CUDACPP gives MadGraph data-parallel helicity amplitudes, delivering linear SIMD CPU speed-ups and up to order-of-magnitude GPU speed-ups for high-multiplicity QCD event generation.
Enhancing software-hardware co-design for HEP by low-overhead profiling of single- and multi-threaded programs on diverse architectures with Adaptyst
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abstract
Given the recent technological trends and novel computing paradigms spanning both software and hardware, physicists and software developers can no longer just rely on computers becoming faster to meet the ever-increasing computing demands of their research. Adapting systems to the new environment may be difficult though, especially in case of large and complex applications. Therefore, we introduce Adaptyst (formerly AdaptivePerf): an open-source and architecture-agnostic tool aiming for making these computational and procurement challenges easier to address. At the moment, Adaptyst profiles on- and off-CPU activity of codes, traces all threads and processes spawned by them, and analyses low-level software-hardware interactions to the extent supported by hardware. The tool addresses the main shortcomings of Linux "perf" and has been successfully tested on x86-64, arm64, and RISC-V instruction set architectures. Adaptyst is planned to be evolved towards a software-hardware co-design framework which scales from embedded to high-performance computing in both legacy and new applications and takes into account a bigger picture than merely choosing between CPUs and GPUs. Our paper describes the current development of the project and its roadmap.
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Data-parallel leading-order event generation in MadGraph5_aMC@NLO
CUDACPP gives MadGraph data-parallel helicity amplitudes, delivering linear SIMD CPU speed-ups and up to order-of-magnitude GPU speed-ups for high-multiplicity QCD event generation.