Runtime Scheduler and Heterogeneous Compute Management

In the EvoX architecture,the Runtime scheduleris the execution hub that connects algorithm logic with physical hardware. It maps upper-layer evolutionary algorithms, problem definitions, and workflows onto heterogeneous compute resources such as GPUs, accelerators (e.g., TPU/NPU), and distributed clusters, abstracting away hardware differences while maintaining the efficient composability of algorithm modules.

Core Responsibilities

Heterogeneous compute scheduling: Uniformly manages execution resources such as GPUs, accelerators, and distributed clusters, assigning tasks, operators, and tensor computation graphs in evolutionary computation to the most suitable hardware backend.
Dependency and state coordination: Coordinates dependencies and execution states among tasks, ensuring that every stage of parallel evolutionary exploration is properly synchronized.
Unified scheduling interface: The algorithm layer submits compute tasks through a unified interface without being tied to a specific device or cluster form, thereby reducing hardware coupling.
Module composability: Supports modular composition, replacement, and reuse of algorithms, problems, operators, and execution backends, so the same codebase can easily migrate to different hardware environments.

Value and Examples

The core value of the Runtime scheduler lies in enabling parallel evolutionary exploration to run efficiently across hardware without compromising the modular design of upper-layer algorithms. For example, when the same evolutionary algorithm switches from a single-machine GPU to a multi-machine cluster, the algorithm-layer code remains unchanged while the Runtime re-maps the compute tasks and execution backends. This design lets researchers focus on algorithm innovation without rewriting code for each type of hardware.

The Value of a Unified Interface

The unified scheduling interface is key to how the Runtime reduces hardware coupling: the algorithm layer only needs to describe "what to do" without worrying about "which hardware to do it on." This allows the same evolutionary algorithm to migrate seamlessly between single-machine, multi-GPU, and even cross-cluster environments.

Architecture diagram of the Runtime scheduler connecting hardware devices with upper-layer algorithms

The Runtime scheduler sits between algorithm logic and physical hardware. Through a unified scheduling interface, it maps evolutionary tasks, operators, and tensor computation graphs onto GPUs, accelerators, or distributed clusters, while coordinating task dependencies and execution states. The algorithm layer therefore only needs to describe what to do without being tied to a device form, and the same codebase does not need to be rewritten when migrating from a single-machine GPU to a multi-machine cluster.

Question 1

What is the primary role of the Runtime scheduler in EvoX?

Defining the objective function of the problem

Providing a user interface

Implementing the core logic of the evolutionary algorithm

Mapping algorithms and workflows onto heterogeneous compute resources and abstracting away hardware differences

Question 2

Which of the following is NOT an execution resource managed by the Runtime scheduler?

GPU

The source code of algorithm modules

Accelerators (TPU/NPU)

Distributed Cluster

Question 3

How does the Runtime help the algorithm layer reduce hardware coupling?

Providing a different algorithm implementation for each type of hardware

Requiring the algorithm layer to manage task dependencies on its own

Forcing the algorithm layer to use specific hardware

Through a unified scheduling interface, the algorithm layer does not need to be tied to a specific device or cluster form

Question 4

In terms of preserving algorithm composability, which of the following operations does the Runtime support?

Modular composition, replacement, and reuse of algorithms, problems, operators, and execution backends

The algorithm layer must be rewritten for each type of hardware

Only a single hardware backend can be used

Replacing the execution backend is prohibited

Question 5

When the same evolutionary algorithm switches from a single-machine GPU to a multi-machine cluster, what does the Runtime do?

Refuses to execute

Requires the algorithm layer to be recompiled

Re-maps compute tasks and execution backends while the algorithm-layer code remains unchanged

Automatically modifies the algorithm logic

Case Study: Heterogeneous Scheduling for Multi-Objective Airfoil Design

Read the scenario and answer the questions.

A research team uses EvoX for multi-objective optimization of a supercritical airfoil, aiming to simultaneously optimize the lift coefficient and cruise lift-to-drag ratio. The task must run on a heterogeneous cluster: some computations are performed on a single-machine GPU, while others are executed on a distributed cluster. The team wants the same algorithm code to migrate seamlessly across different hardware configurations.

How does the Runtime scheduler help the algorithm layer migrate between different hardware backends?

Answer:
Through its unified scheduling interface, the Runtime maps the tasks, operators, and computation graphs submitted by the algorithm layer to specific hardware backends. When the hardware environment changes, the algorithm-layer code remains unchanged while the Runtime re-maps the compute tasks and execution backends. For example, when switching from a single-machine GPU to a multi-machine cluster, the Runtime splits and distributes the computation graph across cluster nodes while coordinating task dependencies and state synchronization.

In this multi-objective optimization scenario, what factors does the Runtime scheduler need to consider when scheduling tasks?

Answer:
The Runtime needs to: 1) identify the computational characteristics and dependencies of tasks; 2) allocate suitable backends based on hardware capabilities (such as GPU parallelism and cluster communication overhead); 3) coordinate the parallel execution of multiple objective evaluations to ensure task synchronization under sparse feedback; and 4) manage status feedback so that the algorithm layer can obtain the results of each candidate solution. Together, these factors ensure efficient deployment across hardware without compromising the composability of algorithm modules.