Software

AMD Introduces ROCm Hyperloom for Automated Inference Optimization

AMD announced ROCm Hyperloom, an open-source tool that reduces inference workload optimization time from weeks to hours, supporting AMD Instinct GPUs.

Image: AMD

AMD has launched ROCm Hyperloom, an open-source, agentic system designed to automate the optimization of end-to-end inference workloads. Using Hyperloom, the time required to optimize a workload can be reduced from weeks to hours, significantly improving efficiency. The tool combines various components into an autonomous optimization loop, allowing users to achieve optimal performance from their models and custom configurations on AMD Instinct GPUs. The system operates as a multi-agent architecture, fully automating the process of profiling, analyzing, and tuning models. All users need to do is provide their workload and configuration files, after which Hyperloom handles the rest. The tool is intended for developers and researchers looking to streamline their inference optimization workflows. Source: amd

Hyperloom addresses the challenges of traditional inference optimization by eliminating the need for manual intervention. The standard workflow requires specialized skills to manually execute each step, including profiling, analyzing, identifying potential optimizations, implementing changes, testing for correctness, benchmarking, and deciding whether to keep or discard changes. A single optimization cycle can take weeks of effort and often results in non-transferable optimizations. Hyperloom reduces this time to hours by systematically exploring the solution space to identify potential optimizations, including obscure and unintuitive fixes, while running correctness checks on every candidate before acceptance. The tool also enables the exploration of cross-repo patches that an engineer would be unlikely to attempt. Source: amd

The Hyperloom architecture includes a closed-loop system that iteratively improves the performance of inference workloads. This system runs as a multi-agent architecture, fully automating the process of profiling, analyzing, and tuning models. The workflow is broken down into distinct stages, with each stage involving specific components that contribute to the overall optimization. First, the TraceLens-Agent identifies bottlenecks and potential optimizations using TraceLens, which leverages Magpie and IntelliKit for trace collection and low-level GPU profiling. Next, the self-evolving optimization engine employs GEAK and Arbor in parallel to improve the performance of individual kernels as well as the full end-to-end workload. Source: amd

Key points

AMD announced ROCm Hyperloom, an open-source agentic system for automating end-to-end inference workload optimization.
Hyperloom reduces the time to optimize an end-to-end workload from weeks to hours, saving time and enabling resource allocation to other tasks.
Hyperloom combines various tools into an autonomous optimization loop to achieve the best performance from models and custom configurations on AMD Instinct GPUs.
The standard inference optimization workflow requires specialized skills to manually execute every step, often taking weeks to complete.
Hyperloom systematically explores the solution space to identify potential optimizations, including obscure and unintuitive fixes, while running correctness checks on every candidate before acceptance.
Hyperloom operates as a multi-agent architecture, fully automating the process of profiling, analyzing, and tuning models.
The Hyperloom architecture includes a closed-loop system that iteratively improves the performance of inference workloads.

Source: AMD Read the original →

WRITTEN BY

Theo Almeida

AI Software & Developer Tools

Theo covers AI software, developer tools, frameworks, and the platforms builders use every day.