Free-electron lasers generate high-brilliance coherent radiation at wavelengths spanning from the infrared to the X-ray domains. The recent development of short-wavelength seeded free-electron lasers now allows for unprecedented levels of control on longitudinal coherence, opening new scientific avenues such as ultra-fast dynamics on complex systems and X-ray nonlinear optics. Although those devices rely on state-of-the-art large-scale accelerators, advancements on laser-plasma accelerators, which harness gigavolt-per-centimetre accelerating fields, showcase a promising technology as compact drivers for free-electron lasers. Using such footprint-reduced accelerators, exponential amplification of a shot-noise type of radiation in a self-amplified spontaneous emission configuration was recently achieved. However, employing this compact approach for the delivery of temporally coherent pulses in a controlled manner has remained a major challenge. Here we present the experimental demonstration of a laser-plasma accelerator-driven free-electron laser in a seeded configuration, where control over the radiation wavelength is accomplished. Furthermore, the appearance of interference fringes, resulting from the interaction between the phase-locked emitted radiation and the seed, confirms longitudinal coherence. Building on our scientific achievements, we anticipate a navigable pathway to extreme-ultraviolet wavelengths, paving the way towards smaller-scale free-electron lasers, unique tools for a multitude of applications in industry, laboratories and universities.

Due to the recent announcement of the Frontier supercomputer, many scientific application developers are working to make their applications compatible with AMD (CPU-GPU) architectures, which means moving away from the traditional CPU and NVIDIA-GPU systems. Due to the current limitations of profiling tools for AMD GPUs, this shift leaves a void in how to measure application performance on AMD GPUs. In this article, we design an instruction roofline model for AMD GPUs using AMD’s ROCProfiler and a benchmarking tool, BabelStream (the HIP implementation), as a way to measure an application’s performance in instructions and memory transactions on new AMD hardware. Specifically, we create instruction roofline models for a case study scientific application, PIConGPU, an open source particle-in-cell simulations application used for plasma and laser-plasma physics on the NVIDIA V100, AMD Radeon Instinct MI60, and AMD Instinct MI100 GPUs. When looking at the performance of multiple kernels of interest in PIConGPU we find that although the AMD MI100 GPU achieves a similar, or better, execution time compared to the NVIDIA V100 GPU, profiling tool differences make comparing performance of these two architectures hard. When looking at execution time, GIPS, and instruction intensity, the AMD MI60 achieves the worst performance out of the three GPUs used in this work.

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