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We implement and benchmark parallel I/O methods for the fully-manycore drivenparticle-in-cell code PIConGPU. Identifying throughput and overall I/O size asa major challenge for applications on today's and future HPC systems, wepresent a scaling law characterizing performance bottlenecks instate-of-the-art approaches for data reduction. Consequently, we propose,implement and verify multi-threaded data-transformations for the I/O libraryADIOS as a feasible way to trade underutilized host-side compute potential onheterogeneous systems for reduced I/O latency.

On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective