Modern GPU applications, such as machine learning (ML), can only partially utilize GPUs, leading to GPU underutilization in cloud environments. Sharing GPUs across multiple applications from different tenants can improve resource utilization and consequently cost, energy, and power efficiency. However, GPU sharing creates memory safety concerns because kernels must share a single GPU address space. Existing spatial-sharing mechanisms either lack fault isolation for memory accesses or require static partitioning, which leads to limited deployability or low utilization.

Today, using multiple heterogeneous accelerators efficiently from applications and high-level frameworks, such as Tensor-Flow and Caffe, poses significant challenges in three respects: (a) sharing accelerators, (b) allocating available resources elastically during application execution, and (c) reducing the required programming effort.

EVOLVE is a pan European Innovation Action that aims to fully-integrate High-Performance-Computing (HPC) hardware with state-of-the-art software technologies under a unique testbed, that enables the convergence of HPC, Cloud and Big-Data worlds and increases our ability to extract value from massive and demanding datasets. EVOLVE's advanced compute platform combines HPC-enabled capabilities, with transparent deployment in high abstraction level, and a versatile Big-Data processing stack for end-to-end workflows. Hence, domain experts have the potential to improve substantially the efficiency of existing services or introduce new models in the respective domains, e.g., automotive services, bus transportation, maritime surveillance and others. In this paper, we describe EVOLVE's testbed, and evaluate the performance of the integrated pilots from different domains.

EVOLVE is a pan-European Innovation Action building a converged infrastructure to bring together the HPC, Cloud, and Big Data worlds. EVOLVE's platform and software stack supports large-scale, data-intensive applications, driven primarily by industry requirements set by pilot and proof-of-concept use cases from diverse fields. Given the unprecedented data growth we are experiencing, EVOLVE's infrastructure is key in enabling the cost-effective processing of massive amounts of data and the adaptation of multiple high-end technologies, in an environment that fosters interoperability and enforces increased security.

The EVOLVE project aims to take important steps in bringing together Big Data, HPC and Cloud domains in a single testbed and expose its services through a user friendly and transparent interface. The EVOLVE testbed is enhanced with acceleration capabilities by leveraging the power of heterogeneous technologies and allows the user to develop and deploy applications through Zeppelin notebooks with ease of use.

Future high-throughput grids may integrate millions or even billions of processing and data storage nodes. Services provided by the underlying grid infrastructure may have to be able to scale to capacities not even imaginable today. In this paper we concentrate on one of the core components of the data grid architecture - the replica location service - and evaluate a redesign of the system based on a structured peer-to-peer network overlay. We argue that the architecture of the currently most widespread solution for file replica location on the grid is biased towards high-performance deployments and cannot scale to the future needs of a global grid. Structured peer-to-peer systems can provide the same functionality, while being much more manageable, scalable and fault-tolerant. However, they are only capable of storing read-only data. To this end, we propose a revised protocol for distributed hash tables that allows data to be changed in a distributed and scalable fashion. Results from a prototype implementation of the system suggest that grids can truly benefit from the scalability and fault-tolerance properties of such peer-to-peer algorithms.