Evolutionary Autotuning: Scalable Optimization

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Evolutionary Autotuning: Scalable Optimization

Introduction

Hey guys! Let's dive into the exciting world of evolutionary and multi-objective autotuning. This article will break down Issue 22, which focuses on enhancing our job orchestrator to support more advanced tuning methods. We're talking about moving beyond simple grid searches to more sophisticated techniques that can optimize for multiple objectives like latency, throughput, and power. This is a game-changer for scaling our tuning capabilities and achieving better performance across the board.

Summary

The core idea is to supercharge our job orchestrator with evolutionary search and multi-objective optimization. Instead of just trying every possible combination of parameters, we'll use algorithms inspired by natural selection to find the best configurations. This means we can optimize for multiple goals simultaneously, like reducing latency while also improving throughput and minimizing power consumption. Think of it as leveling up our tuning game to handle more complex scenarios and deliver even better results. This approach promises to significantly enhance the efficiency and effectiveness of our autotuning processes, enabling us to achieve superior performance outcomes in a more streamlined and scalable manner.

Deliverables

To make this happen, we have a few key deliverables:

  • Candidate Encoding: We need a way to represent different sets of parameters and define how they can be mutated or combined. This will be implemented in uhop/autotune/candidate.py. Think of this as defining the "genes" of our configurations, allowing us to evolve them over time.
  • Evolutionary Driver: This is the engine that drives the evolutionary process. It will consume jobs from the queue, evolve populations of candidates, and respect constraints like timeouts and resource usage. You'll find this in uhop/autotune/evolution.py. This component ensures that the search for optimal configurations is both efficient and adheres to predefined boundaries, preventing excessive resource consumption.
  • Surrogate Model Integration: We'll use a surrogate model (from Issue 13) to predict the performance of candidates before we actually run them. This helps us prune the low-value candidates and focus on the ones that are most promising. This predictive capability significantly reduces the computational overhead by filtering out less promising candidates early in the process.
  • CLI Controls for Objectives and Weights: We'll add command-line options to specify the objectives we want to optimize (e.g., latency, throughput, power) and their relative weights. This will allow users to customize the tuning process to their specific needs. For example: python -m uhop.autotune.run --objectives latency throughput power --weights 0.6 0.3 0.1. This level of control empowers users to tailor the autotuning process to align with their specific performance goals and priorities.

Acceptance Criteria

How will we know if we've succeeded? Here are the acceptance criteria:

  • The evolutionary tuner must find configurations that outperform the baseline grid search by at least 15% in latency reduction on at least one benchmark. This demonstrates the effectiveness of the evolutionary approach compared to traditional methods.
  • The multi-objective mode should produce Pareto-optimal frontiers, which are logged to disk in JSON format for inspection. This allows us to visualize the trade-offs between different objectives and choose the best configuration for our needs. The Pareto-optimal frontiers provide a clear understanding of the performance trade-offs, facilitating informed decision-making.
  • We need unit and integration tests to ensure that mutation/crossover logic is correct, surrogate pruning is effective, and CLI arguments are validated properly. Rigorous testing is crucial to ensure the reliability and accuracy of the autotuning system.

Definition of Done

We'll consider this issue done when:

  • The evolutionary autotuner is fully integrated with the existing orchestrator and documented in docs/PRODUCTION_VISION.md (under the Scalable Autotuning pillar). Proper integration and documentation are essential for ensuring that the autotuner is accessible and usable by the broader team.
  • Follow-up issues are filed for GPU cluster execution and transfer learning across operations/hardware families. This ensures that we continue to improve and expand the capabilities of the autotuner in the future. These follow-up issues will drive further enhancements and broaden the applicability of the autotuning system.

Notes / Dependencies

Keep in mind:

  • This issue relies on Issue 17 (job orchestrator) and Issue 13 (performance predictor). We need these components in place before we can start working on this.
  • We should design the API to accommodate future reinforcement learning or Bayesian strategies without breaking changes. This will ensure that our autotuning system is flexible and can adapt to new techniques as they emerge. A well-designed API is critical for maintaining the long-term viability and adaptability of the autotuning system.

Deep Dive into Key Components

Candidate Encoding

The uhop/autotune/candidate.py module is pivotal in defining how different parameter sets are represented and manipulated within our evolutionary autotuning framework. Think of it as creating the genetic blueprint for each potential configuration. This module must elegantly capture the essence of each parameter, allowing for seamless mutation and crossover operations. The goal here is to ensure that we can efficiently explore the vast configuration space, identifying those sweet spots that lead to optimal performance.

To achieve this, we need a flexible and robust data structure that can accommodate various types of parameters, such as integers, floats, and categorical values. Each parameter should have well-defined boundaries and constraints, preventing the generation of invalid configurations. Furthermore, the mutation and crossover logic must be carefully crafted to ensure that the resulting candidates are both valid and potentially beneficial. For instance, we might use Gaussian mutations for continuous parameters and discrete mutations for categorical ones. Crossover operations could involve swapping parameter values between two parent candidates or combining them in a more sophisticated manner. The key is to strike a balance between exploration and exploitation, allowing the evolutionary algorithm to discover new and promising configurations while also refining existing ones.

Evolutionary Driver

The uhop/autotune/evolution.py module is the heart of our evolutionary autotuning system. It's responsible for orchestrating the entire evolutionary process, from initializing the population to selecting the best candidates for reproduction. This module needs to be highly efficient and scalable, capable of handling large populations and complex workloads. The evolutionary driver consumes jobs from the job queue, evaluates the performance of each candidate, and evolves the population based on their fitness scores. It also needs to respect constraints such as timeouts and resource usage, ensuring that the autotuning process doesn't consume excessive resources or run indefinitely.

The evolutionary driver typically follows a few main steps: (1) Initialization: Create an initial population of random candidates. (2) Evaluation: Evaluate the performance of each candidate by running it on the target system. (3) Selection: Select the best candidates based on their fitness scores. (4) Crossover: Combine the genetic material of the selected candidates to create new offspring. (5) Mutation: Introduce random changes to the offspring to increase diversity. (6) Replacement: Replace the worst candidates in the population with the new offspring. (7) Repeat: Repeat steps 2-6 until a satisfactory solution is found or a predefined stopping criterion is met.

Surrogate Model

The integration of a surrogate model, stemming from Issue 13, into our evolutionary autotuning framework is a game-changer. This predictive model acts as a virtual testing ground, allowing us to estimate the performance of candidate configurations without the need for costly real-world evaluations. By pruning low-value candidates early in the process, we can significantly reduce the computational overhead and focus our efforts on the most promising configurations. The surrogate model essentially learns from past evaluations, capturing the complex relationships between parameter settings and performance metrics.

To effectively leverage the surrogate model, we need to carefully consider its accuracy and computational cost. A highly accurate model will provide more reliable performance predictions, but it may also be more computationally expensive to train and evaluate. Conversely, a less accurate model will be faster to use but may lead to suboptimal pruning decisions. The choice of surrogate model should be based on a trade-off between accuracy and efficiency, taking into account the specific characteristics of the target system and workload.

CLI Controls for Objectives and Weights

Providing command-line controls for objectives and weights empowers users to tailor the autotuning process to their specific needs and priorities. This flexibility is crucial for accommodating diverse use cases and scenarios, where different objectives may have varying degrees of importance. By allowing users to specify the objectives they want to optimize and their relative weights, we enable them to fine-tune the autotuning process to achieve their desired performance outcomes.

For example, a user might want to prioritize latency reduction over power consumption in a latency-critical application. In this case, they could assign a higher weight to the latency objective and a lower weight to the power objective. Conversely, a user might want to minimize power consumption in a power-constrained environment. In this case, they could assign a higher weight to the power objective and a lower weight to other objectives. The command-line interface should provide a clear and intuitive way for users to specify these objectives and weights, ensuring that they can easily customize the autotuning process to their specific requirements.

Conclusion

Implementing evolutionary and multi-objective autotuning is a significant step forward in our quest for scalable and efficient performance optimization. By leveraging the power of evolutionary algorithms and surrogate models, we can overcome the limitations of traditional grid searches and achieve superior performance outcomes. The deliverables outlined in this article, along with the acceptance criteria and definition of done, provide a clear roadmap for achieving this goal. As we move forward, we must continue to focus on innovation and collaboration, ensuring that our autotuning system remains at the cutting edge of technology. So, buckle up, guys! It's going to be an exciting ride as we delve deeper into the world of autotuning and unlock the full potential of our systems.