Speaker
Description
The first production release of the CUDACPP plugin for the Madgraph5_aMC@NLO generator, which speeds up matrix element (ME) calculations for leading-order (LO) processes using a data parallel approach on vector CPUs and GPUs, was delivered in October 2024. This was described at CHEP2024 and in other previous publications by the team behind that effort. In this CHEP2026 contribution, I present my work on some additional developments and optimizations of CUDACPP, mainly but not exclusively for GPUs. A detailed description of this work is available in https://arxiv.org/abs/2510.05392. The new approach, which represents a major restructuring of the CUDACPP computational engine, primarily consists in splitting the ME calculation, previously performed using a single large GPU kernel, into many smaller kernels. A first batch of changes, involving the move to separate “helicity streams” and the optional offloading of QCD color sums to BLAS, has already been merged into a new CUDACPP release, in collaboration with my colleagues. Since then, I have completed a second batch of changes, involving the possibility to split the calculation into groups of Feynman diagrams in separate source code files, which has also been submitted as a pull request. This new feature makes it possible to compute QCD matrix elements for physics processes with a larger number of final state gluons: in particular, I present the first performance results from CUDACPP for the $2\!\rightarrow\!6$ process $gg\!\rightarrow\!t\bar{t}gggg$ on CPUs and GPUs and the $2\!\rightarrow\!7$ process $gg\!\rightarrow\!t\bar{t}ggggg$ on CPUs, which involve over 15k and 230k Feynman diagrams, respectively.