Ponente
Descripción
The generation of hard-scattering events in high-energy physics, such as the process $gg \to t\bar{t}g$, is one of the computational bottlenecks in collider phenomenology. MadGraph provides a flexible framework to evaluate these matrix elements, but the sheer scale of Monte Carlo event production required at the LHC drives both execution time and power consumption to critical levels. In this work, we explore the use of Adaptive Compute Acceleration Platforms (ACAPs) and, in particular, their AI Engine (AIE) cores to accelerate the evaluation of matrix elements for the $gg \to t\bar{t}g$ process. We design and map the helicity-amplitude and color-summation structure of the computation onto clusters of AIE cores, exploiting both vectorized arithmetic and dataflow pipelining across tiles. Preliminary results indicate that the AIE-based implementation can significantly reduce latency while offering superior power efficiency compared to CPU and GPU architectures. While the complexity of multi-leg processes presents challenges for full FPGA acceleration, our study demonstrates the viability of AIE-based event generation as a scalable approach for next-generation Monte Carlo simulations at the LHC.
