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J. J. Rehr & R.C. Albers Rev. Mod. Phys. 72 , 621 (2000)

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FEFF: Real-space Green’s function code for electronic structure, x-ray spectra, …. A “cluster to cloud” story: Naturally parallel Each CPU calculates a few points in the energy grid Loosely coupled Very little communication between processes. John Rehr.

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J. J. Rehr & R.C. Albers Rev. Mod. Phys. 72 , 621 (2000)
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  1. FEFF: Real-space Green’s function code for electronic structure, x-ray spectra, … A “cluster to cloud” story: Naturally parallel Each CPU calculates a few points in the energy grid Loosely coupled Very little communication between processes John Rehr J. J. Rehr & R.C. Albers Rev. Mod. Phys. 72, 621 (2000) http://leonardo.phys.washington.edu/feff/

  2. Challenge of NSF Grant • Is Cloud Computing feasible for on-demand, High-Performance Computing (HPC) for scientific research in the face of declining budgets? • Who is interested? • Is it for everybody? • What kind of code could benefit from it? • How do we make it possible? Disadvantages of Current HPC Approach • Expensive infrastructure: Big clusters = ~1000$/node + capital costs + power + cooling + … • Expensive HPC staff & maintenance • Need expertise in HPC to use efficiently

  3. Advantages of CC for Scientific Computing For “casual” HPC users: On-demand access without the need to purchase, maintain, or even understand HPCs Lease vs. buy: lease as many as needed at ~10¢/cpu-hr Plug & Play HPC scientific codes For developers: Scientific codes can be optimized and pre-installed For administrators & funding agencies: HPC access to a wider class of scientists at lower costs

  4. Development Strategy Develop AMI (Amazon Machine Image) customized for HPC scientific applications Test single-instance performance Develop shell-scripts that make the EC2 look and run like a local HPC cluster (”virtual supercomputer on a laptop”) Test parallel performance

  5. FEFFMPI EC2 AMI Custom Linux distribution replicated oneach instance in cluster • Standard Linux AMI: • Fedora 8 32-bit distribution with Gnu FORTRAN compilers (gfortran and g77) • AWS tools for the EC2: AMI, API and S3 tools • LAM 7.1.4 for parallel MPI codes • Java Runtime Environment 6 • Java Development Kit 1.6 • EC2 Cluster tools • FEFF8.4 serial and parallel versions • JFEFF graphical interface for FEFF8.4

  6. Serial Performance of FEFF on EC2 Virtual machine performance similar to “real”

  7. Serial Performance of Gasoline on EC2 Xeon E5345 (2.33 GHz) Virtual Xeon E5345 (2.33 GHz) Virtual ½ Opteron 2218HE(“1.3” GHz) Virtual Real Xeon E5345 (2.33 GHz) No penalty from virtualization

  8. ec2_clust_* Control Workstation MPI Master MPI Slave MPI Slave startd startd startd Current MPI Scenario User interacts with control workstation • Start cluster • Configure nodes EC2 Compute Instances

  9. UW EC2 Cluster Tools Tools in the local control machine Name Function Analog ec2_clust_launch NLaunches cluster with N instances bootec2_clust_connectConnect to a cluster sshec2_clust_putTransfer data to EC2 cluster scpec2_clust_getTransfer data from EC2 cluster scpec2_clust_list List running clusters ec2_clust_terminate Terminate a running cluster shutdown The tools hide a lot of the “ugliness”: ec2_clust_connect ssh -i /home/fer/.ec2_clust/.ec2_clust_info.7729.r-de70cdb7/key_pair _fdv.pem root@ec2-72-44-53-27.compute-1.amazonaws.com

  10. FEFFMPI on EC2 EC2 UW EC2 works well for highly parallelized applications like FEFF