Accelerator Runtime Control

Structural Problem

Modern AI infrastructure increasingly operates heterogeneous accelerator fleets — combinations of GPUs from different generations, TPUs, custom NPUs, and specialized ASICs. Each accelerator type has distinct performance characteristics, memory hierarchies, communication patterns, and failure modes. The structural problem is that runtime control systems designed for homogeneous fleets create incoherence when applied to heterogeneous execution environments.

The incoherence is structural rather than functional: each accelerator works correctly in isolation, but the control loops that manage scheduling, memory management, and communication across heterogeneous hardware create coupling effects that degrade composite system performance. A workload placed across GPU and TPU nodes may experience synchronization mismatches, memory bandwidth asymmetries, and communication protocol incompatibilities that are invisible to accelerator-specific monitoring.

System Context

This application operates across the accelerator fleet management layer, spanning hardware abstraction, runtime execution, memory management, and inter-accelerator communication. The relevant system boundary includes hardware driver stacks, accelerator-specific runtimes (CUDA, XLA, custom NPU SDKs), unified execution frameworks, and the orchestration layers that allocate workloads to heterogeneous resources.

Diagnostic Capability

• Structural compatibility analysis between accelerator types for specific workload profiles, identifying coupling conflicts before deployment
• Runtime incoherence detection across heterogeneous execution paths, tracing performance degradation to specific cross-accelerator interactions
• Memory hierarchy mismatch diagnostics between accelerator types sharing workloads
• Communication protocol structural assessment for inter-accelerator data transfer paths

Typical Failure Modes

• Synchronization mismatch where accelerators with different clock domains and execution models create structural timing conflicts in collective operations
• Memory bandwidth asymmetry where workloads split across accelerator types encounter bottlenecks at the slowest memory interface
• Driver-level coupling where accelerator driver interactions create contention through shared kernel or OS resources
• Abstraction layer overhead where unified execution frameworks introduce structural overhead that negates the benefits of heterogeneous specialization

Example Use Cases

• Fleet composition planning: Structural analysis of proposed heterogeneous fleet configurations to assess compatibility and performance stability
• Workload-hardware matching: Structural guidance for which workload types can be effectively distributed across which accelerator combinations
• Migration path assessment: Structural analysis of transitioning from one accelerator generation to another while maintaining fleet-level stability

Strategic Relevance

Heterogeneous accelerator fleets are becoming the norm rather than the exception as organizations diversify their compute infrastructure. Structural control over heterogeneous execution is a prerequisite for extracting value from fleet diversity rather than suffering from it. Organizations that master heterogeneous fleet management gain both cost flexibility and vendor independence.

SORT Structural Lens

The SORT framework addresses this application through four structural dimensions, each providing a distinct analytical layer.