We are a world-class team of researchers and industry partners, working to build solutions to today’s most pressing Big Data challenges.

By revolutionising the way heterogeneous compute resources are exploited in the cloud, we will bring disruptive increases in efficiency and reductions in cost to anyone who wants to leverage data at scale.

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End to end solutions

In the current paradigm, when a company needs to run a critical component of their business logic at optimal speed in the cloud, they have three options:

  1. Scale-up (by upgrading processors at the node level)
  2. Scale-out (by adding nodes)
  3. Manually implement code optimisations specific to the underlying hardware.

E2Data proposes an end-to-end solution for Big Data deployments that will fully exploit and advance the state-of-the-art in infrastructure services by delivering more performance from fewer resources. The E2Data stack will achieve this by dynamically profiling, compiling and optimising code for execution on chosen devices, such as CPUs, GPUs, FPGAs, and others. By removing the need for developers to craft specific device code in languages like CUDA or OpenCL, E2Data will create substantial savings in developer time, while still exploiting the power of diverse device architectures, such as are currently offered by Microsoft, Amazon and others.

 

Over the course of the project, testing will be conducted on both high-performing x86 and low-power ARM cluster architectures representing realistic execution scenarios in four resource-demanding applications : finance, healthcare, green architecture, and security.

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Blog

Optimizing and Scheduling Workflows over Heterogeneous Infrastructures

In recent years, popular, resource-intensive tasks in the Machine Learning, A

Showcasing the Potential of GPU Acceleration in Data Analytics

by Clemens Lutz, DFKI

Today, businesses and scientists rely on insigh

This project has received funding from the European Union's Horizon H2020 research and innovation programme under grant agreement No 780245.