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

More documents

Recommendations

Info

Magellan Final Report Figure 11.4: The heatmap indicates the relative abundance (red = low, green = high) of functional roles (rows; functional roles were assessed with subsystems) present in each of the deep soil samples (columns; grassland = second to last column, bare fallow = last column) and 61 soil related metagenomic samples previously analyzed with MG-RAST. Samples were clustered with each other (column dendrogram); the functional roles within the samples were also clustered (row dendrogram). Preliminary analysis indicates that the two deep soil samples exhibit a high degree of correlation with each other that is not shared with the 61 other samples. Data were preprocessed to remove sampling based artifacts. 108
Magellan Final Report comparison tools. MG-RAST is unique in its high-throughput analysis strategies, which have improved its computational capacity by factor of 200 in the last two years. It uses a distributed load-balancing scheme to spread the computationally expensive sequence similarity comparison across a large number of distributed computation resources, including the Magellan systems. MG-RAST has been used to analyze 3 terabases of metagenomic sequence data in the last six months. The goal of the demonstration was to utilize the testbeds at both sites, and to explore potential failover techniques within the cloud. The demonstration used 150 nodes from ALCF’s Magellan to perform the primary computations over the course of a week, with NERSC’s Magellan acting as a failover cloud. Half a dozen machines on ALCF’s Magellan were intentionally failed. Upon detecting the failures, the software automatically started replacement machines on the NERSC Magellan, allowing the computation to continue with only a slight interruption. The same virtual machine image was used on both the ALCF and NERSC Magellan clouds. However, this was not a simple port and required some changes to the image. The instance uses all eight cores on each cloud node and about 40% of the available memory. The demonstration was a single run within the Deep Soil project and represents only ˜1/30th of the work to be performed. Use of the Magellan cloud allowed the project to dynamically utilize resources as appropriate, while the project continued to work on algorithmic improvements. This demonstration showed the feasibility of running a workflow across both the cloud sites, using one site as a failover resource. The project had many challenges similar to STAR discussed above in terms of image management and application design and development. 11.2.3 LIGO Figure 11.5: A screenshot of the Einstein@Home screensaver Showing the celestial sphere (sky) with the constellations. Superimposed on this are the gravity wave observatories (the three colored “angle images”) and the search marker (a bulls-eye). Recent binary radio pulsar search work units also have a gray disc, representing the radio receiver dish upon which the search is focused for that unit. (Image courtesy of Einstein@Home.) The Laser Interferometer Gravitational Wave Observatory (LIGO) collaboration is a dynamic group of more than 800 scientists worldwide focused on the search for gravitational waves from the most violent events in the universe. The collaboration developed the Einstein@Home program that uses donated idle time on private personal computers to search for gravitational waves from spinning neutron stars (also called pulsars) using data from the LIGO gravitational wave detector. Later, the LIGO researchers ported this 109
Page 1 and 2:
The Magellan Report on Cloud Comput
Page 3 and 4:
Executive Summary The goal of Magel
Page 5 and 6:
Key Findings The goal of the Magell
Page 7 and 8:
Magellan Final Report Finding 8. DO
Page 9 and 10:
Magellan Final Report role in addre
Page 11 and 12:
Contents Executive Summary Key Find
Page 13 and 14:
Magellan Final Report 9.7 Discussio
Page 15 and 16:
Chapter 1 Overview Cloud computing
Page 17 and 18:
Magellan Final Report • The Argon
Page 19 and 20:
Chapter 2 Background The term “cl
Page 21 and 22:
Magellan Final Report 2.1.4 Hardwar
Page 23 and 24:
Magellan Final Report Table 3.1: Ke
Page 25 and 26:
Magellan Final Report Little Magell
Page 27 and 28:
Magellan Final Report 3.2 Advanced
Page 29 and 30:
Chapter 4 Application Characteristi
Page 31 and 32:
Magellan Final Report Table 4.1: Pe
Page 33 and 34:
Magellan Final Report Output data
Page 35 and 36:
Magellan Final Report of the pipeli
Page 37 and 38:
Chapter 5 Magellan Testbed As part
Page 39 and 40:
Magellan Final Report Figure 5.1: P
Page 41 and 42:
Magellan Final Report Figure 5.2: P
Page 43 and 44:
Magellan Final Report NERSC deploye
Page 45 and 46:
Magellan Final Report Figure 6.1: A
Page 47 and 48:
Magellan Final Report greater than
Page 49 and 50:
Magellan Final Report specific QoS
Page 51 and 52:
Magellan Final Report configuration
Page 53 and 54:
Magellan Final Report 7.4 Summary U
Page 55 and 56:
Magellan Final Report Firewalls are
Page 57 and 58:
Magellan Final Report Aside from le
Page 59 and 60:
Magellan Final Report 9.1 Understan
Page 61 and 62:
Magellan Final Report grid) on 256
Page 63 and 64:
Magellan Final Report Table 9.1: HP
Page 65 and 66:
Magellan Final Report 25  Ping 
Page 67 and 68:
Magellan Final Report 100  12 
Page 69 and 70:
Magellan Final Report case of GTC,
Page 71 and 72: Magellan Final Report 1.4 IB TCPo
Page 73 and 74: Magellan Final Report only affects
Page 75 and 76: Magellan Final Report Figure 9.11:
Page 77 and 78: Magellan Final Report charted as a
Page 79 and 80: Magellan Final Report Evaluation Cr
Page 81 and 82: Magellan Final Report Write Perform
Page 83 and 84: Magellan Final Report 3500 3000 G
Page 85 and 86: Magellan Final Report Histogram Plo
Page 87 and 88: Magellan Final Report SATA devices.
Page 89 and 90: Magellan Final Report MB/s Virident
Page 91 and 92: Magellan Final Report and the perfo
Page 93 and 94: Magellan Final Report (a) Hosts (b)
Page 95 and 96: Magellan Final Report Routing IP pa
Page 97 and 98: Chapter 10 MapReduce Programming Mo
Page 99 and 100: Magellan Final Report 10.3 Hadoop E
Page 101 and 102: Magellan Final Report 35000  3500
Page 103 and 104: Magellan Final Report summarize som
Page 105 and 106: Magellan Final Report Processing ti
Page 107 and 108: Magellan Final Report in the networ
Page 109 and 110: Magellan Final Report Workload Patt
Page 111 and 112: Magellan Final Report This benchmar
Page 113 and 114: Magellan Final Report Task Tracker
Page 115 and 116: Magellan Final Report processing ti
Page 117 and 118: Magellan Final Report Using ESnet
Page 119 and 120: Magellan Final Report Figure 11.2:
Page 121: Magellan Final Report data collecte
Page 125 and 126: Magellan Final Report 11.2.5 Integr
Page 127 and 128: Magellan Final Report very large (4
Page 129 and 130: Magellan Final Report for optimizat
Page 131 and 132: Magellan Final Report One of the ad
Page 133 and 134: Magellan Final Report commercial cl
Page 135 and 136: Magellan Final Report Table 12.2: H
Page 137 and 138: Magellan Final Report Cost per TF t
Page 139 and 140: Magellan Final Report Productivity.
Page 141 and 142: Magellan Final Report compute insta
Page 143 and 144: Chapter 13 Conclusions Cloud comput
Page 145 and 146: Magellan Final Report Inherently, t
Page 147 and 148: Bibliography [1] G. Aldering, G. Ad
Page 149 and 150: Magellan Final Report [30] I. Foste
Page 151 and 152: Magellan Final Report [67] M. Palan
Page 153 and 154: Appendix A Publications Selected Pr
Page 155 and 156: Magellan Final Report Magellan Rese
Page 157 and 158: Magellan Final Report Selected Mage
Page 159 and 160: Appendix B Surveys B1
Page 161 and 162: • Nuclear Physics - Accelarator P
Page 163 and 164: Allow users to edit responses. What
Page 165 and 166: Amazon Eucalyptus OpenStack Other:
Page 167 and 168: Please list any publications/report
Page 169 and 170: Hadoop Streaming Hadoop Native Prog
show all

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

Create successful ePaper yourself

Delete template?

Save as template?