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CloudyCluster Team's blog
Determine the GCP Quotas for Your CloudyCluster Workload
The most common obstacle users encounter when trying to create a CloudyCluster environment is a conflict with their GCP Project Quotas.
Projects have resource limits in place, which are called Quotas. GCP Quotas are designed to prevent users of a project from accidentally creating too much of a certain resource. By default, the quotas for most resources are set reletively low, which is good for protecting against accidental charges to your billing account, but can impede CloudyCluster from creating a functional Torque/Slurm environment.
A Box in the Cloud for LAMMPS
Introduction One of the great features of LAMMPS is that it can run on single processors or in parallel using message-passing techniques and a spatial-decomposition of the simulation domain. As it’s named acronym implies, Large-scale Atomic/Molecular Massively Parallel Simulator is well suited for parallelization in the cloud, and this article will review ways to optimize those simulations. This can work equally well for Molecular Dynamics and Materials Sciences simulations.
Background/Problem Statement (Boundary 3 - x, y, z) If you were to take one of the example input files from the LAMMPS installation directory, you can run the simulations in a few seconds or minutes on a local or cloud machine.
Cloud HPC Hackathon @ SC19
Denver, CO – November 2019 marked another successful Super Computing conference, but for SGCI and the Omnibond CloudyCluster Team, it was even more special as 5 teams competed at the Cloud HPC Hackathon at SC19. Teams consisted of students from universities at all levels, ranging from Freshman to Ph.D. candidates working together under pressure of the clock and pop-up challenges over a 44 hour period.
Schools represented at this year’s event included locals from University of Denver and University of Colorado.
Cloudify Gateways Awardees
The Cloudify Gateways solicitation was developed to help developers to enable their Science Gatways in the Google Cloud.
The 4 awardees of the SGCI, Google, CloudyCluster Cloudify Gateways solicitation have exciting projects that we are looking forward to working with through this program.
Here are the projects: Making the Open OnDemand Platform Cloud Deployable Principle Investigators:
Alan Chalker, Ohio Supercomputer Center David Hudak, Ohio Supercomputer Center Robert Settlage, Virginia Tech Robert DeLeon, University at Buffalo For more information on Open OnDemand visit openondemand.
CloudyCluster v3 Released
CloudyCluster V3 Released
CloudyCluster version 3 was released and is available in the GCP Marketplace
We are excited for this release, we have spent a lot of tie with beta customers and extensive testing to get this new release to the marketplace. We are looking forward to extending the reach of investigation and research to the Cloud in a familiar campus / lab HPC environment.
The CloudyCluster team has been working hard, reducing latency, creating tighter integration with Google cloud, including the automatic use of placement policies for C2 instances.
How API-driven Public Clouds and HPC are Bringing a Brighter Future
(reprint)…
API-Driven Public Cloud The increasing popularity of cloud computing is evident in a myriad of news articles, blog posts and videos. Although cloud computing offers many benefits, one of the greatest benefits is largely hidden within the cloud.
When public clouds appeared, initial conversation focused on comparing the public cloud to data center virtualization. A critical aspect gradually emerged, the secret sauce: the public cloud is more than just virtualization.
Introducing CloudyCluster on Google Cloud Platform
Many customers have been asking how to seamlessly get HPC jobs to work between clouds and on premise. We are now making this a little more possible by extending CloudyCluster to the Google Cloud Platform. It is avaialble now by request and will be in the GCP Marketplace in a few weeks. Below is the press release: Enjoy…
DALLAS (PRWEB) NOVEMBER 12, 2018
Today, Omnibond announces the availability of CloudyCluster on Google Cloud Platform (GCP).
Leveraging GCS Storage for Efficient Access to Large Data Sets
Data sets for investigation and research can be critical and need to be kept around for at least the life of a research project, but possibly much longer based on data retention policies. Google Cloud Storages (GCS) is object storage that is resilient and easy to use and scale. GCS has such features as:
*>*9.99% availability for standard, and *>*99.95% for other storage classes Multiple storage classes (Standard, Nearline, Coldline, and Archive) which can be managed automatically based on access frequency or time.
Prime Time in Parallel: A Sample MPI Count Prime Numbers Job
One of the best ways to verify that CloudyCluster is ready to run your important research investigations, is to run it through one the Sample Jobs built into the standard image. Samples that we have included have small data sets or task expectations, which give you quick assurances that the environment is ready to handle larger, more demanding workloads.
The sample this article will walk through is using the Message Passing Interface or MPI and a very fast job to count the number of prime numbers between 0 and 25,000,000.
Running Science Gateways on CloudyCluster
CloudyCluster creates an HPC environment in the Public Cloud that is similar to on premise HPC systems. To provide elasticity in the cloud, we have developed a meta-scheduler. The CCQ meta-scheduler enables jobs to control elastic launching and deleting of instances as required. These can be diverse instance types including preemptible and standard as required by the individual jobs.
To support the SGCI Cloudify Gateways solicitation https://sciencegateways.org/cloudify2019 we will be adding new simplified HPC Job Submission APIs for CCQ to the next release of CloudyCluster targeted for the SC19 timeframe.