Enabling extreme scale applications on heterogeneous hardware

We are working towards delivering a new validated programming environment, extending the programmability and maximizing the productivity of application development for large-scale heterogeneous computing systems.

EPiGRAM-HS is motivated by the increasing presence of heterogeneous technologies on pre-exascale supercomputers and by the need of porting key HPC and emerging applications to these systems on time for exascale.

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EPiGRAM-HS is consisted of 6 key European universities and enterprises focused on high performance computing.

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🤩 GPU MPI: running your MPI application on GPU! Harness the compute power of GPUs with small or no code changes! https://epigram-hs.eu/news-gpu-mpi/

Two more hours to our Advanced MPI programming tutorial at @Supercomputing #SC20. Join us at https://sc20.supercomputing.org/presentation/?id=tut110&sess=sess252 online - from anywhere on🌏.

With Pavan Balaji, Rajeev Thakur @argonne and Bill Gropp @NCSAatIllinois!

#HPC

Our @Supercomputing productive #FPGA programming with #HLS tutorial is in full swing. Johannes demonstrates techniques to massage C/C++ codes to perform well on FPGAs. You can still join - if you have to miss it, check out https://arxiv.org/abs/1805.08288 and https://arxiv.org/abs/2010.15218 #HPC

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Delivering a new validated programming environment for large-scale heterogeneous computing systems for enabling HPC and emerging deep-learning frameworks to run on large-scale heterogeneous systems at maximum performance.

Extending the programmability of large-scale heterogeneous systems with GPUs, FPGAs, HBM and NVM, by introducing new concepts, adding functionalities and carrying out implementations in two widely-used HPC programming systems for large-scale supercomputers (MPI and GASPI)

Maximizing the productivity of application development on heterogeneous supercomputers by providing auto-tuned collective communication, a framework for automatic code generation for FPGAs, a memory abstraction device comprised of APIs and a runtime for automatic data placement on diverse memories, and a DSL for large-scale deep-learning frameworks.