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

Key Findings
The goal of the Magellan project is to determine the appropriate role of cloud computing in addressing
the computing needs of scientists funded by the DOE Office of Science. During the course of the Magellan
project, we have evaluated various aspects of cloud computing infrastructure and technologies for use
by scientific applications from various domains. Our evaluation methodology covered various dimensions:
cloud models such as Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), virtual software
stacks, MapReduce and its open source implementation (Hadoop), resource provider and user perspectives.
Specifically, Magellan was charged with answering the following research questions:
• Are the open source cloud software stacks ready for DOE HPC science
• Can DOE cyber security requirements be met within a cloud
• Are the new cloud programming models useful for scientific computing
• Can DOE HPC applications run efficiently in the cloud What applications are suitable for clouds
• How usable are cloud environments for scientific applications
• When is it cost effective to run DOE HPC science in a cloud
We summarize our findings here:
Finding 1. Scientific applications have special requirements that require solutions that are
tailored to these needs.
Cloud computing has developed in the context of enterprise and web applications that have vastly different
requirements compared to scientific applications. Scientific applications often rely on access to large
legacy data sets and pre-tuned application software libraries. These applications today run in HPC centers
with low-latency interconnects and rely on parallel file systems. While these applications could benefit from
cloud features such as customized environments and rapid elasticity, these need to be in concert with other
capabilities that are currently available to them in supercomputing centers. In addition, the cost model for
scientific users is based on account allocations rather than a fungible commodity such as dollars; and the
business model of scientific processes leads to an open-ended need for resources. These differences in cost
and the business model for scientific computing necessitates a different approach compared to the enterprise
model that cloud services cater to today. Coupled with the unique software and specialized hardware requirements,
this points to the need for clouds designed and operated specifically for scientific users. These
requirements could be met at current DOE HPC centers. Private science clouds should be considered only
if it is found that requirements cannot be met by the additional services at HPC centers.
Finding 2. Scientific applications with minimal communication and I/O are best suited for
clouds.
We have used a range of application benchmarks and micro-benchmarks to understand the performance
of scientific applications. Performance of tightly coupled applications running on virtualized clouds using
commodity networks can be significantly lower than on clusters optimized for these workloads. This can be
true at even mid-range computing scales. For example, we observed slowdowns of about 50x for PARATEC
on the Amazon EC2 instances compared to Magellan bare-metal (non-virtualized) at 64 cores and about 7x
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