Session

DescriptionWith a diversity of accelerators commercially available (FPGA, GPU, ASIC, … ) and others to come, it seems evident that high energy physics computation frameworks should support heterogeneous architectures in a way that should be transparent to the programming user. Experiment software has been developed over several decades and it will be extremely intensive to perform multiple porting to targeted architectures. This would not even be viable should the program need to be executed over heterogeneous resources. High level programing languages like OpenACC, OpenMP, High-Level Synthesis or TensorFlow are abstracting the algorithmic part and the execution part of a software, making it possible to run seamlessly over multiple platforms. Memory management is becoming the energy consumption bottleneck and dedicated data-driven computation frameworks help in keeping the overhead low. We propose to review the various options of architecture-agnostic programming frameworks and discuss the challenges of the intensive high energy physics data pipeline.