AI-Based Modeling of System Reliability and Performance Metrics in Heterogeneous Computing Architectures

Abstract

The dramatic increase in the number of heterogeneous computing architectures (HCAs) which include CPUs, GPUs, TPUs, FPGAs, and new neuromorphic processors has transformed the high-performance computing (HPC) and artificial intelligence (AI) workloads. As the complexity of architecture increases there is corresponding uncertainty on reliability and performance checks, particularly when systems are incorporated with different processing elements, interconnections, and memory levels. Modern non-deterministic or non-stochastic heterogeneous environments are associated with traditional reliability models that feel great necessity to model nonlinear interdependencies between components. The current paper proposes an innovative AI-based modeling framework that would be capable of measuring system reliability and performance indicators in the context of HCAs. The suggested solution merges Deep Neural Networks (DNNs), Bayesian Inference Models, and the strategies of Reinforcement Learning (RL) to forecast the increase of the system reliability, the performance bottlenecks, and the mean time between failures (MTBF) at the system level. To train AI models that can capture the multi-domain interactions of computation, communication, and thermal dynamics, a large-scale simulation dataset was created with synthetic benchmarks, actual workloads (SPEC ACCEL, MLPerf, HPCG), unlikely fault profiles, and injected fault profiles to have AI models that are trained to simulate interactions in large-scale systems. The approach puts the feature engineering of runtime telemetry (e.g., power, temperature, utilization) and hardware counters (e.g., instruction-level parallelism, cache miss rates) to construct predictive and adaptive reliability estimators. As opposed to the traditional models, our AI-based system advances its state dynamically with online learning methods of training, allowing its internal state to identify fault and self-optimize it. Findings suggest that the suggested AI system can make accurate predictions in both system performance degradation and reliability estimates with a success level of 96.8 and 94.2 per cent respectively when operating in a heterogeneous environment. Comparative research with classical models of reliability, i.e. Markov and Weibull-based models reveal high gains on adaptability, precision and generalization. Moreover, the optimization using reinforcement learning provided a 1525 percentage ratio in task scheduling performance expressed in a limited thermal and power budget. This article adds to the increasing overlap between system modeling based on AI and the optimization of heterogeneous computing, sets the stage of the introduction of new intelligent reliability management of data centers, autonomous computing systems, and HPC infrastructure