Predictive GPU Kernel Preparation: Forecasting Future Kernel Execution Requirements and Proactively Preparing Computational Operations

Modern GPU computing systems execute increasingly complex computational workloads through large collections of specialized GPU kernels. Deep learning applications, large language models, scientific computing systems, and other GPU-accelerated applications frequently invoke different kernel implementations according to tensor dimensions, data types, memory layouts, computational operators, hardware architectures, and runtime execution conditions. Although existing GPU programming frameworks and deep learning compilers provide just-in-time (JIT) compilation, kernel caching, automatic kernel generation, and autotuning mechanisms, the preparation of a required kernel may still introduce additional computational overhead when an appropriate compiled implementation or optimized execution configuration is unavailable. This paper introduces Predictive GPU Kernel Preparation (PGKP), a conceptual framework that investigates whether future GPU kernel execution requirements can be predicted from historical kernel invocation sequences, computational graph structures, runtime execution traces, workload characteristics, and application execution states. Rather than waiting for an individual kernel invocation to trigger compilation, configuration selection, or optimization, PGKP attempts to anticipate which kernel operations and specializations are likely to be required and proactively prepares their corresponding executable implementations and execution configurations. Its architecture integrates kernel invocation forecasting, workload and tensor-shape prediction, kernel specialization identification, predictive compilation, cost-aware autotuning, execution configuration selection, prepared-kernel caching, and adaptive runtime dispatch. A central component of the framework is a predictive preparation mechanism that estimates the probability of future kernel invocations and evaluates whether advance compilation and optimization are justified by their expected runtime benefits. The framework further distinguishes between kernel source generation, compilation, autotuning, execution configuration preparation, and actual GPU kernel execution, recognizing that these operations have different resource requirements and latency characteristics. A unified optimization formulation is developed to minimize the combined cost of kernel preparation and runtime execution while accounting for prediction uncertainty, compilation expenditure, autotuning cost, cache capacity, GPU and CPU resource availability, hardware compatibility, and execution deadlines. Particular attention is given to the possibility of performing compilation and optimization during periods when the corresponding computational resources are relatively underutilized, without allowing speculative preparation to interfere with active GPU workloads. An experimental methodology is established to evaluate kernel prediction accuracy, preparation hit rate, cold-start latency, first-invocation latency, total compilation expenditure, autotuning overhead, kernel execution performance, and overall application completion time. The framework does not assume that predictive preparation is universally beneficial; its effectiveness depends on the predictability of kernel invocation patterns, the stability of workload characteristics, the reuse frequency of prepared implementations, and the relationship between advance preparation costs and subsequent execution benefits. By extending predictive computing to the GPU kernel level, this research investigates a computational paradigm in which GPU systems anticipate future low-level computational operations and prepare their executable implementations before actual execution requirements materialize.