Enabling extreme scale applications on heterogeneous hardware

EPiGRAM-HS Newsletter #1

EPiGRAM-HS' first newsletter goes through the first three months of the project. Over this period EPiGRAM-HS has managed to lay...
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Hardware-friendly DL by Bengio

1) Quantized neural networks to reduce memory footprint 2) Equilibrium propagation for analog hardware 3) Culture evolution method to reduce...
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Reconsidering the I/O stack

A new paper in ACM communication urge for a reconsideration of the I/O software stack. Check out the article here:...
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EPiGRAM-HS researcher Torsten Hoefler featured in Inside HPC

https://www.youtube.com/watch?v=HwEpXIWAWTU Torsten Hoefler from ETH Zurich and member of EPiGRAM-HS has been featured in InsideHPC to present scientific practices for...
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SC18 – EPiGRAM-HS presence

EPiGRAM-HS is following supercomputing proceedings and events around the world closely and is proudly taking part in SC18 in Dallas November 11-16th,...
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Jeffrey Vetter – Exploring Emerging Memory Technologies in Extreme Scale High Performance Computing

On Monday 8th of October EPiGRAM-HS had the honour of sponsoring Jeffrey Vetter’s talk on “Exploring Emerging Memory Technologies in...
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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.

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.

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Hardware-friendly DL by Bengio: 1) quantized neural networks to reduce memory footprint 2) Equilibrium propagation for analog hardware 3) Culture evolution method to reduce sync and comm wrt SGD https://t.co/zekWpfXPIb

Reconsidering the I/O software stack: bringing both device control and data planes into user space by kernel-bypass seems the thing to do. + Important points on the integration of NVDIMMs.

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


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.