High Performance Computing

Deploy Your Research HPC Applications
in AWS Cloud with Clovertex

Kshitij Kumar, CEO, at the Nanoport tour at Thermo Fisher Scientific in Eindhoven

Clovertex specializes in scientific research computing platforms. We have solutions to process raw data on Amazon Web Services from Illumina sequencers and Thermofisher Scientific CRYO-Electron Microscopes.

In addition to building pipelines, we have AWS Parallel Cluster (Cfn Cluster) and AWS Batch architecture based platforms for various genomics and research applications.

These applications on AWS provide scalability, zero wait time and zero resource sharing for research scientists to be more productive and bring the research time from days to hours and weeks to days.

Clovertex Offering

Clovertex Advantage

Partnered with leading cloud providers, Clovertex brings years of cloud and HPC expertise to enable researchers to get productive at once. We provide a full solution tailored to specific research needs, so that the HPC workload can be seamlessly moved to the cloud.

Clovertex partners with IT stakeholders within your organizations, to deliver solutions up to 10X faster, with a keen eye on ease-of deployment and use. Clovertex has experience in developing solutions based in sound design principles, that take into account enterprise grade reliability, security and compliance requirements. Clovertex has a proven track record of helping organizations exploit all avenues pertaining to operational excellence and cost optimization.

Importance of HPC

High Performance Computing is often referred to as an environment, with an ability to process large volumes of data, or perform complex computational tasks at high speeds. This ability is foundational for various research organizations, in their efforts to ask tough questions, and deliver successful results. The resulting scientific breakthroughs are now prevalent in every industry vertical.

To build such a high-performance architecture, organizations deploy a cluster of networked servers. It is not uncommon for HPC clusters to consist of hundreds or thousands of servers. Each server is referred to as a compute node. Software tools and algorithms run in parallel on these compute nodes, simultaneously accessing a shared data storage environment for input, processing and output activities. The maximum performance of such an environment, is dependent on the optimum configuration of, research tools, system software, and infrastructure components.

As researchers look to new tools and computational methods to solving problems, these traditional HPC environments are proving to be restrictive.

A new approach is needed.

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