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

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Magellan Final Report Table 9.6: Comparison of STAR performance and cost across different Amazon instance types and Magellan Instance type m1.large x2.4xlarge Magellan cores 2 8 8 EC2 units/core 2 3.25 - EC2 units/instance 4 26 - Cost ($)/instance hour $0.38 $2.28 - Cost ($)/hour/EC2 unit $0.10 $0.09 - Wall hours/job 2.119 1.242 1.269 CPU hours/job 1.500 1.240 1.263 Cost ($)/job 0.403 0.354 - cluster was 2 to 3 times slower than on bare metal. Due to the performance hit and the need to move data in and out of the virtual cluster, which did not have access to the NERSC Global File System, the user did not consider this a resource environment worth pursuing at the current time. 9.5.5 EnergyPlus Another type of application that was tested in the “Little Magellan” environment was a code called EnergyPlus (E+) that does building simulations, such as estimating the energy needs of a building before it is built. The user had been tasked with improving the performance, and used OpenMP with great results when comparing serial with 1, 2, 4 and 8 OpenMP threads. Code ran in 10 different configurations (each of them 1, 2, 4 and 8 threads) on both virtual cluster and bare metal. Results for 1, 2 and 4 threads were identical. The results for 8 threads were 1015% slower. 9.5.6 LIGO LIGO was another application that used the Magellan testbed through the “Little Magellan” setup. The application is described in greater detail in Section 11.2.3. LIGO users stated that they saw no difference between running through the serial queue on Magellan and on the virtual machines. The nature of the LIGO workload, i.e., minimal data and limited communication, makes it a suitable candidate to use virtualized cloud resources. There was negligible difference in CPU performance between bare metal and virtualized resources for this application. 9.6 Workload Analysis Our benchmarking results show that applications with minimal communication and I/O do well in cloud environments. In order to further understand the range of HPC applications that might benefit from cloud environments, we analyzed the workflows of researchers who were using the Magellan batch queue system at NERSC. Workload profile information was collected using the Integrated Performance Monitoring (IPM) analysis infrastructure. IPM is a low-overhead profiling infrastructure that provide a high-level report on the execution of a job. IPM reports hardware counters data, MPI function timings, and memory usage. It provides a lowoverhead means to generate scaling studies or performance data. IPM is available to users on NERSC systems using modules. On the Magellan systems, we force IPM profiling information to be collected for every job. The user gets normal output at the end of the job, and a log file is written to a shared space. To aid with the analysis, we built a generic infrastructure using MongoDB, also a cloud technology. MongoDB is a scalable, open source, document-oriented data store that supports dynamic schemas. An existing parsing script in IPM was modified to output a JSON-style document that was then used to populate MongoDB. 78
Magellan Final Report (a) Hosts (b) Tasks (c) IO Comm (d) CPU IO Figure 9.25: IPM Analysis. 79
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The Magellan Report on Cloud Comput
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Executive Summary The goal of Magel
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Key Findings The goal of the Magell
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Magellan Final Report Finding 8. DO
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Magellan Final Report role in addre
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Contents Executive Summary Key Find
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Magellan Final Report 9.7 Discussio
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Chapter 1 Overview Cloud computing
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Magellan Final Report • The Argon
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Chapter 2 Background The term “cl
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Magellan Final Report 2.1.4 Hardwar
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Magellan Final Report Table 3.1: Ke
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Magellan Final Report Little Magell
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Magellan Final Report 3.2 Advanced
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Chapter 4 Application Characteristi
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Magellan Final Report Table 4.1: Pe
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Magellan Final Report Output data
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Magellan Final Report of the pipeli
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Chapter 5 Magellan Testbed As part
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Magellan Final Report Figure 5.1: P
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Chapter 13 Conclusions Cloud comput
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Magellan Final Report Inherently, t
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Bibliography [1] G. Aldering, G. Ad
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Magellan Final Report [30] I. Foste
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Magellan Final Report [67] M. Palan
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Appendix A Publications Selected Pr
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Magellan Final Report Magellan Rese
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Magellan Final Report Selected Mage
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Appendix B Surveys B1
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• Nuclear Physics - Accelarator P
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Allow users to edit responses. What
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Amazon Eucalyptus OpenStack Other:
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Please list any publications/report
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Hadoop Streaming Hadoop Native Prog
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Magellan Final Report - Office of Science - U.S. Department of Energy

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