September 14, 2010
New software that has been developed by researchers at the Johns Hopkins Bloomberg School of Public Health that vastly improves how quickly and cost-effectively scientists can analyze data from RNA sequencing projects. While there is clear value in extending RNA sequencing capabilities to examine genetic links to diseases and assist in detection and drug development, the process is generally lengthy and expensive due to the vast amount of data involved.
The software, termed “Myrna” was funded in part by Amazon Web Services (in addition to the Bloomberg School of Public Health and the National Institutes of Health) was, not surprisingly, making use of compute resources from Amazon. In order to test Myrna, researchers rented time and storage resources from AWS and were able to realize solid performance and cost savings. According to the study's authors, “Myrna calculated differential expression from 1.1 billion RNA sequences reads in less than two hours at a cost of about $66.”
As the lead researcher of study that was just published in the journal Genome Biology, Jeffrey T. Leek noted, “Biological data in many experiments—from brain images to genomic sequences—can now be generated so quickly that it often takes many computers working simultaneously to perform statistical analyses.” With this rush in data, Leeks says the cloud opens more possibilities and grants more open access to researchers as they can focus on their research versus the hassles of data center operation.
As Leek stated, “the cloud computing approach we developed for Myrna is one way that statisticians can quickly build different models to find the relevant patterns in sequencing data and connect them to different diseases.” He suggests that while Myrna has been designed to analyze next-generation sequencing reads, using cloud in conjunction with statistical modeling can carry over to other fields that generate large amounts of data.
Full story at Johns Hopkins University Gazette
Researchers from the Suddhananda Engineering and Research Centre in Bhubaneswar, India developed a job scheduling system, which they call Service Level Agreement (SLA) scheduling, that is meant to achieve acceptable methods of resource provisioning similar to that of potential in-house systems. They combined that with an on-demand resource provisioner to ensure utilization optimization of virtual machines.
Experimental scientific HPC applications are continually being moved to the cloud, as covered here in several capacities over the last couple of weeks. Included in that rundown, Co-founder and CEO of CloudSigma Robert Jenkins penned an article for HPC in the Cloud where he discussed the emergence of cloud technologies to supplement research capabilities of big scientific initiatives like CERN and ESA (the European Space Agency)...
When considering moving excess or experimental HPC applications to a cloud environment, there will always be obstacles. Were that not the case, the cost effectiveness of cloud-based HPC would rule the high performance landscape. Jonathan Stewart Ward and Adam Barker of the University of St. Andrews produced an intriguing report on the state of cloud computing, paying a significant amount of attention to the problems facing cloud computing.
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/02/2012 | AMD | Developers today are just beginning to explore the potential of heterogeneous computing, but the potential for this new paradigm is huge. This brief article reviews how the technology might impact a range of application development areas, including client experiences and cloud-based data management. As platforms like OpenCL continue to evolve, the benefits of heterogeneous computing will become even more accessible. Use this quick article to jump-start your own thinking on heterogeneous computing.