What is Hyperloom?

The system operates through a sophisticated multi-stage pipeline. First TraceLens, the profiling brain of the workload understanding stage, consumes traces collected by Magpie (which in turn relies on IntelliKit for some low-level GPU profiling tools), captures bottlenecks, and derives the roofline targets that seed the optimization search tree.

Next, Hyperloom employs a self-evolving code optimization engine following an iterative agentic loop (Think → Decide → Implement → Benchmark). Arbor intelligently explores the optimization space using a Dynamic Specialist Agent and Knowledge Base. In parallel to Arbor, GEAK, a multi-agent GPU performance optimizer, optimizes “hot” kernels (kernels that have been identified as good candidates for optimization). Once optimizations are identified and validated, Hyperloom prepares the optimized code and generates a report with all proposed changes and expected performance improvements. This end-to-end automation enables developers to achieve significant performance improvements while maintaining code quality and reducing the manual effort traditionally required for GPU optimization.

Provide your workload, and the agent works toward an optimized configuration: profiling against peak hardware potential, identifying bottlenecks, and iteratively rewriting code to maximize throughput on AMD GPUs.

The optimization loop

The following diagram describes how Hyperloom processes a workload from submission to validated delivery.

Hyperloom combines:

• Trace analysis, identifying kernel bottlenecks and bridge planning through TraceLens Agent (backend support from Magpie and Intellikit)

• Kernel optimization through the GEAK backend.

• Agentic search space exploration through Arbor, a tree-based cognition layer with dynamic agents, long-horizon campaigns, and self-evolving optimization guided by a curated knowledge base of hardware learnings, pitfalls, and prior campaign artifacts.