Purdue University’s distributed-GPU simulator covers Ampere, Hopper, and Blackwell, achieving 99% Pearson correlation against physical H100 silicon.

Purdue University researchers published a technical paper titled “Architecting the Next Generation of Asynchronous, Distributed GPUs for the AI Era.” The paper presents a cycle-level simulation framework for distributed GPU systems and AI workloads.

The simulator covers NVIDIA’s Ampere, Hopper and Blackwell GPU generations. These are GPU microarchitecture families, rather than separate server, rack or cluster products.

The researchers reported validation against physical silicon, including an NVIDIA H100 GPU based on the Hopper architecture. The framework achieved a 99% Pearson correlation coefficient in that validation.

Pearson correlation measures how closely two sets of results vary together; it is not itself an absolute-error percentage or a direct performance-speedup figure. The provided source excerpt does not disclose the simulator’s runtime overhead, complete benchmark suite or absolute prediction error.