Modern machine learning workloads frequently execute on systems composed of diverse processing units, including multi-core CPUs, powerful GPUs, and specialized AI accelerators (like TPUs, NPUs, or custom ASICs). Effectively harnessing the combined power of these resources requires sophisticated runtime scheduling strategies. The goal is to orchestrate the execution of computational tasks across these different devices to minimize overall execution time while maximizing hardware utilization. This is a complex optimization problem, demanding careful consideration of computational costs, data locality, inter-device communication overhead, task dependencies, and the specific capabilities of each hardware component.

Characterizing Compute Resources

A prerequisite for intelligent scheduling is a clear understanding of the target hardware's capabilities. The runtime scheduler often relies on a performance model, either derived statically or refined through runtime profiling, which characterizes each device based on metrics such as:

Compute Throughput: Measured in FLOPS (Floating-Point Operations Per Second) or TOPS (Tera Operations Per Second), often differentiating between precisions (e.g., FP32, FP16, INT8).
Memory Bandwidth: The rate at which data can be read from or written to the device's local memory (e.g., HBM, GDDR6, DDR4).
Memory Capacity: The total amount of memory available on the device.
Specialized Units: Presence and performance of hardware accelerators for specific operations (e.g., NVIDIA Tensor Cores, AMD Matrix Cores).
Communication Interfaces: Bandwidth and latency characteristics of the connections between devices and host memory (e.g., PCIe generations, NVLink, Infinity Fabric).

Accurate characterization allows the scheduler to make informed decisions about where and when to execute specific computational kernels.

Task Representation and Dependencies

ML computations are typically represented as a Directed Acyclic Graph (DAG), where nodes represent operations (kernels) and edges represent data dependencies (tensors). The scheduler operates on this graph, deciding the placement and execution order of tasks.

A simplified ML inference graph showing potential device placement for tasks and highlighting data transfers between host (CPU) and device (GPU) memory.

The granularity of tasks within the DAG significantly impacts scheduling. Fine-grained tasks (e.g., individual arithmetic operations) offer maximum flexibility but incur high scheduling and synchronization overhead. Coarse-grained tasks (e.g., fused operator sequences) reduce overhead but limit opportunities for parallel execution and load balancing across devices. Many runtimes operate on an intermediate granularity, often corresponding to individual ML framework operations or optimized kernels.

Scheduling Objectives and Trade-offs

While minimizing end-to-end latency is often the primary goal, other objectives influence scheduling decisions:

Throughput: Maximizing the number of inferences or training steps completed per unit time.
Resource Utilization: Keeping expensive accelerators busy to justify their cost.
Energy Efficiency: Minimizing power consumption, particularly critical in mobile or edge deployments.
Quality of Service (QoS): Meeting specific latency deadlines or fairness criteria in multi-tenant environments.

These objectives can conflict. For example, maximizing throughput might involve batching requests, potentially increasing latency for individual requests. Energy-saving strategies might involve powering down devices or running them at lower frequencies, impacting peak performance. The scheduler must often balance these competing demands based on system policies or application requirements.

Core Scheduling Strategies

Runtime schedulers employ various strategies to assign tasks to devices and determine their execution order:

Static Scheduling: Scheduling decisions are made ahead-of-time (AOT), typically during the compilation phase. The compiler analyzes the DAG, estimates task execution times and communication costs based on device profiles, and generates a fixed execution plan. Algorithms like Heterogeneous Earliest Finish Time (HEFT) or critical path analysis, adapted for heterogeneous costs, are often used.
- Pros: Minimal runtime overhead, predictable performance (assuming accurate cost models).
- Cons: Inflexible to runtime variations (e.g., dynamic data sizes, contention from other processes), highly dependent on the accuracy of static cost models.
Dynamic Scheduling: Scheduling decisions are made at runtime, just before a task is ready to execute. As tasks complete, the scheduler examines ready tasks (those whose dependencies are met) and assigns them to available devices based on current system state and scheduling policies.
- Pros: Adapts to real-time conditions, dynamic inputs, and actual task durations; can achieve better load balancing.
- Cons: Introduces runtime overhead for scheduling decisions; requires fast heuristics; may lead to suboptimal global schedules due to local decision-making. Common techniques include priority queues (prioritizing tasks on the critical path or those requiring specific accelerators) and work-stealing queues (where idle processors "steal" tasks from busy ones, useful for balancing load across similar devices).
Hybrid Scheduling: This approach combines static planning with dynamic adjustments. Large computational blocks or critical paths might be scheduled statically, while smaller tasks or adjustments based on runtime conditions (e.g., queue lengths, actual execution times) are handled dynamically. This aims to balance low overhead with adaptability.

The Central Role of Data Locality

Perhaps the most significant factor in heterogeneous scheduling is managing data movement. Transferring tensors between the host CPU's main memory and the accelerator's local memory (e.g., over PCIe) is often orders of magnitude slower than computation or on-device memory access. Effective schedulers prioritize minimizing or hiding this communication latency:

Task Co-location: Schedule consecutive operations that consume/produce the same tensor onto the same device to avoid unnecessary data transfers.
Communication Hiding: Overlap data transfers with computation on other devices using asynchronous memory copy operations and dedicated hardware copy engines (e.g., using CUDA streams or HIP streams). The scheduler explicitly manages dependencies between compute kernels and copy operations.
Memory Management Awareness: Consider device memory capacity constraints. Schedulers might need to orchestrate eviction and prefetching of tensors if the working set exceeds device memory, interacting closely with the runtime's memory manager. Techniques like using pinned host memory for faster DMA transfers or leveraging Unified Virtual Memory (UVM) can simplify programming but require careful performance tuning by the scheduler.

Device Affinity and Placement Heuristics

Deciding which type of device (CPU, GPU, other accelerator) is most suitable for a given task involves heuristics based on:

Operation Type: Large, parallelizable operations like matrix multiplications or convolutions are typically assigned to GPUs or accelerators. Control flow, irregular memory access patterns, or operations not well-supported by accelerators might run on the CPU.
Data Size: Very small operations might incur more overhead from launching on an accelerator and transferring data than executing directly on the CPU.
Hardware Specialization: Tasks leveraging specific data types (e.g., INT8, FP8) or operations (e.g., matrix multiplies) should be scheduled on devices with dedicated hardware units (like Tensor Cores) for maximum performance.
Current Load: Dynamic schedulers consider the current utilization and task queues of each device to balance the load.

Synchronization Mechanisms

Executing a DAG across multiple devices requires synchronization. When a task on device B depends on the output of a task on device A, the scheduler must ensure task A completes before task B starts. This is typically managed using lightweight synchronization primitives provided by the hardware/driver APIs (e.g., CUDA events, HIP events, fences). The scheduler inserts and waits on these events. Cross-device synchronization introduces latency, so minimizing synchronization points is another optimization goal, often achieved through careful task grouping and scheduling.

Advanced Scheduling Considerations

Beyond these core concepts, advanced runtimes incorporate further sophistication:

Multi-Device Scheduling: Scaling to systems with multiple GPUs or accelerators introduces topology awareness. The scheduler must consider the bandwidth and latency of inter-device links (e.g., NVLink vs. PCIe) when distributing communicating tasks.
Power-Aware Scheduling: Incorporating power consumption into the cost model allows scheduling for energy efficiency, potentially by choosing less powerful devices for non-critical tasks or adjusting device frequencies (DVFS).
Interference and OS Interaction: Runtime schedulers must be aware of potential interference from other applications or system processes and may need to interact with or adapt to OS-level scheduling decisions.
Predictive Modeling: Some runtimes employ machine learning models trained on execution traces to predict task durations and communication costs more accurately than static heuristics, leading to better dynamic scheduling decisions.

Designing an effective scheduler for heterogeneous systems remains an active area of research and engineering. It requires a deep understanding of both the ML workload characteristics and the underlying hardware capabilities, carefully balancing numerous trade-offs to deliver optimal performance and efficiency for complex AI applications.