Magellan Final Report - Office of Science - U.S. Department of Energy

Magellan Final Report
at a virtual OpenCL (VOCL) framework that could support the transparent utilization of local or remote
GPUs. This framework, based on the OpenCL programming model, exposes physical GPUs as decoupled
virtual resources that can be transparently managed independent of the application execution. The performance
of VOCL for four real-world applications was evaluated as part of the project, looking at various
computation and memory access intensities. The work showed that compute-intensive applications can execute
with relatively small amounts of overhead within the VOCL framework.
Virtualization Overhead Benchmarking. The benchmarking of virtualization overheads using both
Eucalyptus and OpenStack was performed in collaboration with the Mathematics and Computer Science
Division (MCS) at Argonne, which does algorithm development and software design in core areas such as
optimization, explores new technologies such as distributed computing and bioinformatics, and performs
numerical simulations in challenging areas such as climate modeling, the Advanced Integration Group at
ALCF, which designs, develops, benchmarks, and deploys new technology and tools, and the Performance
Engineering Group at ALCF, which works to ensure the effective use of applications on ALCF systems and
emerging systems. This work is detailed in Chapter 9.
SuperNova Factory. Magellan project personnel were part of the team of researchers from LBNL who
received the Best Paper Award at ScienceCloud 2010. The paper describes the feasibility of porting the
Nearby Supernova Factory pipeline to the Amazon Web Services environment and offers detailed performance
results and lessons learned from various design options.
MOAB Provisioning. We worked closely with Adaptive Computing’s MOAB team to test both bare-metal
provisioning and virtual machine provisioning through the MOAB batch queue interface at NERSC. Our early
evaluation provides an alternate model for providing cloud services to HPC center users allowing them to
benefit from customized environments while leveraging many of the services they are already used to such as
high bandwidth low latency interconnects, access to high performance file systems, access to archival storage.
Juniper 10GigE. Recent cloud offerings such as Amazon’s Cluster Compute instances are based on 10GigE
networking infrastructure. The Magellan team at NERSC worked closely with Juniper to evaluate their
10GigE infrastructure on a subset of the Magellan testbed. Detailed benchmarking evaluation using both
bare-metal and virtualization was performed and is detailed in Chapter 9.
IBM GPFS-SNC. Hadoop and Hadoop Distributed File System show the importance of data locality in
file systems when handling workloads with large data volumes. However HDFS does not have a POSIX
interface which is a significant challenge for legacy scientific applications. Alternate storage architecture
implementations such as IBM’s General Parallel File System - Shared Nothing Cluster (GPFS-SNC), a distributed
shared-nothing file system architecture, provides many of the features of HDFS such as data locality
and data replication while preserving the POSIX IO interface. The Magellan team at NERSC worked closely
with the IBM Almaden research team to install and test an early version of GPFS-SNC on Magellan hardware.
Storage architectures such as GPFS-SNC hold promise for scientific applications but a more detailed
benchmarking effort will be needed which is outside of the scope of Magellan.
User Education and Support. User education and support have been critical to the success of the
project. Both sites were actively involved in providing user education at workshops and through other
forums. Additionally, Magellan project personnel engaged heavily with user groups to help them in their
evaluation of the cloud infrastructure. In addition, the NERSC project team did an initial requirements
gathering survey. At the end of the project, user experiences from both sites were gathered through a survey
and case studies, that are described in Chapter 11.
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