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

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Magellan Final Report 1000 Comparison of allocation delay for mgrast image on magellan Eucalyptus-1.6.2 Eucalyptus-2.0 OpenStack 800 600 Delay (seconds) 400 200 0 0 50 100 150 200 250 300 # of instances Figure 9.23: The allocation delays for the MGRAST image as the number of requested instances increased on the ALCF Magellan cloud. The delays are shown for the Eucalyptus (versions 1.6.2 and 2.0) and OpenStack provisioning. The MGRAST image was pre-cached across all nodes. 100  25000  90  Azure  EC2 Large  EC2 Xlarge  80  EC2‐taskFarmer  20000  Time (minutes)  70  60  50  40  Franklin‐taskFarmer  EC2‐Hadoop  Azure  Projected Cost (in dollars)  15000  10000  30  5000  20  10  0  MIN COST  MAX COST  0  16  32  64  128  Number of processors  Cost Range based on performance  (a) Performance comparison (b) Projected Cost for 12.5 million gene sequences Figure 9.24: Performance and cost analysis of running BLAST on a number of cloud platforms 9.5.2 BLAST The Joint Genome Institute’s Integrated Microbial Genomes (IMG) pipeline is a motivating example of a workload with growing needs for computing cycles due to the growth of sequence data expected from next-generation sequencers. One of the most frequently used algorithms in IMG, the Basic Local Alignment Search Tool (BLAST), finds regions of local similarity between sequences. We benchmarked the BLAST algorithm on HPC systems (Franklin at NERSC) and cloud platforms including Amazon EC2, Yahoo! M45 and Windows Azure. We managed the execution of loosely coupled BLAST runs using a custom developed task farmer and Hadoop. For this evaluation, we ran 2500 sequences against a reference database of about 3 GB, and compared the performance data. Figure 9.24 shows the performance of the task farmer on Franklin and Amazon EC2 76
Magellan Final Report and the performance of Hadoop on EC2 and the BLAST service on Windows Azure. For this workload, the performance of cloud platforms compares favorably with traditional HPC platforms. On Amazon EC2, the database and the input data sets were located on elastic block store (a high-speed, efficient, and reliable data store provided by Amazon for use with one node) that is served out to the other worker nodes through NFS. We were provided friendly access to the Windows-based BLAST service deployed on Microsoft’s cloud solution, Windows Azure. The experiments were run on Azure’s large instance, which has four cores, a speed of 1.6 GHz, and 7 GB of memory. This instance type is between the large and xLarge instances we used on Amazon EC2. On Azure, the total time for data loading into the virtual machines from the storage blob and back to the storage blob is also included in the timing. Additionally the data sets were partitioned equally across the nodes, and we see the effects of bunching. Thus the model here is slightly different from our setup on Hadoop but gives us an idea of the relative performance. We ran the same experiment on Yahoo! M45, a research Hadoop cluster. The performance is comparable to other platforms when running a small number of sequences. The load on the system affects execution time, inducing large amount of variability, but performance seems to be reasonable on the whole. However, when running 250 concurrent maps (10 genes per map, 2500 genes total), there is a heavy drop in performance— the overall search completes in 3 hours and 39 minutes. This seems to be the result of thrashing, since two map tasks, each requiring about 3.5 GB, are run per node with 6 GB memory. We extrapolated from the running times the cost of commercial platforms to run a single experiment of 12.5 million sequences (run every 3 to 4 months). The cost of a single experiment varies from about $14K to $22K based on the type of machine and the performance achieved on these platforms (Figure 9.24). There is a reasonable amount of effort required to learn these environments and to get them set up for a particular application. Early results of this work were presented at the System Biology Cloud Workshop at SC09. A manuscript is in preparation with additional experiments that explore different data storage and communication options. 9.5.3 STAR The STAR experiment data analysis was run across different Magellan systems. More details of the application are presented in our case studies in Chapter 11. The STAR team has also experimented with other cloud systems including Amazon. Table 9.6 shows the performance and cost across different Amazon instances. The team ran on two different types of Amazon instances: (a) Standard Large Instance (m1.large) with 7.5 GB of memory and 850 GB of local instance storage, and (b) High-Memory Quadruple Extra Large Instance (x2.4xlarge) with 68.4 GB of memory and 1690 GB of local instance storage. The m1.large instance is one of the standard instance types, whereas x2.4xlarge is a high-memory instance. Table 9.6 demonstrates the importance of selecting the right instance type for a particular application. Both performance and cost are better for the high-memory instance for the STAR application. The difference in performance between the Amazon high memory instances and Magellan is due to the differences in underlying hardware and memory allocated to the VMs. 9.5.4 VASP VASP (Vienna Ab-initio Simulation Package) is a package for performing ab initio quantum-mechanical molecular dynamics (MD) using pseudopotentials and a plane wave basis set. This package is available on all major NERSC systems and has a broad user base (close to 200 licensed users at this point). In order to facilitate a comparison between VASP run on bare metal and on a virtual cluster, the user made use of “Little Magellan”—a virtual cluster booted on top of Eucalyptus (details in Chapter 3). The user ran first an 8-core VASP job (one batch node), and timing was comparable with similar runs on the batch queue system. After that, a set of identical 16-core runs was set up on both the batch queue system and “Little Magellan.” The output was checked for accuracy, and the results were exactly the same (as expected) to the last digit of reported precision. However, with two nodes only, the timing on the virtual 77
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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 compute insta
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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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