Raffaele Montella

A GPGPU transparent virtualization component for high performance computing clouds

The GPU Virtualization Service (gVirtuS) presented in this work tries to fill the gap between in-house hosted computing clusters, equipped with GPGPUs devices, and pay-for-use high performance virtual clusters deployed via public or private computing clouds. gVirtuS allows an instanced virtual machine to access GPGPUs in a transparent and hypervisor independent way, with an overhead slightly greater than a real machine/GPGPU setup. The performance of the components of gVirtuS is assessed through a suite of tests in different deployment scenarios, such as providing GPGPU power to cloud computing based HPC clusters and sharing remotely hosted GPGPUs among HPC nodes.

pPOM: A nested, scalable, parallel and Fortran 90 implementation of the Princeton Ocean Model

Abstract In this work we describe the development of a parallel implementation of the Princeton Ocean Model (POM2k) with a nested-domain feature. Parallelization has been handled using the Run-Time System Library (RSL) and the Fortran Loop Index Converter (FLIC), avoiding a direct use of the MPI library. Modularity and flexibility have been added through advanced Fortran 90 features, such as modules, dynamic memory allocation, pointers and recursion. The “seamount problem”, either in a nested and non-nested configuration, is used as a test bed for showing results scalability.

A GPU Accelerated High Performance Cloud Computing Infrastructure for Grid Computing Based Virtual Environmental Laboratory

Numerical models play a main role in the earth sciences, filling in the gap between experimental and theoretical approach. Nowadays, the computational approach is widely recognized as the complement to the scientific analysis. Meanwhile, the huge amount of observed/modelled data, and the need to store, process, and refine them, often makes the use of high performance parallel computing the only effective solution to ensure the effective usability of numerical applications, as in the field of atmospheric /oceanographic science, where the development of the Earth Simulator supercomputer [65] is just the edge. Grid Computing [38] is a key technology in all the computational sciences, allowing the use of inhomogeneous and geographically spread computational resources, shared across a virtual laboratory. Moreover, this technology offers several invaluable tools in ensuring security, performance, and availability of the applications. A large amount of simulation models have been successfully developed in the past, but a lot of them are poorly engineered and have been designed following a monolithic programming approach, unsuitable for a distributed computing environment or to be accelerated by GPGPUs [53]. The use of the grid computing technologies is often limited to computer science specialists, because of the complexity of grid itself and of its middleware. Another source of complexity resides on the use of coupled models, as, for example, in the case of atmosphere/seawave/ocean dynamics. The grid enabling approach could be hampered by the grid software and hardware infrastructure complexity. In this context, the build-up of a grid-aware virtual laboratory for environmental applications is a topical challenge for computer scientists. The term “e-Science” is usually referred to computationally enhanced science. With the rise of cloud computing technology and on-demand resource allocation, the meaning of eScience could straightforwardly change to elastic-Science. The aim of our virtual laboratory is to bridge the gap between the technology push of the high performance cloud computing and the pull of a wide range of scientific experimental applications. It provides generic functionalities supporting a wide class of specific e-Science application environments and

Virtualizing high-end GPGPUs on ARM clusters for the next generation of high performance cloud computing

High performance cloud computing is behind the scene powering “the next big thing” as the mainstream accelerator for innovation in many areas. We describe here how to accelerate inexpensive ARM-based computing nodes with high-end GPGPUs hosted on x86_64 machines using the GVirtuS general-purpose virtualization service. We draw the vision of a possible next generation computing clusters characterized by highly heterogeneous parallelism heading to a lower electric power demanding, less heat producing and more environmental friendliness. Preliminary but promising performance data suggest that this solution could be considered as part of the foundations of the next generation of high performance cloud computing components.

Virtualizing CUDA Enabled GPGPUs on ARM Clusters

The acceleration of inexpensive ARM-based computing nodes with high-end CUDA enabled GPGPUs hosted on x86 64 machines using the GVirtuS general-purpose virtualization service is a novel approach to hierarchical parallelism. In this paper we draw the vision of a possible hierarchical remote workload distribution among different devices. Preliminary, but promising, performance evaluation data suggests that the developed technology is suitable for real world applications.

FACE-IT: A science gateway for food security research: FACE-IT: A SCIENCE GATEWAY FOR FOOD SECURITY RESEARCH

Progress in sustainability science is hindered by challenges in creating and managing complex data acquisition, processing, simulation, post‐processing, and intercomparison pipelines. To address these challenges, we developed the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE‐IT) for crop and climate impact assessments. This integrated data processing and simulation framework enables data ingest from geospatial archives; data regridding, aggregation, and other processing prior to simulation; large‐scale climate impact simulations with agricultural and other models, leveraging high‐performance and cloud computing; and post‐processing to produce aggregated yields and ensemble variables needed for statistics, for model intercomparison, and to connect biophysical models to global and regional economic models. FACE‐IT leverages the capabilities of the Globus Galaxies platform to enable the capture of workflows and outputs in well‐defined, reusable, and comparable forms. We describe FACE‐IT and applications within the Agricultural Model Intercomparison and Improvement Project and the Center for Robust Decision‐making on Climate and Energy Policy. Copyright

On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework

The astonishing development of diverse and different hardware platforms is twofold: on one side, the challenge for the exascale performance for big data processing and management; on the other side, the mobile and embedded devices for data collection and human machine interaction. This drove to a highly hierarchical evolution of programming models. GVirtuS is the general virtualization system developed in 2009 and firstly introduced in 2010 enabling a completely transparent layer among GPUs and VMs. This paper shows the latest achievements and developments of GVirtuS, now supporting CUDA 6.5, memory management and scheduling. Thanks to the new and improved remoting capabilities, GVirtus now enables GPU sharing among physical and virtual machines based on x86 and ARM CPUs on local workstations, computing clusters and distributed cloud appliances.

Using Hybrid Grid/Cloud Computing Technologies for Environmental Data Elastic Storage, Processing, and Provisioning

High-resolution climate and weather forecast models, and regional and global sensor networks, are producing ever-larger quantities of multidimensional environmental data. To be useful, this data must be stored, managed, and made available to a global community of researchers, policymakers, and others.

A globus toolkit 4 based instrument service for environmental data acquisition and distribution

In this paper we describe our experiences in the field of integration of geographically distributed instruments in a grid environment based on web services provided by the Globus Toolkit 4. Our first goal is to develop a common instrument interface system, based on standard interoperable components, in order to share data acquisition resources on the grid in a secure and homogeneous way and then distribute data in a grid aware content network fashion. We rely on previous developed components as our Job Flow Scheduler Service, the Resource Broker Service and the GrADS Data Service to achieve the result of a self advertised set of atmospheric and ocean data acquisition instruments as weather stations, marine surface current high frequency radars and wind profilers. We provide an example based on our grid aware virtual laboratory for environmental modeling showing how the instrument service integrates into the system, performing on line analysis and assessments on operational weather forecast model behavior. The software components we describe in this work, leveraging on the content distribution network approach, contribute to the grand challenge in the search of grid computing killer applications in the field of applied computational environmental sciences.

The High Performance Internet of Things: Using GVirtuS to Share High-End GPUs with ARM Based Cluster Computing Nodes

The availability of computing resources and the need for high quality services are rapidly evolving the vision about the acceleration of knowledge development, improvement and dissemination. The Internet of Things is growing up. The high performance cloud computing is behind the scene powering the next big thing. In this paper, using the GVirtuS, general purpose virtualization service, we demonstrate the feasibility of accelerate inexpensive ARM based computing nodes with high-end GPUs hosted on \(\mathrm{x}86\_64\) machines. We draw the vision of a possible next generation of low-cost, off the shelf, computing clusters we call Neowulf characterized by high heterogenic parallelism and expected as low electric power demanding and head producing.