See the HPC in Cloud Educational Series videos

As the interest in HPC in the Cloud grows, Cycle Computing, in conjunction with Amazon Web Services, Avere, Google, and Microsoft Azure, presented the HPC in the Cloud Educational Series at SC15 this year. This series delivered a set of in-depth discussions on key topics around leveraging Public Clouds for large computation and big data analytics.   The series was well received and we have made the presentations available for viewing. If you saw the series at SC, you may want another chance to review the material. If you missed it, this is a great chance to hear from the experts. Please take a look and let us know what you think.   The series is available here and includes the following:   Why wouldn’t you use public cloud for HPC – Rob Futrick, Cycle Computing — As part of the HPC in the Cloud Educational Series, Rob Futrick, CTO of Cycle  Computing discusses the benefits and challenges of moving big data and HPC workloads to the public cloud.   HPC Cloud Data Mgmt – Jeff Layton, Amazon Web Services — As part of the HPC in the Cloud Educational Series, Jeff Layout, HPC Principal Architect at Amazon Web Services, explains concepts and options around using storage in the AWS Cloud.   Microsoft Azure for Engineering Analysis and Simulation – Tejas Karmarkar, Microsoft Azure — As part of the HPC in the Cloud Educational Series, Tejas Karmarkar, Senior Program Manager, Microsoft Azure, presents techniques for doing engineering analysis and simulation within the Microsoft Azure cloud.   Broad Institute use of Pre-Emptible VMs – Marcos Novaes, Google Cloud Platform – As part of the HPC in...

Months-long cancer gene analysis in an evening: using CycleCloud on Google Compute Engine Preemptible VMs

Cycle’s mission is to enable our customers to easily access the Big Compute resources required to solve problems and meet deadlines. Over the years our software has orchestrated workloads both internally and externally while accelerating the move to cloud. This is why we were ready when the Broad Institute came to us with a problem: Their cancer researchers saw value in a highly-complex genome analysis, but even though they already had powerful processing systems in-house, running the analysis would take months or more. We thought this would be a perfect opportunity to utilize Google Compute Engine’s Preemptible VMs to further their cancer research, which was a natural part of our mission. And now that Preemptible VMs are generally available, we’re excited to tell you about this work.   The Science: The search for understanding cancer The Broad Institute’s Cancer Program has data sets that include hundreds of cancer cell lines, information on the genetic mutations present in each cell line, gene expression data showing which genes are more or less active under various conditions, as well as  information about how various small molecules interact with the cell lines at both large and small scales. Each of these data sets is massively complex in its own right.  Combining them to explore the interactions between these layers of knowledge quickly creates a vast landscape of interrelated data to explore.   One of the Cancer Program’s goals is to intelligently direct future research using these datasets. This particular workload used machine learning techniques to infer  relationships among and between these cell line and gene/expression data sets. This map provides scientists that are...