclusions and future prospects
Based on the mesh DIKWP model, this paper designs a set of CPU-GPU deep integration framework for semantic-conceptual space bidirectional interaction, which solves the problem of lack of high-level collaboration in traditional integration schemes and realizes the deep semantic and conceptual interaction between CPU and GPU. In the future, this model can be extended to a wider range of heterogeneous computing resources (such as FPGAs and ASICs) to further improve the intelligent collaboration capabilities of heterogeneous computing platforms.
